Flexible risk metrics for identifying and monitoring conservation-priority species

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Flexible risk metrics for identifying and monitoring conservation-priority species Jessica C. Stanton a , Brice X. Semmens b , Patrick C. McKann a , Tom Will c , Wayne E. Thogmartin a,∗ a

U.S. Geological Survey, Upper Midwest Environmental Sciences Center, 2630 Fanta Reed Road, La Crosse, WI 54603, USA Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA c U.S. Fish and Wildlife Service, Division of Migratory Birds, Midwest Regional Office, 3815 American Boulevard East, Bloomington, MN 20013, USA b

a r t i c l e

i n f o

Article history: Received 20 March 2015 Received in revised form 5 August 2015 Accepted 5 October 2015 Keywords: Conservation planning Monitoring Multivariate autoregressive state-space models North American Breeding Bird Survey Species of conservation concern Species prioritization

a b s t r a c t Region-specific conservation programs should have objective, reliable metrics for species prioritization and progress evaluation that are customizable to the goals of a program, easy to comprehend and communicate, and standardized across time. Regional programs may have vastly different goals, spatial coverage, or management agendas, and one-size-fits-all schemes may not always be the best approach. We propose a quantitative and objective framework for generating metrics for prioritizing species that is straightforward to implement and update, customizable to different spatial resolutions, and based on readily available time-series data. This framework is also well-suited to handling missing-data and observer error. We demonstrate this approach using North American Breeding Bird Survey (NABBS) data to identify conservation priority species from a list of over 300 landbirds across 33 bird conservation regions (BCRs). To highlight the flexibility of the framework for different management goals and timeframes we calculate two different metrics. The first identifies species that may be inadequately monitored by NABBS protocols in the near future (TMT, time to monitoring threshold), and the other identifies species likely to decline significantly in the near future based on recent trends (TPD, time to percent decline). Within the individual BCRs we found up to 45% (mean 28%) of the species analyzed had overall declining population trajectories, which could result in up to 37 species declining below a minimum NABBS monitoring threshold in at least one currently occupied BCR within the next 50 years. Additionally, up to 26% (mean 8%) of the species analyzed within the individual BCRs may decline by 30% within the next decade. Conservation workers interested in conserving avian diversity and abundance within these BCRs can use these metrics to plan alternative monitoring schemes or highlight the urgency of those populations experiencing the fastest declines. However, this framework is adaptable to many taxa besides birds where abundance time-series data are available. Published by Elsevier Ltd.

1. Introduction Since it first emerged as its own field of study, conservation biology has been described as a ‘crisis discipline’ (Cardillo and Meijaard, 2012; Pullin and Knight, 2001; Soulé, 1985), with policy decisions and management actions proceeding despite uncertainty. Further, conservation biology is implemented under extremely limited resources where prioritization schemes for maximal efficiency abound (Wilson et al., 2006). Discussions are increasing of whether and how triage techniques might determine which species and ecosystems are imperiled beyond the effort and

∗ Corresponding author. Tel.: +1 6087816309. E-mail address: [email protected] (W.E. Thogmartin).

cost of recovery (Bottrill et al., 2008; Clements et al., 2011; Pimm, 2000; Walker, 1992). In practice, however, numerous governmental and nongovernmental organizations operating at international, national, and regional levels express that ‘keeping common species common’ is part of their mandate. A focus on common species would seem at odds with a discipline primarily tasked with identification and recovery of species on the brink of extinction. In reality, conservation seeking to maintain and support species or populations that are not immediately imperiled and conservation seeking to rescue and restore species or populations that are severely degraded and vulnerable are both vitally important to maintaining abundant, diverse, and functioning ecosystems. The issue of persistent declines in population abundance among taxa has been gaining attention recently (Butchart et al., 2010;

http://dx.doi.org/10.1016/j.ecolind.2015.10.020 1470-160X/Published by Elsevier Ltd.

