- Email: [email protected]
Flying the North American Adirondack whitetail on instruments
Journal for
J. Nat. Conserv. 10, 280–294 (2003) © Urban & Fischer Verlag
Nature Conservation
http://www.urbanfischer.de/journals/jnc
Flying the North American Adirondack whitetail on instruments A multi-parameter modeling approach to ecosystem-based wildlife management Richard W. Sage Jr.†1, Bernard C. Patten2,* & Paulette A. Salmon1 1 2
Adirondack Ecological Center, SUNY College of Environmental Science and Forestry, Newcomb, New York 12852, USA Institute of Ecology, University of Georgia, Athens, Georgia 30602, USA; e-mail: [email protected]
Abstract We compare ecosystem-based wildlife management to instrument flight of aircraft. Airplanes cannot be controlled without visual ground reference, or if this is impossible to a cluster of flight instruments. Instrument pilots are trained to develop a rhythmic scan of the cluster to monitor and correct flight path and attitude. The untrained tendency is to fixate on a single gauge. Then, the aircraft deviates from its desired attitude and trajectory, and control may be lost. Fixation is like single-factor management wherein variables like habitat quality, recruitment, predator control, or harvest rates are singled out for adjustment without considering the others. Ungulate populations are no less complex than aircraft in flight. They are multifactorial and move through time and space. To be managed effectively they must be guided in these movements through the monitoring and control instruments nature has provided. These are not necessarily proximate because populations are embedded in ecosystems and cannot be isolated from systemic complexity. Holistic management is needed, which requires a suite of monitoring and control parameters analogous to those in instrument flight. We hold that every institution serving the interests of wise resource use should employ comprehensive, large-scale, ecosystem-based models built, tested, and perfected as a committed institutional activity over long periods of time. We call this Institutionalized Model-Making (IMM), and see models drawn from the collective expertise of scientists and their data in the same relation to nature as flight simulators are to actual aircraft. They mimic responses to control actions, and enable training in multifactorial management analogous to the instrument scans and control actions of the instrument pilot. A model comprehensive enough for institutional use has not been built. We call attention to our efforts to develop such a model at the Huntington Wildlife Forest in New York’s Adirondack Mountains. This is a model of the North American whitetail deer (Odocoileus virginianus Miller). It is too complex and incomplete for use in this paper, so a smaller-scale model is employed to make the case. In simulation trials parameters are ranked as to control sensitivity, then manipulated singly and multiply to demonstrate the superiority of the multiparameter approach. Key words: Adirondack Park, ecosystem management, Institutionalized Model-Making, instrument flight, STELLA simulation, white-tailed deer.
1. Introduction Managing exploited populations on a sustainable basis is an ideal not fully realized in wildlife and fisheries management. The paradigm of maximum sustained yield (MSY), for example, which has motivated many approaches during the last half-century, has proved largely ineffective. Living resources are overexploited
to the edge of local extinction, or mismanaged to the point of overpopulation. The North American whitetailed deer of the present study, for example, is currently widely overpopulated in much of Eastern USA. A new approach is needed that restructures management along more holistic lines, and the purpose of this
*corresponding author
1617-1381/03/10/04-280 $ 15.00/0
Flying the North American Adirondack whitetail
paper is to outline such an approach based on modeling. We will exemplify our ideas by focusing on a local population of the white-tailed deer, seen not as an isolated population in the classic wildlife management sense, but as a process and product of a whole ecosystem in which it is deeply embedded as an integral component. 1.1 Our region The ecosystem in question is the Adirondack Park of New York State. This is a 2.4-million hectare area in the northern corner, fully one-sixth of the area of the entire state (28,600 km2). The park was established as a mix of public and private lands in 1882 in response to wide scale exploitation by forest practices of the time, the public lands to be restored and preserved “forever wild” for the people of New York. Geologically, the park is part of the Canadian Shield, a region of very old Grenville rocks recently uplifted to form the Adirondack Dome. The Park contains 42 peaks > 1200 m, the highest of which is Mt. Marcy (1629 m). Glaciation has produced more than 1,000 glacial lakes, and continuing orogeny adds 1.5 mm annually to elevation. The landscape is a mosaic of wilderness, towns, working forests, and natural areas. Known for its outdoor recreation, the Adirondack Mountains offer some 3200 km of hiking trails, 40 state-operated and many more private campgrounds and lakeside resorts, and hundreds of kilometers of established canoe routes. 1.2 Our outdoor laboratory The Huntington Wildlife Forest in the central Adirondacks is a 6071 ha tract devoted to research in silviculture and wildlife management. Elevations range 475–816 m of mountainous topography (slopes 0–40%). There are five lakes and numerous beaver ponds (628 ha), 38 km of streams, and 32.2 km of unpaved roads. Forests (5443 ha) include old growth (> 150 y; 1089 ha), managed even- and uneven-aged stands (2994 ha), and fire-succession communities less than 100 years old (1360 ha). Forest types are 50% northern hardwoods (beech, birch, maple; 2722 ha), 15% coniferous (spruce, fir, pine, hemlock; 816 ha), and 35% mixed hardwood and coniferous (1905 ha). 1.3 Our animal The North American white-tailed deer (Odocoileus virginianus Miller) has an estimated New York State population of 1-million and an Adirondack Park population of 40–50,000 animals. The Huntington Forest population ranges 100–500. Age at first reproduction is
281
1.5–2.5 years, and litter size is 1–2. Sex ratios (M:F) vary 1:3–1:12. Adult males (called “bucks”) weigh 56–114 kg (live weight) and adult females (called “does”) 43–79 kg. Males live 10 y and females up to 17 y. Survival is generally low in young-of-year animals (fawns), and 2nd-year (yearling) age classes experience chronic winter losses. Occasionally, adult (breeding) and sub-adult (non-breeding) animals suffer severe winter mortality. Hunting is generally permitted for males only. Since 1943 the Adirondack harvest of males has ranged 5–13,000, depending on population density. 1.4 Our laboratory: Institutionalized Model-Making The Adirondack Ecological Center is a field station of SUNY’s College of Environmental Science and Forestry located on the Huntington Forest. At this station we are exploring “Institutionalized Model-Making” (IMM) as an approach to ecosystem-based, multiparameter resource management. Some envisioned characteristics of the IMM approach are as follows: 1. IMM emphasizes process over product. Ecological modeling is usually seen as a technical procedure with the goal of producing a predictive computer model as a final product. Modelers charged with the responsibility of synthesizing one or more disparate data sets into a coherent representation of some system of interest normally do the modeling. We take a somewhat different approach. Recognizing that the true information for a model resides not only in data, but also in the minds of subject-matter experts who produce the data, we seek to engage primary scientists in the modeling process, particularly in the all-important first phase of model conceptualization. 2. IMM facilitates and structures people interactions. The formats inherent in model products can be used a priori to structure conceptual modeling. Workshops and other situational activities can be employed to draw forth the requisite information. In the best of worlds we see ongoing conceptual modeling by multidisciplinary groups of scientists (and others, like stakeholders) as a permanent fixture in the organization of research laboratories and other institutions with regional research and management responsibilities. Institutionalized modeling activity on a continuing basis can forge coherent shared perspectives from initially divergent viewpoints and opinions. This potential of modeling which places process over product is as yet largely untapped in resource management fields. 3. IMM captures and organizes the knowledge stateof-the-art. Whether one or many scientists share their knowledge
282
R. W. Sage †, B. C. Patten & P. A. Salmon
and data, the developing model becomes an organized repository for current information, great or small. As such, it: • Identifies knowns and unknowns, • Guides research directions and priorities, • Facilitates scientific interactions, • Formats databases, • Informs management decision-making, • Enables coherent communication with constituencies, • Holds the place for continuing further development, • Enables ecosystem-based holistic management. As a permanent and evolving part of institutional organization, IMM has enormous potential for facilitating cross-disciplinary dialogue, structuring databases, and recording the state-of-the-art pertaining to institutional areas of responsibility. As models are never really complete and can always be improved, model evolution itself can become a permanent feature in the life and dynamics of institutions as personnel, goals, and priorities shift and change with changing times and responsibilities. We see such models not as quick products for predictive problem solving, but as robust, comprehensive, and above all operational, compendia of available knowledge and data in process of ongoing development and improvement. As such, they can be used to explore and manage the complexities of manipulated resource dynamics within existing frames of their encompassing ecosystems. The foreseen activities, we suggest, are not unlike flying a modern aircraft.
2. “Flying” the Adirondack whitetail To maintain a stable, directional flight trajectory from a point of origin to a destination, an aircraft must be able to navigate and monitor its flight configuration, and exert controls to maintain an acceptable perfor-
mance window. The essence of control is negative feedback. 2.1 Cybernetic Control The elements of cybernetic control by negative feedback error regulation are illustrated in Figure 1. Common man-made control systems include aircraft autopilots, automobile cruise controls, and thermostats. Objective functions such as, respectively, attitude and heading, velocity, and room temperature, are specified and performance is monitored. Deviations beyond specified ranges are registered as error and fed back to the controller, which acts on the system controlled to correct the error. Such deviation damping or error reduction constitutes “negative feedback”, the basis of cybernetic control. In aircraft flight the three functions of navigation, monitoring, and control must be performed continuously from the moment of departure to the moment of touchdown. Deviation from desired trajectories and flight attitudes is constantly and instantaneously corrected. Instrument flight (“IFR” – meaning under Instrument Flight Rules), makes uninterrupted continuation of all functions possible should the visible horizon on which visual flight (“VFR” – under Visual Flight Rules) depends be lost, such as when flying in clouds. IFR flight requires special training beyond that for VFR. 2.2 The instrument flight cockpit In an IFR cockpit the basic instruments for monitoring are an altimeter, magnetic compass, artificial horizon/attitude indicator, vertical speed indicator, turn-and-bank coordinator, and airspeed indicator. The latter is marked in three color-coded zones – normal (green), caution (yellow), and a never-exceed speed (or “red-line”) beyond which structural damage to the airframe may occur. Control in three-dimensions is provided by
Figure 1. Generalized cybernetic control by negative feedback.