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Dirzo et al., 2014; Inger et al., 2014). The importance of maintaining healthy populations is far from trivial as regional declines may result in a degradation of the character or function of ecosystems (Dirzo et al., 2014; Gaston, 2010), or escalate rapidly to global imperilment or complete extinction (Lindenmayer et al., 2011; Stanton, 2014; Tilman et al., 1994). Referring to the need for triage analogizes the field of conservation biology with that of medicine (Pullin and Knight, 2001; Soulé, 1985). To extend the analogy: effective and efficient emergency care is essential, but so too is good prevention. The evidence used in decision making and management at the regional level should be customizable to the project. Conservation prioritization schemes with the goal of maintaining or restoring regionally healthy populations of species may not find global species extinction risk assessment such as the International Union for the Conservation of Nature (IUCN) Red List (IUCN, 2014) informative, especially if most of the regional species composition is at low risk of global extinction. Nonetheless, local populations may show signs of distress due to changes in land use, exploitation, disease, pollution, or invasive species that are vital to address as soon as possible. Therefore, it is important that conservation programs have metrics and indicators that can be tailored to their specific conservation goals and management agendas. A useful metric or indicator for planning and evaluating a conservation program should have several general properties. Metrics should be objective, easily comprehensible, standardized across time, and able to be generated on a regular basis for monitoring progress. Further, metrics should be directly linked to the phenomenon or process of interest such that a change in the metric directly reflects a change in the condition or true state of the system (National Research Council, 2000). We outline a general framework that can be used to generate region-specific species prioritizations that: (1) are straightforward and relatively easy to implement; (2) may be implemented at different spatial resolutions for projects with regional to national or international focus; (3) are flexible to differing policy and management concerns; (4) use data that are often readily available for many species (abundance time-series data); (5) can accommodate data-gaps and measurement error; (6) can be readily updated with the availability of new data for long-term monitoring and evaluation of management action effectiveness; and (7) can be presented in terms that are easy to communicate to the general public and stakeholders. Another important aspect of this framework is that it provides a way to prioritize species using metrics that incorporate three key aspects of viability: abundance, population trend, and population variability (Lande and Orzack, 1988; Lande et al., 2003; Vucetich et al., 2000; Wilson et al., 2011). Many programmatic species prioritization assessments, such as the State of the Birds report (NABCI, 2009), use metrics including population abundance and trend from long-term time series but ignore a third axis relating to imperilment, that of population variability. Variability in time series of species counts arise from two sources, process error and non-process error (Clark and Bjørnstad, 2004; Holmes, 2001). Process error is variation in the observed count resulting from population growth processes (e.g., random birth-death processes, environmentally driven variation in reproduction), whereas non-process (or observer) errors represent variation in counts resulting from sampling and measurement errors (Clark and Bjørnstad, 2004). This non-process error is akin to noise obscuring the signal of the underlying population dynamics (Lindley, 2003). When non-process error is large and ignored, predictions about future behavior of a population may be incorrect because process variation is overestimated (Clark and Bjørnstad, 2004; Dennis et al., 1991; McNamara and Harding, 2004). Therefore, prioritization metrics based on historical population data should

include trend and variability and should attempt to disentangle these sources of variability. Our approach incorporates trend, abundance, and variability and also allows for the observed variability to be parsed into observer error and process error. The framework first uses timeseries observations of population abundances (or an appropriate abundance index) to estimate the trend and process error of the ‘true states’ of the population through application of a state space model. Then the parameter estimates (trend and process error) are used to project probabilistic future population states using a diffusion approximation model. The resulting metrics are presented in terms of the time until pre-defined decline thresholds are anticipated. These metrics can then be used to assist in prioritizing species for conservation action. The metrics are regionally customizable through the set of species to include in the assessment, the spatial region over which the analysis is conducted, the timeframe over which to estimate the population trends (long-term versus recent), the time horizon for future projection (may be based on regional planning timelines), and the decline thresholds (Fig. 1). We demonstrate this framework by applying it to population time series data available from the North American Breeding Bird Survey (NABBS). We exhibit the flexibility of this approach to the needs of different conservation projects by estimating two types of metrics that can be used to organize species priority rankings. The first metric, time to decline to a minimum monitoring threshold (TMT; Fig. 2a), is the anticipated number of years until a declining population will become so rare as to be virtually undetectable by NABBS survey methodology. This metric might be employed for planning future regional monitoring schemes. The other metric, time to percent decline (TPD; Fig. 2b), is the anticipated number of years until a species’ population abundance will decline by a pre-determined percentage. The level of decline and time-frame to consider can be tailored to the needs and interests of policy makers and managers within and among regions. This metric might be used to identify, and communicate a sense of urgency for, species anticipated to experience substantial declines in the near future. The TMT and TPD metrics, although based on similar calculations, have different meanings and consequences in terms of conservation. The forecast horizons for TPDs are designed to prioritize species within a region based on the magnitude of anticipated declines over relatively short time-frames regardless of whether they are at great risk of local or global extirpation. The TMT metric is designed to identify species projected to become rare (and possibly extirpated) from currently occupied regions. By setting a TMT level well below the abundance threshold deemed to result in low credibility due to an important data deficiency by the NABBS’s own standards (Sauer et al., 2014), the metric aids in identifying species for which an alternative monitoring program may become necessary for continued management. Recent work has questioned the utility of attempting to predict declines in abundance in the near future based on observed past trends in variable populations (Connors et al., 2014; Ward et al., 2014; D’Eon-Eggertson et al., 2015). Since this criticism might call into question the utility of any metrics based on projecting observed trends we also provide an analysis of the predictive accuracy of short-term trends in this dataset.

2. Materials and methods 2.1. Data source We used time series of indices of abundance from the NABBS (Sauer et al., 2013), the primary means for determining regional and continental trends of species in North America (Robbins et al., 1986;

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Fig. 1. Process for establishing regionally customized metrics to support priority species ranking decisions.

Sauer et al., 2013). The NABBS collects data on more than 420 species of birds along more than 4,000 routes. The survey is comprised of 50 listening locations equidistant along 24.5 miles of secondary road. Birds seen and heard within 3-min periods at each of these 50 stops are counted to provide a route-level summary of abundance. The route-level data are then aggregated to regional indices using a model-based approach (Sauer et al., 2013, 2008). We used the annual NABBS abundance indices (Sauer et al., 2014) for 303 species across 33 Bird Conservation Regions (BCRs) between the years 1970 and 2012. We included only diurnal and terrestrial species in the analysis as they are more likely to be adequately sampled by the NABBS methodology (Ralph and Scott, 1981). As a further quality assurance measure, we excluded from analysis any species by BCR combinations we deemed to have very imprecise index estimates. We defined ‘very imprecise’

as a species-BCR combination with a mean ratio of annual 95% confidence interval widths, to corresponding median index, that exceeds 2. 2.2. Trend and variance estimation To analyze species trends at the regional level, we fit a multivariate autoregressive state-space model simultaneously across all BCRs where the species occurred during the NABBS survey and population indices met our data-quality measures. We cast the model in a state-space framework as follows: xt = xt−1 + u + wt , yt = xt + vt ,

where wt ∼MVN(0, Qt )

where vt ∼MVN(0, Rt )

Fig. 2. Illustration of framework for generating customizable metrics for regional species prioritization schemes. In this example, North American Breeding Bird Survey (NABBS) abundance index records are first analyzed in a state-space framework to separate process error (variability) from observer error and represented as fitted states with the observer error removed. Prioritization metrics are represented as the expected time (width of solid horizontal lines represent 95% confidence intervals) for the population to reach a decline benchmark meaningful to the conservation project. Two examples, (a) time to a minimum monitoring threshold (TMT) and (b) time to a predetermined percent decline (TPD), are shown. The framework is also customizable in terms of whether projections are based on long-term or the most recent short-term trends (width of gray bar).

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Table 1 Summary of metrics to prioritize terrestrial and diurnal birds in North America based on variability and trend estimates derived from the North American Breeding Bird Survey (NABBS) summarized over either the entire coverage range of the survey or within bird conservation regions (BCRs). Metric

Description

Threshold(s)

Spatial regions analyzed

Trend summarization period

TPD – Time to percent decline

Projected number of years until current population falls by a given percentage threshold Projected number of years until current population reaches minimum monitoring density threshold

30, 50, or 70%

BCRs, NABBS survey-range

2003–2012

0.01 birds/route

BCRs, NABBS survey-range

1970–2012

TMT – Time to monitoring threshold

where yt was the log abundance index input as an n by T matrix where n was the number of BCRs analyzed for each species and T was the total number of time steps. The unobserved abundance states, xt , was a matrix of the same dimensions. We allowed the model to fit separate and independent terms for population trend, u, and process variance, Qt , for each BCR. Therefore, the parameter u was a n by 1 column vector of independent terms and Qt was a n by n matrix with independently fit parameters along the diagonal, and zeros elsewhere. We constrained observation variance to a single term across all BCRs included in the analysis, therefore Rt was an n by n matrix with identical parameters fit along the diagonal and zeros elsewhere. For example, a species with trajectories in 3 BCRs would be written as:

⎡

x1,t

⎤

⎡

x1,t−1

⎤ ⎡

u1

⎤ ⎡

W1,t

⎤

⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ x2,t ⎦ = ⎣ x2,t−1 ⎦ + ⎣ u2 ⎦ + ⎣ W2,t ⎦ , x3,t

x3,t−1

u3

⎛⎡ ⎤ ⎡ q1 0 0 ⎜⎣ ⎦ ⎢ wt ∼ MVN ⎝ 0 , ⎣ 0 q2 0

⎡

⎤

⎡

⎤ ⎡

0

0

W3,t 0 0

y3,t

x1,t

x3,t

v1,t

v3,t

2.3. Probability of decline With the estimated population trend, variability, and current abundance index for each species within each BCR calculated from the state-space model, we computed the probability of decline according to Dennis et al. (1991), whereby: Pr(decline)t = (u) × ϕ

⎤⎞

×ϕ

⎥⎟ ⎦⎠

(u) = exp

q3

⎤

⎛⎡ ⎤ ⎡ ⎤⎞ 0 r 0 0 ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎣ y2,t ⎦ = ⎣ x2,t ⎦ + ⎣ v2,t ⎦ , vt ∼MVN ⎝⎣ 0 ⎦ , ⎣ 0 r 0 ⎦⎠ . y1,t

comparison of our model output with other North American avian prioritization schemes, such as the regional concern scores from Partners in Flight (Partners in Flight Science Committee, 2012). We also conducted this analysis at the full survey level (i.e., that of the NABBS) using survey-wide index scores for each species allowing the model to fit terms for both observation and process variance. The models were fit using the MARSS package (Holmes et al., 2012) in R (R Core Team 2013).

0

0 0

r

The equation for xt is a first-order autoregressive process. This is a Gompertz model (Dennis and Taper, 1994; Reddingius and den Boer, 1989) with density dependence relaxed; we relax density dependence here because we are primarily concerned with those species which are declining in abundance (Dennis et al., 1991) and are therefore less likely to be influenced by density dependent effects. We allowed the model to fit independent terms for trend and process error for each BCR because preliminary exploration of different model configurations indicated that these parameters can vary markedly across regions. We constrained the observation variance to a single term for each species because we expected that adherence to the NABBS survey protocols across regions to result in similar observation errors (Ahrestani et al., 2013). This model structure also improved the ability of the model to converge on non-zero solutions of both variance types. Implicit in this model structure is the assumption that BCR boundaries delineate biological populations or, at the very least, that large-scale factors influencing general population trends and year-to-year environmental stochasticity act consistently within BCRs. BCR boundaries were designed by the North American Bird Conservation Initiative to encapsulate areas of roughly homogeneous climate, habitat and avian assemblage (CEC, 1999), and are often used as a spatial unit to aggregate and describe North American Bird populations (Jones et al., 2008; Murray et al., 2008; Sauer et al., 2003). This model structure allows for integration or

 (−x + |u|) × t  d sqrt(q × t)

 (−x − |u|) × t  d sqrt(q × t)

 −2ux  d

q

+ exp

 2x × |u|  d q

; (u) = 1 if u ≤ 0;