Flying the North American Adirondack whitetail
ailerons (roll), horizontal stabilizer (pitch), and rudder (yaw). In addition there are flaps to increase lift, and engine power to climb, descend, or maintain straight-andlevel flight at desired airspeeds. The navigation instruments are irrelevant to the present discussion. 2.3 The IFR instrument scan The instrument cockpit is complex. VFR pilots and inexperienced IFR trainees tend to focus on and stare at a single gauge. This is called fixation. The gauge in question may be showing the plane in straight-andlevel flight, or at a safe airspeed, even as the remaining instruments are unwinding and showing that the aircraft is out of control. Fixation can be likened to single-parameter resource management, for example in connection with the white-tailed deer, adjusting harvest rates, culling predators, or undertaking habitat improvement (but not, concertedly, all three). To maintain control when reference to the visual horizon is lost, a continuous scan and actuation of all the monitoring and control instruments is necessary. Instrument flight training is devoted to learning how to make such scanning and correcting unconscious and automatic.
283
The same principle, we suggest, is applicable to the management of exploited resource species. 2.4 An instrument panel from STELLA Figure 2 shows an analog in the simulation software STELLA (High Performance Systems, Hanover, New Hampshire, USA) of the flight instrument cockpit. Graphic outputs (left column) and numerical outputs (middle and right columns, center) enable selected parameters of a model to be monitored. A warning device (middle column, top) flashes green when the parameter it represents is operating within a prescribed zone, yellow to indicate caution when outside this zone, and red to signify great enough deviation from a desired trajectory to be considered alarming. Control devices include dials and toggle switches (bottom center, the two smaller devices), and sliders (right column, group of three) to change the values of constant parameters during simulation, and a graphic input device (upper right) to allow time-varying parameters to be configured, also during simulation. Any number of these devices may be added to accommodate desired monitoring and control variables of a simulation model as needed.
Figure 2. An “instrument panel” from STELLA. The devices are described in the text.
284
R. W. Sage †, B. C. Patten & P. A. Salmon
2.5 The “renewable resources instrument scan” Populations exploited as living resources are no less complex than an IFR aircraft. In many ways, especially in identity and numbers of potential parameters for monitoring and control, they are more complex. They differ also in having been designed by nature, not by man for human use. Being deeply embedded in ecosystems as processes and products of entire natural organizations, relevant parameters may not be directly associated with the focal resource, per se. They may instead have origins in remote events or processes of diffuse or multiple character propagated through layers of complexity to emerge eventually as ripple effects manifested in the subject resource. Thus, the IMM approach must eventually create models not only of the resource populations of interest, but also of their encompassing ecosystems. This must be done in enough detail to incorporate a rich set of direct and indirect monitoring and control parameters, both proximate to the populations and also distal in the ecosystem. And, for effective application of the principles of instrument flight, a systematic “renewable resources instrument scan” must be developed to avoid the nemesis of instrument flight – “fixation.” Complex living resources cannot be managed by single-factor control. Sustainability requires a continuous scan and adjustment of multiple monitoring and control parameters. The white-tailed deer, as a case in point, must be “flown on instruments.” 2.6 Interplay of modeling, monitoring and control: indicator variables Not all the parameters directly or indirectly influencing a complex, embedded, resource system have equal
weight. Those more proximate, or learned to be of greater significance in the distal causal network, may be selected as a plausible monitoring subset. These are indicator variables. In the IMM context they should have, at a minimum, the following characteristics: 1. They should be relatively easy to observe and measure empirically; time-series data are always difficult and expensive to acquire in monitoring programs. 2. They should be variables to which model dynamics are sensitive; this can be determined in simulation trials by sensitivity analysis. 3. The model dynamics they reflect should be accessible through one or more control parameters; otherwise management interventions cannot be identified. 4. The control parameters within models should have manipulable empirical counterparts; otherwise management actions cannot be implemented. Figure 3 illustrates the model-management loop, showing how empirical indicator variables feed back through monitoring and control parameters of models to empirical management parameters.
3 Sixty-year case study: 1941–2001 An ecosystem-based model of the white-tailed deer, under development at the Huntington Forest over a period of years, is too complex to use as a case study in this paper. This model meets the criteria we see for the IMM conception in being a comprehensive, operational, and open-ended repository of a laboratory’s knowledge, concepts, and long-term data under continual development through scientist-modeler interac-
Figure 3. The model-management loop relating empirical indicator variables to management parameters through the monitoring and control parameters of models.
Flying the North American Adirondack whitetail
tions. A simpler population model, prepared by Sage in dBASE (unpublished), and converted to STELLA by Salmon (unpublished), will serve here to illustrate “flying the whitetail.” 3.1 dBASE/STELLA population model This model computes quarterly population changes as a balance of positive and negative factors prevailing during each season of the year (Figure 4). The database is a 60-year record compiled at the Huntington Forest (1941–2001). Factors included in the model are winter severity, population density, fawn survival, predation, illegal hunting and crippling, area inhabited, habitat quality, reproduction, recruitment, hunting, and roadkill. The three factors underscored will be manipulated as control parameters in simulation trials presented further below. 3.2 Winter severity Adirondack winters can be quite severe. For the whitetailed deer winter is defined by snow depth, specifically the number of days when depths equal or exceed 38.1 cm (15 in). Snow exacts a heavy metabolic toll for activity during such periods of prolonged near-starvation. Adaptation calls for minimizing physical movements. When snow reaches the ± 38.1 cm threshold, the animals leave their normal “summer” range and move to winter “yarding” areas. These are typically under coniferous vegetation, which reduces snow on the ground, moderates temperatures, and provides some low quality browse. In 60 years of daily snow depth data taken at the Huntington Forest, the longest winter (in deer terms, the severest) was 1943 when snow cover was ≥38.1 cm for 138 days. The shortest (mildest) winter on record was 1983 when snow never
285
exceeded the critical depth. An empirical winter severity index (WSI) is computed as the ratio of days with ≥38.1 cm to a hypothetical maximum of 140 days, just above the 1943 record. The lighter trace in Figure 5.1 shows the 60-year (240-season) WSI record for the Huntington Forest as generated in a STELLA simulation run. The time line is in seasons because the model computes the deer population as discrete values for each quarter-year over the 60-year period. All the simulations in Figure 5 (and later, Figures 6 and 7) appear continuous because the discrete seasonal values run together. 3.3 Strength of control parameters As indicated in Section 3.1, factors to be manipulated as control parameters in STELLA simulation trials include hunting, predation, and habitat quality. Five adjustable parameters with varying strengths to influence simulated dynamics represent these: harvest of adult males, yearling males, and females; predation; and habitat quality. The panels of Figures 5a–f show simulations establishing the general sensitivity of population change to each of these five controllers. Results, described below, condition a baseline simulation (Figure 6) against which trajectories generated by singlefactor (Figure 7) and multi-factorial (Figure 8) management trials will subsequently be compared. Figure 5a: Best case conditions. The lighter curve in the figure shows the 60-year WSI record for reference. The darker trajectory shows simulated deer densities, beginning at a realistic, moderate population density of 10 deer/2.5 km2 (= mi2). This is the bestcase simulation, corresponding to no hunting, no predation, and maximum habitat quality. Over the period and under these conditions, densities increase to unrealistic levels of overpopulation in approximately exponential fashion, visibly modulated by winter severity. Figure 5b: Poorest habitat. When habitat quality is set to its minimum value, and the other parameters still maintained at no hunting and predation, deer densities (darker trace) decrease to low but realistic levels compared to those of Figure 5a (lighter curve – this will be included in subsequent panels for reference). This establishes habitat quality as a sensitive parameter in this model.
Figure 4. Sixty-year mean biomass (kg ha–1) and animal numbers (ha–1) during six periods and the four seasons of each year from 1941–2001, as recorded on the Huntington Forest.
Figure 5c: Maximum hunting, adult males. With best habitat quality restored (here and in the Figure 5d–f simulations to follow), maximum harvesting of adult males significantly reduces the population. Seasonal amplitudes (absolute, though probably not relative) are somewhat dampened compared to the best-case sce-
286
R. W. Sage †, B. C. Patten & P. A. Salmon
nario. These results suggest harvesting adult males is a moderately sensitive control parameter in the model.
compared. The following baseline trajectory will serve this purpose.
Figure 5d: Maximum hunting, yearling males. Yearlings are much smaller in proportion to the total population than adults, and their reproductive contribution is proportionately less. As a result, yearling harvest has only small impact on the population’s growth capacity. This variable does have a unique property, among those included in the model, of increasing the amplitudes of seasonal fluctuations in comparison with the best and other cases. This appears to reflect reproductive and recruitment biology. Permanent dispersal away from their natal home range is coincident with the late-autumn rut in which yearlings in the Adirondacks hardly participate. Steep fourth-quarter drops in density observed in Figure 5d appear to reflect fall dispersal in this segment of the population. Reciprocally, high firstand second-quarter recruitment from surviving fawns of the previous year, observed in the figure, effectively replenishes the yearlings in spring and summer. In general, in the model, yearling-male harvest can be considered an insensitive control parameter.
Figure 6a: To generate this baseline control parameters were set to values near the middle of their ranges, except female hunting which was set at a low nonzero value. As shown, over the sixty-year interval the population tracks reasonably closely above and below the initial level.
Figure 5e: Maximum hunting, females. Female deer are critical to population continuance. In the severe climate of the Adirondacks harvest of this segment of the population just before the rut in November–December with winter rigors approaching can be devastating. The simulation shows this. Although female hunting is the model’s most sensitive control parameter, we do not intend to generalize this to milder climates or other situations where the practice may well be a useful element in the tool kit of multi-parameter management. Figure 5f: Maximum predation. Predator control is difficult to implement in practical management. When deer predation is maximized the population is suppressed slightly lower than when adult males are subjected to maximum hunting (Figure 5c). Predation rate can therefore be considered a moderately sensitive, if hard to manage, control parameter. In summary, from comparison of the Figure 5b–f simulations with the best case (Figure 5a), ranking of control parameter strengths investigated in accordance with the sensitivity of population change to their manipulation is, in descending order: female harvest > habitat quality > predation > adult male harvest > yearling male harvest. In Section 3.5, exploring single-factor parameter manipulations, these will be taken up in reverse order, that is, of increasing regulatory strength. 3.4 Baseline simulation Manipulations of control parameters require a reference simulation against which simulated results can be
Figure 6b: Here, for better visualization, the baseline simulation of Fig. 6a is rescaled to a restricted range of values encompassing the reference behavior. This trajectory will appear for comparison as a lighter trace in all the panels of Figures 7 and 8. The management goal will be to remain within the objective zone of 10–20 animals/2.5 km2, shown as the shaded area above the “red line.” The baseline trajectory shows two runs of under-population, one quite extended, and three of overpopulation. 3.5 Management simulations: single-factor Figure 7 shows simulated results of attempts to manage the Huntington Forest deer population model within the specified limits. The “red-line” can be taken as a minimum acceptable population level, in analogy with the never-exceed maximum airspeed of an aircraft in flight. Overpopulation (> 20 deer/2.5 km2) will also be considered undesirable, but at moderate levels less critical. The management scenarios for Figure 7 are singlefactor trials in order of increasing control capacity as previously determined (Section 3.3). These simulations are analogous to single-instrument fixation in IFR flight. In Figure 8 further below, a number of multiple-factor trials are presented that correspond to the coordinated instrument scan and continuous attitude correction required for safe instrument flight. Figure 7a: Adjusting yearling male harvest. This weakest control parameter has little capacity by itself to exert significant change in baseline dynamics. It is possible, by maximizing yearling hunting when population is high and minimizing it when low, to achieve some moderation of extremes and remain statistically within the goal envelope more than does the baseline case. Figure 7b: Adjusting adult male harvest. This second-weakest control factor has somewhat more capacity than the previous to keep the population within desired limits. Again, maximizing hunting during high population periods and minimizing it during low periods moderates extremes compared to the baseline, most strongly during approximately the seasonal interval 150–205. This simulation suggests adjusting adult-
Flying the North American Adirondack whitetail
287
Figure 5. Sixty-year simulations (1941–2001) of the Huntington Forest deer population under various single-factor conditions referenced to the best-case simulation. (a) Best-case simulation corresponding to no hunting and no predation under the best available habitat quality conditions. The monitoring and control instrument cluster (see Figure 2) used to “fly the whitetail” and generate the simulations is shown at the top. Due to space limitations these have been deleted from all other panels in Figures 5–8. The three warning devices on the left indicate snow depth, winter severity index, and deer density. The dial controls simulation area but was inactivated. The five sliders clustered to the right of center represent control parameters and are marked with ovals to visually enhance their relative settings (high, low, intermediate). Large arrows like the one shown were used to call attention to the slider studied in each simulation (in this case habitat quality). The lighter trace is the 60-year record of the winter severity index. Short-term effects of severe and mild winters on the generally increasing long-term trend are evident. (b) Same as (a) but now with the worst available habitat quality. The lighter trace in this and subsequent panels is the best-case simulation of (a) for comparison. (c) Same as (a) but now with maximum harvest of adult males. (d) Same as (a) but now with maximum harvest of yearling males. (e) Same as (a) but now with maximum harvest of females. (f) Same as (a) but now with maximum predation.
288
R. W. Sage †, B. C. Patten & P. A. Salmon
Figure 6. Sixty year (1941–2001) baseline simulation generated by intermediate values of habitat quality, predation, and harvest to serve as the reference trajectory for subsequent simulation trials of Figures 7 and 8. Values near the middle of their ranges were selected, except for female hunting which was set at a low nonzero value. (a) Ordinate scaled as in Figure 5 panels for comparison with those simulations. (b) Ordinate rescaled to active range of simulation, and management goal superimposed, for comparisons with simulations of Figures 7 and 8.
male-only hunting as a sole means of management would be ineffective. Figure 7c: Adjusting predation. This somewhat stronger parameter than the previous has a little more capacity to maintain the deer population within the stated bounds. A difference compared to male hunting
(the two previous factors) is a tendency for reduced predation to encourage overshoot. Therefore, though predator control or enhancement is possible as a longterm management strategy, this simulation suggests it would not only be a weak tool by itself, but could also encourage undesirable prey-predator oscillations. Introduction of gray wolves (Canis lupus) into the
Flying the North American Adirondack whitetail
Adirondack Park has recently been considered, but rejected for several reasons (Paquet et al. 1999). Although the Adirondacks provide ample habitat, they lack sufficient landscape connectivity with surrounding regions to enable normal wolf ranging. Also, wolf introduction would impact not only the whitetail, but also its currently dominant non-human predator, the coyote (Canis latrans), and human harvest patterns as well. Moreover, genetic studies indicate the original endemic Adirondack species to be the Eastern Canadian red wolf (Canis rufus), and contemporary C. latrans to be hybrids with this species. Figure 7d: Adjusting habitat quality. This second strongest control parameter (Figure 5b) is, like predator manipulation, also inherently a long-term proposition.
289
Effective habitat alteration to serve multiple competing uses requires holistic ecosystem understanding, in which it is fully accepted that deer are integral processes whose removal as products reverberates through vast networks of energy, matter and information interchange. Some sense of this is given from exclosure experiments, whose results typically show dramatic impacts of herbivore grazing – the tip of the ripple-effects “iceberg.” Disturbance to this network, because it is so vast and incorporates compensatory checks and balances, tends initially to be unnoticed. But when use is sustained, excessive, or abusive such as to erode the basic organization, systemic deterioration results. “Ecosystem management”, a buzzword but also a principle that IMM could conceivably implement, is of the essence in sustainable best uses of living resources. The
Figure 7. Sixty-year (1941–2001) simulation trials based on manipulating single control parameters. (a) Single factor control trial involving manipulation of yearling male harvest only. The lighter trace in this and subsequent panels is the baseline simulation of Figure 6b for comparison. (b) Single factor control trial involving manipulation of adult male harvest only. (c) Single factor control trial involving manipulation of predation only. (d) Single factor control trial involving manipulation of habitat quality only. (e) Single factor control trial involving manipulation of female deer harvest only.
290
R. W. Sage †, B. C. Patten & P. A. Salmon
simulation shows that habitat adjustments can significantly moderate deer population extremes, perhaps more effectively in preventing and damping overshoot (seasons 40–80 and 210–240, approximately), less so in checking undershoot (seasons 120–160). As a sensitive factor with long-term connotations and consequences, the present model indicates habitat quality to be one of the potentially most effective control parameters on the front line of the arsenal for multiple-use management. Figure 7e: Adjusting female deer harvest. Doe hunting is rarely allowed in the Adirondacks. This is understood as a severe action in a region of harsh climate that already takes a natural toll on the deer population. Hunting females is the strongest single control parameter in the model (Figure 5e). The Figure 7e simulation shows it to be effective in limiting overshoot (approximate seasons 60–80, 105–115, and 205–240), but it does also encourage undershoot (seasons 80–95, 118–135, and 215–230) because the population suppression is strong. To summarize, the foregoing results show that none of the five controls in the model manipulated singly allow the population to be maintained substantially within the bounds of 10–20 deer/2.5 km2. Even though the control parameters have different strengths (Figure 5 panels), the experimental trials basically maintain the general up-and-down form of the baseline simulation. Where this goes up over a period of years, so do the experimental simulations, and when the baseline trend is downward, so generally are those of the manipulation trials. There is not enough management power in single control parameters to straighten the trajectories and constrain them within the prescribed limits. The model, therefore, verifies the paper’s premise that single-factor control is not a viable strategy for managing exploited populations. To achieve sustainability, protocols based on integrated multi-parameter manipulation must be developed. The principles parallel those for IFR flight, and are illustrated in the next, and last, simulations. 3.6 Management simulations: multiple-factor The panels of Figure 8 depict a sequence of multifactorial trials progressively engaging different and additional control parameters. Figures 8a and 8b: Bucks-only hunting. Comparison of Figure 8a with Figures 7a and 7b shows harvesting of both adult and yearling males to be more effective than either one singly. In this particular trial the effects seem greater in preventing undershoot (seasons 15–25 and, extensively, 130–215) than in damping overshoot (which does not occur). This suggests that male culling would not be effective in reducing an
overpopulated herd, but could reduce the amplitude and frequency of population nadirs. However, Fig. 8b shows results of maximum male hunting throughout the simulation, which make clear that culling males does lower the trajectory relative to the baseline. Maleonly harvest appears able to reduce the population to within the desired range. The Figure 5e curve for maximum female harvest is repeated in Figure 8b to show the sharp decline induced by this treatment. Local extinction occurs within 15 years (60 seasons). Figure 8c: Male and female hunting. Figure 8c shows that adding females to male-only hunting is moderately effective in steering the population within the specified limits. As previously concluded in connection with Figure 5e, doe harvest is the single most sensitive parameter in the model. The present simulation expresses this in several quick tumbles from high to low density (approximately, seasons 59–60, 75–76, 105–115, and 161–163), verifying what is already wellknown empirically – that harvesting a population’s reproductive animals encourages instability and surprise. Figure 8d: Habitat improvement and predator control. Habitat quality control and regulation of predators are long-term management practices. In the present simulations the corresponding parameters were manipulated, like the others, on a short-term basis. Therefore, results do little more than reflect immediate sensitivities, not the lags or accumulated inertia inherent in their longterm expression. Figure 8d indicates this pair of control parameters manipulated in concert moderates baseline highs and lows. Sudden declines occur twice (seasons 59–60, as in Figure 8c also, and 210–218). Altogether, these two factors jointly demonstrate a fair capability to maintain population density within the desired range. Taking the Figure 8a–d results as a group, it cannot be said that manipulating parameters two or three at a time achieves positive control. The multifactorial trials improve on the single-factor ones, but in both cases weakness is evidenced by the fact that the basic undulating curve form of the baseline simulation is little changed in the experimental trials. Strong control should be able to induce basic change in the nominal shape of the trajectory. This we see in the next two trials, which engage all the control parameters simultaneously, emulating IFR flight. Figure 8e: Integrated all-factor control, Trial 1. In this simulation, excursions outside the established limits are quickly damped to within the shaded objective zone. All-factor control is effective, though not 100 percent so. Three overshoots and fewer than 20 undershoots are recorded, all mostly minor. Sev-
Flying the North American Adirondack whitetail
eral population crashes occur, around seasons 170, 190, and 210, the first more significant than the others. Figure 8f: Integrated all-factor control, Trial 2. Here, again, a definite capability to straighten the baseline curve form is demonstrated. Seven overshoots and under 20 undershoots occur. There are also seven tumbles of ± 10 deer/2.5 km2, more or less, along the way,
291
around seasons 45, 60, 70, 95, 140, 210, and 225, the last being particularly dramatic. In summary, adjusting all five control parameters available in the model enables the population to be steered effectively within the desired range of densities. This could not be achieved by manipulating fewer factors, and in particular, single factors. Only multiple “pressure points” have the power to straighten the sinuous curve form of the baseline simulation enough to
Figure 8. Sixty-year (1941–2001) simulation trials based on manipulating multiple control parameters. (a) Multifactorial control involving regulation of harvest of adult and yearling males (darker trajectory), referenced (in this and subsequent panels) to the Figure 6b baseline simulation (lighter trajectory). (b) Effects of maximally harvesting adult and yearling males (darker trajectory), and also females (extinction trajectory to left). (c) Multifactorial control through regulation of both male and female hunting. (d) Multifactorial control through manipulation of both predation and habitat quality. (e) Multifactorial control through manipulation of all five control parameters in the model: Trial 1, and (f) Trial 2.