if u > 0

The parameters u (trend) and q (process error) are as above; xd = ln(n0 /ne ), where n0 and ne > 0 denote the current abundance index value and the monitoring threshold, respectively; and t is the number of projected annual time steps. The probability function is evaluated with standard normal cumulative distribution functions (cdf), denoted by ϕ. We used this equation to calculate both the TPD and TMT risk metrics (Fig. 2 and Table 1) (see Appendix A in supplementary material for example R code). We calculated the probability of the abundance indices declining 30%, 50%, or 70% relative to current indices for generating the TPD metrics. We also calculated the probability of abundance indices declining to a monitoring threshold (ne ) that corresponds to a median route observation density of 0.01 birds/route to generate the TMT metrics. The monitoring threshold we used is one order of magnitude less than the regional abundance index at which the NABBS would consider the data collected through the survey methodology to have an important data deficiency for estimating regional trends (Sauer et al., 2014). We forecast the probabilities of a population reaching given percent declines and the monitoring threshold annually for 150 years to capture the full 95% confidence intervals on the time horizons. However, uncertainty in forecast predictions generally increases with forecast horizon. Therefore, we present TPD of 70% within 50 years, 50% within 30 years, and 30% within 10 years, and the TMT within the next 100 years. 2.4. Predictive accuracy of short-term trends To assess the predictive accuracy of TPD metrics we estimated decline forecasts based on the 10-year survey-wide trends from

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Fig. 3. Map of bird conservation regions (BCRs) showing the proportion of species analyzed within each region showing a declining trend. Note that BCRs 32, 33, 34, 35, 36, and 37 traverse the United States/Mexico border but are shown truncated to the United States because the North American Breeding Bird Survey (NABBS) data from Mexico were not used in this analysis.

1970 to 1979. We then evaluated predicted time periods to each level of decline against the maximum observed percent decline since 1980. We defined a decline as being predicted at each level (positive predicted decline; P) if the median probability of a given percent decline was achieved between 1980 and 2012 and the upper 95% confidence limit was less than 150 years. We considered the prediction to be true (true positive, TP) if the level of percent decline was observed within the predicted timeframe. We defined a forecast to not have a predicted percent decline (negative prediction; N) if the population was either not predicted to decline, predicted to decline by a lower percentage, or predicted to decline by the given amount, but the median probability time was beyond 2012. For each level of predicted percent decline from 5% to 50% we determined how many species were or were not predicted to decline by that amount within the predicted timeframe and counted the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). From this confusion matrix we calculated sensitivity (TP/P), specificity (TN/N), the predictive value of a positive test (TP/(TP + FP)), and the predictive value of a negative test (TN/(TN + FN)). We used the variance estimates from the full dataset of modeled states to avoid confounding uncertainty due to variability with poor model fit from lack of data. We make the assumption that the process error is not variable through time. We used this approach because we were interested in evaluating how well short-term declines in the past predict continued declines in the future (TPD metrics based on the most recent 10-year trends) rather than

whether 10-years of time-series data are sufficient to build a model. 3. Results 3.1. Species in decline We calculated population trends for a total of 3204 species by BCR combinations for 303 diurnal landbird species in 33 BCRs. Within each BCR, 13–45% (mean 28%) of species analyzed showed overall declining population trajectories (defined as negative trend with confidence intervals not encompassing zero) from 1970 to 2012 (Fig. 3 and Table 2). Survey-wide, 42% of species had overall declining population trajectories. 3.2. Time to monitoring threshold Based on trends from 1970 to 2012, we found 37 species with median TMT within the next 50 years (upper confidence limit within 80 years) (see Appendix B for TMT results for each BCR) in at least one currently occupied BCR (range 1–3). We found one species (Rusty Blackbird, Euphagus carolinus) with a TMT within this timeframe across the entire NABBS coverage range, with an additional 8 species with TMT within 100 years (upper confidence limit < 150 years; Fig. 4). Within each of the 33 BCRs, the number of species with TMT within the next century varied from 0 to 18 (mean 4.7) (Table 2; Appendix B). As a percentage of the number of species analyzed,

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Table 2 Summary of results for each bird conservation region (BCR) analyzed. BCR #

4 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37

Number species analyzed

38 122 93 71 139 137 112 134 125 129 99 125 95 79 94 67 92 105 113 101 87 67 92 132 98 119 61 125 66 98 79 57 53

TMT within 80 years

TPD 30% within 10 years

TPD 50% within 30 years

Currently declining

Number meeting threshold

Proportion

Number meeting threshold

Proportion

Number meeting threshold

proportion

Number declining

Proportion

3 7 10 7 3 1 0 10 8 18 2 4 1 2 4 3 6 3 10 3 2 0 1 10 7 11 5 4 2 5 3 0 1

0.08 0.06 0.11 0.10 0.02 0.01 0.00 0.07 0.06 0.14 0.02 0.03 0.01 0.03 0.04 0.04 0.07 0.03 0.09 0.03 0.02 0.00 0.01 0.08 0.07 0.09 0.08 0.03 0.03 0.05 0.04 0.00 0.02