292
R. W. Sage †, B. C. Patten & P. A. Salmon
allow the whitetail to be “flown” substantially within the designated objective zone.
Conclusions: need for institutionalized model-making The foregoing results uphold the proposition that management of resource populations would benefit from concerted, modeling-assisted, multifactorial control in accordance with the principles put into refined practice in instrument flight. The state-of-the-art norm is fragmented single-factor control based on scientific specialization. In a word, this is the instrument aviator’s “fixation.” We make no claims for the model employed in the case study beyond the facts of the demonstration. It is a population model, and it does represent a synthesis of long-term data filtered through years of field experience. The empirical experience makes it evident that many more places for monitoring and control exist in real systems, and that somehow the frontal focus on direct parameters such as fecundity, mortality, and recruitment, or direct impacts of exogenous demands like predation and harvest, is not enough. There are also indirect, implicit, and often remote factors within and outside the bounds of the ecosystem, such as those weakly captured in concepts like “habitat quality” and “winter severity”, that lie beyond the sphere of narrowly-conceived population biology. It is the sense of embedding or enfolding given by the field experience that motivates the observations to follow. The white-tailed deer, or any other resource population, is not an isolated or autonomous inhabitant of an ecosystem wherein it can just happen fortuitously to make a living. In its inhabitancy it becomes a vital, integral, and deeply coupled process in a system that is vastly more complex than the population itself (Patten and Jørgensen 1995). Above, we referred to multifactorial control being “modeling assisted”, not “model assisted.” We make a subtle distinction. Like the whitetail seen as a product of and process in an ecosystem, a model is a product of the process of modeling. The model product of ultimate interest in the management sphere, we have come to believe, is one that takes wholeness and natural complexity into account. And the way to do this is to engage the modeling process as an ongoing enterprise in the science-and-management dialogue. Models should serve human interests from their strengths, not their weaknesses. We see models and modeling as the only current tool of science to allow coherence to be brought to natural complexity. Moreover, since the truth of models is always open to question and proof, the way of doing modeling for applied purposes is to emphasize the process more and
the product less. This is our advocacy. To carry it out requires some shift in perspective about the relationship between models, modeling, and the applied interests intended to be served. The strength of modeling currently is not with its products. That models do not work across a wide spectrum of uses is an incontrovertible fact. Typically, they are perceived to “work” until they fail, and a population or industry collapses as a result. Part of the reason is the theory of modeling (Zeigler 1976) is underdeveloped as a technical area of science, and there seems little current interest in developing it despite great interest in making models. The fact is that making a mathematical representation of a system is a complicated process in its own right. The main steps (Jørgensen 2001) fall into three categories: • Conceptualization, involving aggregation of variables and loss of information (Cale 1995; Luckyanov 1995); • Formulation, involving choice of mathematical forms and functions, usually from habit and fashion with little guidance from theory; and • Quantification, involving data-gathering to calibrate parameters, then verifying predictions with independent data sets in validation. Given the realities of model inadequacies in resource management, we consider there must be fundamental change in how the responsible institutions work. They should emphasize modeling more and models less – Institutionalized Model-Making (IMM), as we call it, putting process over product. Conventional approaches proceed with a product orientation, that is, an endpoint to be realized in the form of a usable predictive model. The process of arriving there often involves one or more “modelers” charged with making operational sense of scientists’ accumulated databases. Product over process is emphasized, and the modeling activity is designed deliberately to minimize complexity by selectively leaving things out. The rationale is many things are irrelevant, and insufficient data exist to implement a more comprehensive approach. This differs from real nature where nothing is left out. We think the IMM prescription can be realized by changing modeling from being endpoint driven to a continuing process of codifying existing knowledge and evolving with data whose acquisition it drives. We see the true “modelers” in institutions to be not technical modelers in the conventional sense, but scientists, managers and others who have expertise and interest as stakeholders. That is, by workshops, brainstorming sessions, one-on-one interactions, and other variations on team approaches, subject-matter experts engage in primary conceptualization, the first phase of modeling (above). In this vision, modelers per se provide for-
Flying the North American Adirondack whitetail
mats and technical know-how to facilitate an ongoing process, seen as perpetual and cross-generational in institutional organization wherein scientists, managers, and stakeholders come together under a conceptual umbrella they themselves create. To some extent, IMM could even promote a degree of science-driven, bottom-up organization as an alternative to traditional top-down administration. Over the long period of an institution’s lifetime, IMM would endeavor to include, in coarse to fine resolution, everything significant about a subject population and its encompassing ecosystem. If the sixty years of data reflected in our case study, and other investigations on the Huntington Forest over the same period, had been accompanied by concurrent integrative modeling, our laboratory would now be in a position to robustly close the model-management loop of Figure 3. Avoiding specialty-derived fixation, we could now with some assurance and skill fly the whitetail and other living resources in our charge “on instruments” in the manner we hope to have demonstrated. Past technology and fragmentation through specialization might not have allowed this, and institutions cannot go back. But present technology and developing holistic understanding do allow it, and institutions can go forward from where they are to chart new courses in more comprehensive directions. The state of the world’s living resources, attained under management rubrics focused on populations in ecosystems increasingly degraded by man, demands something better. IMM, as a modeling-based approach that ... • addresses natural complexity in terms of whole ecosystems, • engages process over product in multidisciplinary team-building, • enables multifactorial control by direct and indirect parameters, and
293
• fosters revised institutional frameworks ... could, in a variety of permutations to suit different circumstances, become at least a part of this something better. Acknowledgment: John A. Tougas, Computer Services Specialist I at the University of Georgia’s Institute of Ecology, converted the original PowerPoint figures for this paper, in color, to black and white.
References Cale WG (1995) Model aggregation: ecological perspectives. In: Complex Ecology. The Part-Whole Relation in Ecosystems. (eds BC Patten, SE Jørgensen): 230–241. Prentice Hall, Englewood Cliffs, New Jersey. Jørgensen SE (2001) Fundamentals of Ecological Modeling, 3rd ed. Elsevier, Amsterdam. Luckyanov NK (1995) Model aggregation: mathematical perspectives. In: Complex Ecology. The Part-Whole Relation in Ecosystems. (eds BC Patten, SE Jørgensen): 242–261. Prentice Hall, Englewood Cliffs, New Jersey. Paquet PC, Strittholt JR, & Staus WL (1999) Wolf reintroduction feasibility in the Adirondack Park – a GIS-based modeling analysis of potential wolf introduction into Adirondack Park using habitat suitability and genetic factors. Report of the Conservation Biology Institute, 260 Southwest Madison Avenue, Corvallis, Oregon 97333 (http://www.consbio.org/cbi/what/wolf.htm). Patten BC & Jørgensen SE (1995) Complex Ecology. The Part-Whole Relation in Ecosystems. Prentice Hall, Englewood Cliffs, New Jersey. Zeigler BP (1976) Theory of Modeling and Simulation. Wiley, New York.
Received 11. 02. 02 Accepted 07. 01. 03
294
R. W. Sage †, B. C. Patten & P. A. Salmon
In Memoriam
It was a blue summer day, August 6, 2002, when our colleague Dick Sage died suddenly on Whiteface Mountain in the New York Adirondack Mountains. He was doing what he loved most in his professional life, being in the field with students. He collapsed from a heart condition he struggled with for years. A self-described “stumpy” (meaning old-school, nononsense woodsman and wildlifer/forester), Dick began some eight or ten years earlier an improbable interaction with a new-fangled systems ecologist type, me. He did this with a reluctance and skepticism I never heard expressed, but could imagine only an earthy vernacular such as he might use would adequately convey, as in ...“buncha crap.” We started to work, feeling our way along at intellectual arms length, on a deer model we agreed would be a compendium of known knowledge about the whitetail in the Adirondack forest ecosystem. It did not take long for Dick to realize that modeling was making organized sense of the countless bits and pieces of data and information he possessed, that heretofore had been only a clutter in his mind and files. Resistance gave way to awakening, and enthusiasm and active advocacy soon followed. It might have been a stretch, but I often said of Dick that, in embracing modeling and systems thinking as an organized way to make sense of the world, he became in his later life a true systems ecologist. Not methodologically, but philosophically. He ended his life a
holist, and no wildlife or forest manager ever wore that badge better. Dick knew everything beforehand that slowly found its way into our models, which now hopefully will slowly find their way into the literature. But it took the modeling process, and the models that emerged from this, to make him realize how much he knew. It was all there; modeling just gave it form and made it accessible. This power of modeling to organize information and data, and make it operational, is inherent in the name we gave our process “Institutionalized Model-Making.” This is featured in this paper. For the last few years of his life Dick took delight in the fact that he could field virtually any question from a lay or scientific audience, place it in context of ordered multifactorial complexity as represented in our model, and never be tempted to give a single factor answer. Nor did acquaintance with the fact of complexities ever overwhelm him or tie his hands, quite the contrary. Modelbuilding organized the expansive knowledge he spent a lifetime acquiring, and released his deeper insights to be conveyed with clarity and authority. No one did it better, and the world is a little less by his not being allowed to do it longer. The model of this paper is not the comprehensive one he and I worked on for years. It is his model, rendered originally by him in dBASE software and later converted to STELLA by Paulette. My role was ..., well, let’s just say an erstwhile pilot and modeler stumbled onto an analogy between instrument flight and wildlife management. For both Paulette and I, and all the others of the Huntington Forest family who give a uniqueness to that small place that is the Adirondack Ecological Center, we want it recorded how much we miss him – his knowledge, his personhood, his consummate professionalism, and his love for what he did. So long, Dick; thanks for letting us into your life, and for adding so much unforgettable to ours. Bernie
J. Nat. Conserv. 10, 280–294 (2003) © Urban & Fischer Verlag
Nature Conservation
http://www.urbanfischer.de/journals/jnc
Flying the North American Adirondack whitetail on instruments A multi-parameter modeling approach to ecosystem-based wildlife management Richard W. Sage Jr.†1, Bernard C. Patten2,* & Paulette A. Salmon1 1 2
Adirondack Ecological Center, SUNY College of Environmental Science and Forestry, Newcomb, New York 12852, USA Institute of Ecology, University of Georgia, Athens, Georgia 30602, USA; e-mail: [email protected]
Abstract We compare ecosystem-based wildlife management to instrument flight of aircraft. Airplanes cannot be controlled without visual ground reference, or if this is impossible to a cluster of flight instruments. Instrument pilots are trained to develop a rhythmic scan of the cluster to monitor and correct flight path and attitude. The untrained tendency is to fixate on a single gauge. Then, the aircraft deviates from its desired attitude and trajectory, and control may be lost. Fixation is like single-factor management wherein variables like habitat quality, recruitment, predator control, or harvest rates are singled out for adjustment without considering the others. Ungulate populations are no less complex than aircraft in flight. They are multifactorial and move through time and space. To be managed effectively they must be guided in these movements through the monitoring and control instruments nature has provided. These are not necessarily proximate because populations are embedded in ecosystems and cannot be isolated from systemic complexity. Holistic management is needed, which requires a suite of monitoring and control parameters analogous to those in instrument flight. We hold that every institution serving the interests of wise resource use should employ comprehensive, large-scale, ecosystem-based models built, tested, and perfected as a committed institutional activity over long periods of time. We call this Institutionalized Model-Making (IMM), and see models drawn from the collective expertise of scientists and their data in the same relation to nature as flight simulators are to actual aircraft. They mimic responses to control actions, and enable training in multifactorial management analogous to the instrument scans and control actions of the instrument pilot. A model comprehensive enough for institutional use has not been built. We call attention to our efforts to develop such a model at the Huntington Wildlife Forest in New York’s Adirondack Mountains. This is a model of the North American whitetail deer (Odocoileus virginianus Miller). It is too complex and incomplete for use in this paper, so a smaller-scale model is employed to make the case. In simulation trials parameters are ranked as to control sensitivity, then manipulated singly and multiply to demonstrate the superiority of the multiparameter approach. Key words: Adirondack Park, ecosystem management, Institutionalized Model-Making, instrument flight, STELLA simulation, white-tailed deer.
1. Introduction Managing exploited populations on a sustainable basis is an ideal not fully realized in wildlife and fisheries management. The paradigm of maximum sustained yield (MSY), for example, which has motivated many approaches during the last half-century, has proved largely ineffective. Living resources are overexploited
to the edge of local extinction, or mismanaged to the point of overpopulation. The North American whitetailed deer of the present study, for example, is currently widely overpopulated in much of Eastern USA. A new approach is needed that restructures management along more holistic lines, and the purpose of this
*corresponding author
1617-1381/03/10/04-280 $ 15.00/0
Flying the North American Adirondack whitetail
paper is to outline such an approach based on modeling. We will exemplify our ideas by focusing on a local population of the white-tailed deer, seen not as an isolated population in the classic wildlife management sense, but as a process and product of a whole ecosystem in which it is deeply embedded as an integral component. 1.1 Our region The ecosystem in question is the Adirondack Park of New York State. This is a 2.4-million hectare area in the northern corner, fully one-sixth of the area of the entire state (28,600 km2). The park was established as a mix of public and private lands in 1882 in response to wide scale exploitation by forest practices of the time, the public lands to be restored and preserved “forever wild” for the people of New York. Geologically, the park is part of the Canadian Shield, a region of very old Grenville rocks recently uplifted to form the Adirondack Dome. The Park contains 42 peaks > 1200 m, the highest of which is Mt. Marcy (1629 m). Glaciation has produced more than 1,000 glacial lakes, and continuing orogeny adds 1.5 mm annually to elevation. The landscape is a mosaic of wilderness, towns, working forests, and natural areas. Known for its outdoor recreation, the Adirondack Mountains offer some 3200 km of hiking trails, 40 state-operated and many more private campgrounds and lakeside resorts, and hundreds of kilometers of established canoe routes. 1.2 Our outdoor laboratory The Huntington Wildlife Forest in the central Adirondacks is a 6071 ha tract devoted to research in silviculture and wildlife management. Elevations range 475–816 m of mountainous topography (slopes 0–40%). There are five lakes and numerous beaver ponds (628 ha), 38 km of streams, and 32.2 km of unpaved roads. Forests (5443 ha) include old growth (> 150 y; 1089 ha), managed even- and uneven-aged stands (2994 ha), and fire-succession communities less than 100 years old (1360 ha). Forest types are 50% northern hardwoods (beech, birch, maple; 2722 ha), 15% coniferous (spruce, fir, pine, hemlock; 816 ha), and 35% mixed hardwood and coniferous (1905 ha). 1.3 Our animal The North American white-tailed deer (Odocoileus virginianus Miller) has an estimated New York State population of 1-million and an Adirondack Park population of 40–50,000 animals. The Huntington Forest population ranges 100–500. Age at first reproduction is
281
1.5–2.5 years, and litter size is 1–2. Sex ratios (M:F) vary 1:3–1:12. Adult males (called “bucks”) weigh 56–114 kg (live weight) and adult females (called “does”) 43–79 kg. Males live 10 y and females up to 17 y. Survival is generally low in young-of-year animals (fawns), and 2nd-year (yearling) age classes experience chronic winter losses. Occasionally, adult (breeding) and sub-adult (non-breeding) animals suffer severe winter mortality. Hunting is generally permitted for males only. Since 1943 the Adirondack harvest of males has ranged 5–13,000, depending on population density. 1.4 Our laboratory: Institutionalized Model-Making The Adirondack Ecological Center is a field station of SUNY’s College of Environmental Science and Forestry located on the Huntington Forest. At this station we are exploring “Institutionalized Model-Making” (IMM) as an approach to ecosystem-based, multiparameter resource management. Some envisioned characteristics of the IMM approach are as follows: 1. IMM emphasizes process over product. Ecological modeling is usually seen as a technical procedure with the goal of producing a predictive computer model as a final product. Modelers charged with the responsibility of synthesizing one or more disparate data sets into a coherent representation of some system of interest normally do the modeling. We take a somewhat different approach. Recognizing that the true information for a model resides not only in data, but also in the minds of subject-matter experts who produce the data, we seek to engage primary scientists in the modeling process, particularly in the all-important first phase of model conceptualization. 2. IMM facilitates and structures people interactions. The formats inherent in model products can be used a priori to structure conceptual modeling. Workshops and other situational activities can be employed to draw forth the requisite information. In the best of worlds we see ongoing conceptual modeling by multidisciplinary groups of scientists (and others, like stakeholders) as a permanent fixture in the organization of research laboratories and other institutions with regional research and management responsibilities. Institutionalized modeling activity on a continuing basis can forge coherent shared perspectives from initially divergent viewpoints and opinions. This potential of modeling which places process over product is as yet largely untapped in resource management fields. 3. IMM captures and organizes the knowledge stateof-the-art. Whether one or many scientists share their knowledge
282
R. W. Sage †, B. C. Patten & P. A. Salmon
and data, the developing model becomes an organized repository for current information, great or small. As such, it: • Identifies knowns and unknowns, • Guides research directions and priorities, • Facilitates scientific interactions, • Formats databases, • Informs management decision-making, • Enables coherent communication with constituencies, • Holds the place for continuing further development, • Enables ecosystem-based holistic management. As a permanent and evolving part of institutional organization, IMM has enormous potential for facilitating cross-disciplinary dialogue, structuring databases, and recording the state-of-the-art pertaining to institutional areas of responsibility. As models are never really complete and can always be improved, model evolution itself can become a permanent feature in the life and dynamics of institutions as personnel, goals, and priorities shift and change with changing times and responsibilities. We see such models not as quick products for predictive problem solving, but as robust, comprehensive, and above all operational, compendia of available knowledge and data in process of ongoing development and improvement. As such, they can be used to explore and manage the complexities of manipulated resource dynamics within existing frames of their encompassing ecosystems. The foreseen activities, we suggest, are not unlike flying a modern aircraft.
2. “Flying” the Adirondack whitetail To maintain a stable, directional flight trajectory from a point of origin to a destination, an aircraft must be able to navigate and monitor its flight configuration, and exert controls to maintain an acceptable perfor-
mance window. The essence of control is negative feedback. 2.1 Cybernetic Control The elements of cybernetic control by negative feedback error regulation are illustrated in Figure 1. Common man-made control systems include aircraft autopilots, automobile cruise controls, and thermostats. Objective functions such as, respectively, attitude and heading, velocity, and room temperature, are specified and performance is monitored. Deviations beyond specified ranges are registered as error and fed back to the controller, which acts on the system controlled to correct the error. Such deviation damping or error reduction constitutes “negative feedback”, the basis of cybernetic control. In aircraft flight the three functions of navigation, monitoring, and control must be performed continuously from the moment of departure to the moment of touchdown. Deviation from desired trajectories and flight attitudes is constantly and instantaneously corrected. Instrument flight (“IFR” – meaning under Instrument Flight Rules), makes uninterrupted continuation of all functions possible should the visible horizon on which visual flight (“VFR” – under Visual Flight Rules) depends be lost, such as when flying in clouds. IFR flight requires special training beyond that for VFR. 2.2 The instrument flight cockpit In an IFR cockpit the basic instruments for monitoring are an altimeter, magnetic compass, artificial horizon/attitude indicator, vertical speed indicator, turn-and-bank coordinator, and airspeed indicator. The latter is marked in three color-coded zones – normal (green), caution (yellow), and a never-exceed speed (or “red-line”) beyond which structural damage to the airframe may occur. Control in three-dimensions is provided by
Figure 1. Generalized cybernetic control by negative feedback.
Flying the North American Adirondack whitetail
ailerons (roll), horizontal stabilizer (pitch), and rudder (yaw). In addition there are flaps to increase lift, and engine power to climb, descend, or maintain straight-andlevel flight at desired airspeeds. The navigation instruments are irrelevant to the present discussion. 2.3 The IFR instrument scan The instrument cockpit is complex. VFR pilots and inexperienced IFR trainees tend to focus on and stare at a single gauge. This is called fixation. The gauge in question may be showing the plane in straight-andlevel flight, or at a safe airspeed, even as the remaining instruments are unwinding and showing that the aircraft is out of control. Fixation can be likened to single-parameter resource management, for example in connection with the white-tailed deer, adjusting harvest rates, culling predators, or undertaking habitat improvement (but not, concertedly, all three). To maintain control when reference to the visual horizon is lost, a continuous scan and actuation of all the monitoring and control instruments is necessary. Instrument flight training is devoted to learning how to make such scanning and correcting unconscious and automatic.
283
The same principle, we suggest, is applicable to the management of exploited resource species. 2.4 An instrument panel from STELLA Figure 2 shows an analog in the simulation software STELLA (High Performance Systems, Hanover, New Hampshire, USA) of the flight instrument cockpit. Graphic outputs (left column) and numerical outputs (middle and right columns, center) enable selected parameters of a model to be monitored. A warning device (middle column, top) flashes green when the parameter it represents is operating within a prescribed zone, yellow to indicate caution when outside this zone, and red to signify great enough deviation from a desired trajectory to be considered alarming. Control devices include dials and toggle switches (bottom center, the two smaller devices), and sliders (right column, group of three) to change the values of constant parameters during simulation, and a graphic input device (upper right) to allow time-varying parameters to be configured, also during simulation. Any number of these devices may be added to accommodate desired monitoring and control variables of a simulation model as needed.
Figure 2. An “instrument panel” from STELLA. The devices are described in the text.
284
R. W. Sage †, B. C. Patten & P. A. Salmon
2.5 The “renewable resources instrument scan” Populations exploited as living resources are no less complex than an IFR aircraft. In many ways, especially in identity and numbers of potential parameters for monitoring and control, they are more complex. They differ also in having been designed by nature, not by man for human use. Being deeply embedded in ecosystems as processes and products of entire natural organizations, relevant parameters may not be directly associated with the focal resource, per se. They may instead have origins in remote events or processes of diffuse or multiple character propagated through layers of complexity to emerge eventually as ripple effects manifested in the subject resource. Thus, the IMM approach must eventually create models not only of the resource populations of interest, but also of their encompassing ecosystems. This must be done in enough detail to incorporate a rich set of direct and indirect monitoring and control parameters, both proximate to the populations and also distal in the ecosystem. And, for effective application of the principles of instrument flight, a systematic “renewable resources instrument scan” must be developed to avoid the nemesis of instrument flight – “fixation.” Complex living resources cannot be managed by single-factor control. Sustainability requires a continuous scan and adjustment of multiple monitoring and control parameters. The white-tailed deer, as a case in point, must be “flown on instruments.” 2.6 Interplay of modeling, monitoring and control: indicator variables Not all the parameters directly or indirectly influencing a complex, embedded, resource system have equal
weight. Those more proximate, or learned to be of greater significance in the distal causal network, may be selected as a plausible monitoring subset. These are indicator variables. In the IMM context they should have, at a minimum, the following characteristics: 1. They should be relatively easy to observe and measure empirically; time-series data are always difficult and expensive to acquire in monitoring programs. 2. They should be variables to which model dynamics are sensitive; this can be determined in simulation trials by sensitivity analysis. 3. The model dynamics they reflect should be accessible through one or more control parameters; otherwise management interventions cannot be identified. 4. The control parameters within models should have manipulable empirical counterparts; otherwise management actions cannot be implemented. Figure 3 illustrates the model-management loop, showing how empirical indicator variables feed back through monitoring and control parameters of models to empirical management parameters.
3 Sixty-year case study: 1941–2001 An ecosystem-based model of the white-tailed deer, under development at the Huntington Forest over a period of years, is too complex to use as a case study in this paper. This model meets the criteria we see for the IMM conception in being a comprehensive, operational, and open-ended repository of a laboratory’s knowledge, concepts, and long-term data under continual development through scientist-modeler interac-
Figure 3. The model-management loop relating empirical indicator variables to management parameters through the monitoring and control parameters of models.
Flying the North American Adirondack whitetail
tions. A simpler population model, prepared by Sage in dBASE (unpublished), and converted to STELLA by Salmon (unpublished), will serve here to illustrate “flying the whitetail.” 3.1 dBASE/STELLA population model This model computes quarterly population changes as a balance of positive and negative factors prevailing during each season of the year (Figure 4). The database is a 60-year record compiled at the Huntington Forest (1941–2001). Factors included in the model are winter severity, population density, fawn survival, predation, illegal hunting and crippling, area inhabited, habitat quality, reproduction, recruitment, hunting, and roadkill. The three factors underscored will be manipulated as control parameters in simulation trials presented further below. 3.2 Winter severity Adirondack winters can be quite severe. For the whitetailed deer winter is defined by snow depth, specifically the number of days when depths equal or exceed 38.1 cm (15 in). Snow exacts a heavy metabolic toll for activity during such periods of prolonged near-starvation. Adaptation calls for minimizing physical movements. When snow reaches the ± 38.1 cm threshold, the animals leave their normal “summer” range and move to winter “yarding” areas. These are typically under coniferous vegetation, which reduces snow on the ground, moderates temperatures, and provides some low quality browse. In 60 years of daily snow depth data taken at the Huntington Forest, the longest winter (in deer terms, the severest) was 1943 when snow cover was ≥38.1 cm for 138 days. The shortest (mildest) winter on record was 1983 when snow never
285
exceeded the critical depth. An empirical winter severity index (WSI) is computed as the ratio of days with ≥38.1 cm to a hypothetical maximum of 140 days, just above the 1943 record. The lighter trace in Figure 5.1 shows the 60-year (240-season) WSI record for the Huntington Forest as generated in a STELLA simulation run. The time line is in seasons because the model computes the deer population as discrete values for each quarter-year over the 60-year period. All the simulations in Figure 5 (and later, Figures 6 and 7) appear continuous because the discrete seasonal values run together. 3.3 Strength of control parameters As indicated in Section 3.1, factors to be manipulated as control parameters in STELLA simulation trials include hunting, predation, and habitat quality. Five adjustable parameters with varying strengths to influence simulated dynamics represent these: harvest of adult males, yearling males, and females; predation; and habitat quality. The panels of Figures 5a–f show simulations establishing the general sensitivity of population change to each of these five controllers. Results, described below, condition a baseline simulation (Figure 6) against which trajectories generated by singlefactor (Figure 7) and multi-factorial (Figure 8) management trials will subsequently be compared. Figure 5a: Best case conditions. The lighter curve in the figure shows the 60-year WSI record for reference. The darker trajectory shows simulated deer densities, beginning at a realistic, moderate population density of 10 deer/2.5 km2 (= mi2). This is the bestcase simulation, corresponding to no hunting, no predation, and maximum habitat quality. Over the period and under these conditions, densities increase to unrealistic levels of overpopulation in approximately exponential fashion, visibly modulated by winter severity. Figure 5b: Poorest habitat. When habitat quality is set to its minimum value, and the other parameters still maintained at no hunting and predation, deer densities (darker trace) decrease to low but realistic levels compared to those of Figure 5a (lighter curve – this will be included in subsequent panels for reference). This establishes habitat quality as a sensitive parameter in this model.
Figure 4. Sixty-year mean biomass (kg ha–1) and animal numbers (ha–1) during six periods and the four seasons of each year from 1941–2001, as recorded on the Huntington Forest.
Figure 5c: Maximum hunting, adult males. With best habitat quality restored (here and in the Figure 5d–f simulations to follow), maximum harvesting of adult males significantly reduces the population. Seasonal amplitudes (absolute, though probably not relative) are somewhat dampened compared to the best-case sce-
286
R. W. Sage †, B. C. Patten & P. A. Salmon
nario. These results suggest harvesting adult males is a moderately sensitive control parameter in the model.
compared. The following baseline trajectory will serve this purpose.
Figure 5d: Maximum hunting, yearling males. Yearlings are much smaller in proportion to the total population than adults, and their reproductive contribution is proportionately less. As a result, yearling harvest has only small impact on the population’s growth capacity. This variable does have a unique property, among those included in the model, of increasing the amplitudes of seasonal fluctuations in comparison with the best and other cases. This appears to reflect reproductive and recruitment biology. Permanent dispersal away from their natal home range is coincident with the late-autumn rut in which yearlings in the Adirondacks hardly participate. Steep fourth-quarter drops in density observed in Figure 5d appear to reflect fall dispersal in this segment of the population. Reciprocally, high firstand second-quarter recruitment from surviving fawns of the previous year, observed in the figure, effectively replenishes the yearlings in spring and summer. In general, in the model, yearling-male harvest can be considered an insensitive control parameter.
Figure 6a: To generate this baseline control parameters were set to values near the middle of their ranges, except female hunting which was set at a low nonzero value. As shown, over the sixty-year interval the population tracks reasonably closely above and below the initial level.
Figure 5e: Maximum hunting, females. Female deer are critical to population continuance. In the severe climate of the Adirondacks harvest of this segment of the population just before the rut in November–December with winter rigors approaching can be devastating. The simulation shows this. Although female hunting is the model’s most sensitive control parameter, we do not intend to generalize this to milder climates or other situations where the practice may well be a useful element in the tool kit of multi-parameter management. Figure 5f: Maximum predation. Predator control is difficult to implement in practical management. When deer predation is maximized the population is suppressed slightly lower than when adult males are subjected to maximum hunting (Figure 5c). Predation rate can therefore be considered a moderately sensitive, if hard to manage, control parameter. In summary, from comparison of the Figure 5b–f simulations with the best case (Figure 5a), ranking of control parameter strengths investigated in accordance with the sensitivity of population change to their manipulation is, in descending order: female harvest > habitat quality > predation > adult male harvest > yearling male harvest. In Section 3.5, exploring single-factor parameter manipulations, these will be taken up in reverse order, that is, of increasing regulatory strength. 3.4 Baseline simulation Manipulations of control parameters require a reference simulation against which simulated results can be
Figure 6b: Here, for better visualization, the baseline simulation of Fig. 6a is rescaled to a restricted range of values encompassing the reference behavior. This trajectory will appear for comparison as a lighter trace in all the panels of Figures 7 and 8. The management goal will be to remain within the objective zone of 10–20 animals/2.5 km2, shown as the shaded area above the “red line.” The baseline trajectory shows two runs of under-population, one quite extended, and three of overpopulation. 3.5 Management simulations: single-factor Figure 7 shows simulated results of attempts to manage the Huntington Forest deer population model within the specified limits. The “red-line” can be taken as a minimum acceptable population level, in analogy with the never-exceed maximum airspeed of an aircraft in flight. Overpopulation (> 20 deer/2.5 km2) will also be considered undesirable, but at moderate levels less critical. The management scenarios for Figure 7 are singlefactor trials in order of increasing control capacity as previously determined (Section 3.3). These simulations are analogous to single-instrument fixation in IFR flight. In Figure 8 further below, a number of multiple-factor trials are presented that correspond to the coordinated instrument scan and continuous attitude correction required for safe instrument flight. Figure 7a: Adjusting yearling male harvest. This weakest control parameter has little capacity by itself to exert significant change in baseline dynamics. It is possible, by maximizing yearling hunting when population is high and minimizing it when low, to achieve some moderation of extremes and remain statistically within the goal envelope more than does the baseline case. Figure 7b: Adjusting adult male harvest. This second-weakest control factor has somewhat more capacity than the previous to keep the population within desired limits. Again, maximizing hunting during high population periods and minimizing it during low periods moderates extremes compared to the baseline, most strongly during approximately the seasonal interval 150–205. This simulation suggests adjusting adult-
Flying the North American Adirondack whitetail
287
Figure 5. Sixty-year simulations (1941–2001) of the Huntington Forest deer population under various single-factor conditions referenced to the best-case simulation. (a) Best-case simulation corresponding to no hunting and no predation under the best available habitat quality conditions. The monitoring and control instrument cluster (see Figure 2) used to “fly the whitetail” and generate the simulations is shown at the top. Due to space limitations these have been deleted from all other panels in Figures 5–8. The three warning devices on the left indicate snow depth, winter severity index, and deer density. The dial controls simulation area but was inactivated. The five sliders clustered to the right of center represent control parameters and are marked with ovals to visually enhance their relative settings (high, low, intermediate). Large arrows like the one shown were used to call attention to the slider studied in each simulation (in this case habitat quality). The lighter trace is the 60-year record of the winter severity index. Short-term effects of severe and mild winters on the generally increasing long-term trend are evident. (b) Same as (a) but now with the worst available habitat quality. The lighter trace in this and subsequent panels is the best-case simulation of (a) for comparison. (c) Same as (a) but now with maximum harvest of adult males. (d) Same as (a) but now with maximum harvest of yearling males. (e) Same as (a) but now with maximum harvest of females. (f) Same as (a) but now with maximum predation.
288
R. W. Sage †, B. C. Patten & P. A. Salmon
Figure 6. Sixty year (1941–2001) baseline simulation generated by intermediate values of habitat quality, predation, and harvest to serve as the reference trajectory for subsequent simulation trials of Figures 7 and 8. Values near the middle of their ranges were selected, except for female hunting which was set at a low nonzero value. (a) Ordinate scaled as in Figure 5 panels for comparison with those simulations. (b) Ordinate rescaled to active range of simulation, and management goal superimposed, for comparisons with simulations of Figures 7 and 8.
male-only hunting as a sole means of management would be ineffective. Figure 7c: Adjusting predation. This somewhat stronger parameter than the previous has a little more capacity to maintain the deer population within the stated bounds. A difference compared to male hunting
(the two previous factors) is a tendency for reduced predation to encourage overshoot. Therefore, though predator control or enhancement is possible as a longterm management strategy, this simulation suggests it would not only be a weak tool by itself, but could also encourage undesirable prey-predator oscillations. Introduction of gray wolves (Canis lupus) into the
Flying the North American Adirondack whitetail
Adirondack Park has recently been considered, but rejected for several reasons (Paquet et al. 1999). Although the Adirondacks provide ample habitat, they lack sufficient landscape connectivity with surrounding regions to enable normal wolf ranging. Also, wolf introduction would impact not only the whitetail, but also its currently dominant non-human predator, the coyote (Canis latrans), and human harvest patterns as well. Moreover, genetic studies indicate the original endemic Adirondack species to be the Eastern Canadian red wolf (Canis rufus), and contemporary C. latrans to be hybrids with this species. Figure 7d: Adjusting habitat quality. This second strongest control parameter (Figure 5b) is, like predator manipulation, also inherently a long-term proposition.
289
Effective habitat alteration to serve multiple competing uses requires holistic ecosystem understanding, in which it is fully accepted that deer are integral processes whose removal as products reverberates through vast networks of energy, matter and information interchange. Some sense of this is given from exclosure experiments, whose results typically show dramatic impacts of herbivore grazing – the tip of the ripple-effects “iceberg.” Disturbance to this network, because it is so vast and incorporates compensatory checks and balances, tends initially to be unnoticed. But when use is sustained, excessive, or abusive such as to erode the basic organization, systemic deterioration results. “Ecosystem management”, a buzzword but also a principle that IMM could conceivably implement, is of the essence in sustainable best uses of living resources. The
Figure 7. Sixty-year (1941–2001) simulation trials based on manipulating single control parameters. (a) Single factor control trial involving manipulation of yearling male harvest only. The lighter trace in this and subsequent panels is the baseline simulation of Figure 6b for comparison. (b) Single factor control trial involving manipulation of adult male harvest only. (c) Single factor control trial involving manipulation of predation only. (d) Single factor control trial involving manipulation of habitat quality only. (e) Single factor control trial involving manipulation of female deer harvest only.
290
R. W. Sage †, B. C. Patten & P. A. Salmon
simulation shows that habitat adjustments can significantly moderate deer population extremes, perhaps more effectively in preventing and damping overshoot (seasons 40–80 and 210–240, approximately), less so in checking undershoot (seasons 120–160). As a sensitive factor with long-term connotations and consequences, the present model indicates habitat quality to be one of the potentially most effective control parameters on the front line of the arsenal for multiple-use management. Figure 7e: Adjusting female deer harvest. Doe hunting is rarely allowed in the Adirondacks. This is understood as a severe action in a region of harsh climate that already takes a natural toll on the deer population. Hunting females is the strongest single control parameter in the model (Figure 5e). The Figure 7e simulation shows it to be effective in limiting overshoot (approximate seasons 60–80, 105–115, and 205–240), but it does also encourage undershoot (seasons 80–95, 118–135, and 215–230) because the population suppression is strong. To summarize, the foregoing results show that none of the five controls in the model manipulated singly allow the population to be maintained substantially within the bounds of 10–20 deer/2.5 km2. Even though the control parameters have different strengths (Figure 5 panels), the experimental trials basically maintain the general up-and-down form of the baseline simulation. Where this goes up over a period of years, so do the experimental simulations, and when the baseline trend is downward, so generally are those of the manipulation trials. There is not enough management power in single control parameters to straighten the trajectories and constrain them within the prescribed limits. The model, therefore, verifies the paper’s premise that single-factor control is not a viable strategy for managing exploited populations. To achieve sustainability, protocols based on integrated multi-parameter manipulation must be developed. The principles parallel those for IFR flight, and are illustrated in the next, and last, simulations. 3.6 Management simulations: multiple-factor The panels of Figure 8 depict a sequence of multifactorial trials progressively engaging different and additional control parameters. Figures 8a and 8b: Bucks-only hunting. Comparison of Figure 8a with Figures 7a and 7b shows harvesting of both adult and yearling males to be more effective than either one singly. In this particular trial the effects seem greater in preventing undershoot (seasons 15–25 and, extensively, 130–215) than in damping overshoot (which does not occur). This suggests that male culling would not be effective in reducing an
overpopulated herd, but could reduce the amplitude and frequency of population nadirs. However, Fig. 8b shows results of maximum male hunting throughout the simulation, which make clear that culling males does lower the trajectory relative to the baseline. Maleonly harvest appears able to reduce the population to within the desired range. The Figure 5e curve for maximum female harvest is repeated in Figure 8b to show the sharp decline induced by this treatment. Local extinction occurs within 15 years (60 seasons). Figure 8c: Male and female hunting. Figure 8c shows that adding females to male-only hunting is moderately effective in steering the population within the specified limits. As previously concluded in connection with Figure 5e, doe harvest is the single most sensitive parameter in the model. The present simulation expresses this in several quick tumbles from high to low density (approximately, seasons 59–60, 75–76, 105–115, and 161–163), verifying what is already wellknown empirically – that harvesting a population’s reproductive animals encourages instability and surprise. Figure 8d: Habitat improvement and predator control. Habitat quality control and regulation of predators are long-term management practices. In the present simulations the corresponding parameters were manipulated, like the others, on a short-term basis. Therefore, results do little more than reflect immediate sensitivities, not the lags or accumulated inertia inherent in their longterm expression. Figure 8d indicates this pair of control parameters manipulated in concert moderates baseline highs and lows. Sudden declines occur twice (seasons 59–60, as in Figure 8c also, and 210–218). Altogether, these two factors jointly demonstrate a fair capability to maintain population density within the desired range. Taking the Figure 8a–d results as a group, it cannot be said that manipulating parameters two or three at a time achieves positive control. The multifactorial trials improve on the single-factor ones, but in both cases weakness is evidenced by the fact that the basic undulating curve form of the baseline simulation is little changed in the experimental trials. Strong control should be able to induce basic change in the nominal shape of the trajectory. This we see in the next two trials, which engage all the control parameters simultaneously, emulating IFR flight. Figure 8e: Integrated all-factor control, Trial 1. In this simulation, excursions outside the established limits are quickly damped to within the shaded objective zone. All-factor control is effective, though not 100 percent so. Three overshoots and fewer than 20 undershoots are recorded, all mostly minor. Sev-
Flying the North American Adirondack whitetail
eral population crashes occur, around seasons 170, 190, and 210, the first more significant than the others. Figure 8f: Integrated all-factor control, Trial 2. Here, again, a definite capability to straighten the baseline curve form is demonstrated. Seven overshoots and under 20 undershoots occur. There are also seven tumbles of ± 10 deer/2.5 km2, more or less, along the way,
291
around seasons 45, 60, 70, 95, 140, 210, and 225, the last being particularly dramatic. In summary, adjusting all five control parameters available in the model enables the population to be steered effectively within the desired range of densities. This could not be achieved by manipulating fewer factors, and in particular, single factors. Only multiple “pressure points” have the power to straighten the sinuous curve form of the baseline simulation enough to
Figure 8. Sixty-year (1941–2001) simulation trials based on manipulating multiple control parameters. (a) Multifactorial control involving regulation of harvest of adult and yearling males (darker trajectory), referenced (in this and subsequent panels) to the Figure 6b baseline simulation (lighter trajectory). (b) Effects of maximally harvesting adult and yearling males (darker trajectory), and also females (extinction trajectory to left). (c) Multifactorial control through regulation of both male and female hunting. (d) Multifactorial control through manipulation of both predation and habitat quality. (e) Multifactorial control through manipulation of all five control parameters in the model: Trial 1, and (f) Trial 2.
292
R. W. Sage †, B. C. Patten & P. A. Salmon
allow the whitetail to be “flown” substantially within the designated objective zone.
Conclusions: need for institutionalized model-making The foregoing results uphold the proposition that management of resource populations would benefit from concerted, modeling-assisted, multifactorial control in accordance with the principles put into refined practice in instrument flight. The state-of-the-art norm is fragmented single-factor control based on scientific specialization. In a word, this is the instrument aviator’s “fixation.” We make no claims for the model employed in the case study beyond the facts of the demonstration. It is a population model, and it does represent a synthesis of long-term data filtered through years of field experience. The empirical experience makes it evident that many more places for monitoring and control exist in real systems, and that somehow the frontal focus on direct parameters such as fecundity, mortality, and recruitment, or direct impacts of exogenous demands like predation and harvest, is not enough. There are also indirect, implicit, and often remote factors within and outside the bounds of the ecosystem, such as those weakly captured in concepts like “habitat quality” and “winter severity”, that lie beyond the sphere of narrowly-conceived population biology. It is the sense of embedding or enfolding given by the field experience that motivates the observations to follow. The white-tailed deer, or any other resource population, is not an isolated or autonomous inhabitant of an ecosystem wherein it can just happen fortuitously to make a living. In its inhabitancy it becomes a vital, integral, and deeply coupled process in a system that is vastly more complex than the population itself (Patten and Jørgensen 1995). Above, we referred to multifactorial control being “modeling assisted”, not “model assisted.” We make a subtle distinction. Like the whitetail seen as a product of and process in an ecosystem, a model is a product of the process of modeling. The model product of ultimate interest in the management sphere, we have come to believe, is one that takes wholeness and natural complexity into account. And the way to do this is to engage the modeling process as an ongoing enterprise in the science-and-management dialogue. Models should serve human interests from their strengths, not their weaknesses. We see models and modeling as the only current tool of science to allow coherence to be brought to natural complexity. Moreover, since the truth of models is always open to question and proof, the way of doing modeling for applied purposes is to emphasize the process more and
the product less. This is our advocacy. To carry it out requires some shift in perspective about the relationship between models, modeling, and the applied interests intended to be served. The strength of modeling currently is not with its products. That models do not work across a wide spectrum of uses is an incontrovertible fact. Typically, they are perceived to “work” until they fail, and a population or industry collapses as a result. Part of the reason is the theory of modeling (Zeigler 1976) is underdeveloped as a technical area of science, and there seems little current interest in developing it despite great interest in making models. The fact is that making a mathematical representation of a system is a complicated process in its own right. The main steps (Jørgensen 2001) fall into three categories: • Conceptualization, involving aggregation of variables and loss of information (Cale 1995; Luckyanov 1995); • Formulation, involving choice of mathematical forms and functions, usually from habit and fashion with little guidance from theory; and • Quantification, involving data-gathering to calibrate parameters, then verifying predictions with independent data sets in validation. Given the realities of model inadequacies in resource management, we consider there must be fundamental change in how the responsible institutions work. They should emphasize modeling more and models less – Institutionalized Model-Making (IMM), as we call it, putting process over product. Conventional approaches proceed with a product orientation, that is, an endpoint to be realized in the form of a usable predictive model. The process of arriving there often involves one or more “modelers” charged with making operational sense of scientists’ accumulated databases. Product over process is emphasized, and the modeling activity is designed deliberately to minimize complexity by selectively leaving things out. The rationale is many things are irrelevant, and insufficient data exist to implement a more comprehensive approach. This differs from real nature where nothing is left out. We think the IMM prescription can be realized by changing modeling from being endpoint driven to a continuing process of codifying existing knowledge and evolving with data whose acquisition it drives. We see the true “modelers” in institutions to be not technical modelers in the conventional sense, but scientists, managers and others who have expertise and interest as stakeholders. That is, by workshops, brainstorming sessions, one-on-one interactions, and other variations on team approaches, subject-matter experts engage in primary conceptualization, the first phase of modeling (above). In this vision, modelers per se provide for-
Flying the North American Adirondack whitetail
mats and technical know-how to facilitate an ongoing process, seen as perpetual and cross-generational in institutional organization wherein scientists, managers, and stakeholders come together under a conceptual umbrella they themselves create. To some extent, IMM could even promote a degree of science-driven, bottom-up organization as an alternative to traditional top-down administration. Over the long period of an institution’s lifetime, IMM would endeavor to include, in coarse to fine resolution, everything significant about a subject population and its encompassing ecosystem. If the sixty years of data reflected in our case study, and other investigations on the Huntington Forest over the same period, had been accompanied by concurrent integrative modeling, our laboratory would now be in a position to robustly close the model-management loop of Figure 3. Avoiding specialty-derived fixation, we could now with some assurance and skill fly the whitetail and other living resources in our charge “on instruments” in the manner we hope to have demonstrated. Past technology and fragmentation through specialization might not have allowed this, and institutions cannot go back. But present technology and developing holistic understanding do allow it, and institutions can go forward from where they are to chart new courses in more comprehensive directions. The state of the world’s living resources, attained under management rubrics focused on populations in ecosystems increasingly degraded by man, demands something better. IMM, as a modeling-based approach that ... • addresses natural complexity in terms of whole ecosystems, • engages process over product in multidisciplinary team-building, • enables multifactorial control by direct and indirect parameters, and
293
• fosters revised institutional frameworks ... could, in a variety of permutations to suit different circumstances, become at least a part of this something better. Acknowledgment: John A. Tougas, Computer Services Specialist I at the University of Georgia’s Institute of Ecology, converted the original PowerPoint figures for this paper, in color, to black and white.
References Cale WG (1995) Model aggregation: ecological perspectives. In: Complex Ecology. The Part-Whole Relation in Ecosystems. (eds BC Patten, SE Jørgensen): 230–241. Prentice Hall, Englewood Cliffs, New Jersey. Jørgensen SE (2001) Fundamentals of Ecological Modeling, 3rd ed. Elsevier, Amsterdam. Luckyanov NK (1995) Model aggregation: mathematical perspectives. In: Complex Ecology. The Part-Whole Relation in Ecosystems. (eds BC Patten, SE Jørgensen): 242–261. Prentice Hall, Englewood Cliffs, New Jersey. Paquet PC, Strittholt JR, & Staus WL (1999) Wolf reintroduction feasibility in the Adirondack Park – a GIS-based modeling analysis of potential wolf introduction into Adirondack Park using habitat suitability and genetic factors. Report of the Conservation Biology Institute, 260 Southwest Madison Avenue, Corvallis, Oregon 97333 (http://www.consbio.org/cbi/what/wolf.htm). Patten BC & Jørgensen SE (1995) Complex Ecology. The Part-Whole Relation in Ecosystems. Prentice Hall, Englewood Cliffs, New Jersey. Zeigler BP (1976) Theory of Modeling and Simulation. Wiley, New York.
Received 11. 02. 02 Accepted 07. 01. 03
294
R. W. Sage †, B. C. Patten & P. A. Salmon
In Memoriam
It was a blue summer day, August 6, 2002, when our colleague Dick Sage died suddenly on Whiteface Mountain in the New York Adirondack Mountains. He was doing what he loved most in his professional life, being in the field with students. He collapsed from a heart condition he struggled with for years. A self-described “stumpy” (meaning old-school, nononsense woodsman and wildlifer/forester), Dick began some eight or ten years earlier an improbable interaction with a new-fangled systems ecologist type, me. He did this with a reluctance and skepticism I never heard expressed, but could imagine only an earthy vernacular such as he might use would adequately convey, as in ...“buncha crap.” We started to work, feeling our way along at intellectual arms length, on a deer model we agreed would be a compendium of known knowledge about the whitetail in the Adirondack forest ecosystem. It did not take long for Dick to realize that modeling was making organized sense of the countless bits and pieces of data and information he possessed, that heretofore had been only a clutter in his mind and files. Resistance gave way to awakening, and enthusiasm and active advocacy soon followed. It might have been a stretch, but I often said of Dick that, in embracing modeling and systems thinking as an organized way to make sense of the world, he became in his later life a true systems ecologist. Not methodologically, but philosophically. He ended his life a
holist, and no wildlife or forest manager ever wore that badge better. Dick knew everything beforehand that slowly found its way into our models, which now hopefully will slowly find their way into the literature. But it took the modeling process, and the models that emerged from this, to make him realize how much he knew. It was all there; modeling just gave it form and made it accessible. This power of modeling to organize information and data, and make it operational, is inherent in the name we gave our process “Institutionalized Model-Making.” This is featured in this paper. For the last few years of his life Dick took delight in the fact that he could field virtually any question from a lay or scientific audience, place it in context of ordered multifactorial complexity as represented in our model, and never be tempted to give a single factor answer. Nor did acquaintance with the fact of complexities ever overwhelm him or tie his hands, quite the contrary. Modelbuilding organized the expansive knowledge he spent a lifetime acquiring, and released his deeper insights to be conveyed with clarity and authority. No one did it better, and the world is a little less by his not being allowed to do it longer. The model of this paper is not the comprehensive one he and I worked on for years. It is his model, rendered originally by him in dBASE software and later converted to STELLA by Paulette. My role was ..., well, let’s just say an erstwhile pilot and modeler stumbled onto an analogy between instrument flight and wildlife management. For both Paulette and I, and all the others of the Huntington Forest family who give a uniqueness to that small place that is the Adirondack Ecological Center, we want it recorded how much we miss him – his knowledge, his personhood, his consummate professionalism, and his love for what he did. So long, Dick; thanks for letting us into your life, and for adding so much unforgettable to ours. Bernie