5 13 11 5 5 2 4 9 7 17 3 5 2 7 4 10 10 11 14 6 10 4 4 8 9 12 16 4 5 4 10 5 3

0.13 0.11 0.12 0.07 0.04 0.01 0.04 0.07 0.06 0.13 0.03 0.04 0.02 0.09 0.04 0.15 0.11 0.10 0.12 0.06 0.11 0.06 0.04 0.06 0.09 0.10 0.26 0.03 0.08 0.04 0.13 0.09 0.06

9 28 26 9 12 13 12 29 22 39 18 20 8 10 16 22 24 17 19 15 20 12 11 18 19 31 25 14 12 24 21 11 10

0.24 0.23 0.28 0.13 0.09 0.09 0.11 0.22 0.18 0.30 0.18 0.16 0.08 0.13 0.17 0.33 0.26 0.16 0.17 0.15 0.23 0.18 0.12 0.14 0.19 0.26 0.41 0.11 0.18 0.24 0.27 0.19 0.19

12 52 30 26 30 38 17 60 43 57 38 29 12 11 14 18 16 23 29 29 30 20 21 47 32 45 23 31 16 42 18 10 11

0.32 0.43 0.32 0.37 0.22 0.28 0.15 0.45 0.34 0.44 0.38 0.23 0.13 0.14 0.15 0.27 0.17 0.22 0.26 0.29 0.34 0.30 0.23 0.36 0.33 0.38 0.38 0.25 0.24 0.43 0.23 0.18 0.21

this represents up to 14% (mean 5%) of the total number of species analyzed within a BCR.

3.3. Time to percentage decline If the most recent population trends (from 2003 to 2012) continue, we found 99 species with TPD of 30% within 10 years (with

an upper confidence limit within 30 years) in at least one currently occupied BCR (Appendix C). Survey-wide, we found 16 species with TPD of 30% within the next decade and TPD of 70% within the next 4 decades (Fig. 5). Within the BCRs, an average (mean) of 8% (range 1–26%) of species analyzed have TPD of 30% within 10 years (Table 2). A TPD of 50% within 30 years is expected for 8% to 41% (mean 19%) of species within the BCRs (Table 2).

Rusty Blackbird Bendire's Thrasher Red-cockaded Woodpecker Blackpoll Warbler Black Swi Lewis's Woodpecker Evening Grosbeak Allen's Hummingbird Cerulean Warbler 25

50

75

100

125

Time to monitoring threshold (years) Fig. 4. Time expected to decline to the minimum monitoring threshold (TMT) for species with the greatest risk of declining to the threshold over the entire spatial coverage of the North American Breeding Bird Survey (NABBS). Symbols show the year the median probability is reached and the width of the bars represent the 95% confidence intervals.

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(a)

Scaled Quail

Yellow-billed Magpie

0.8

Accuracy measure

Northern Bobwhite

1.0

Blackpoll Warbler Pacific Wren Black Swi Lark Bunng Allen's Hummingbird

0.6 0.4 0.2 0.0

Bendire's Thrasher

5

House Sparrow Rusty Blackbird

10

15

20

25

30

35

40

45

50

25 30 35 Decline percentage

40

45

50

(b)

Pinyon Jay

1.0

Loggerhead Shrike Olive-sided Flycatcher Cerulean Warbler 0

20

40

60

Time to percent decline (years) Fig. 5. Time expected for species to decline by a given percentage (TPD) for species with the greatest risk of declining by at least 30% within the next decade over the entire spatial coverage of the North American Breeding Bird Survey (NABBS). Symbols show the year the median probability is reached and the width of the bars represent the 95% confidence intervals. Square, years to a 30% decline; circle, years to a 50% decline; triangle, years to a 70% decline.

3.4. Predictive accuracy of short-term trends Survey-wide species trends observed between 1970 and 1979 combined with estimates of annual variability were successfully able to predict whether a given percent decline would be observed between 1980 and 2012. The predictive value of a negative test was consistently high across percent decline values (mean 0.99) while sensitivity declined slightly with increasing predicted percent decline but remained above 0.8 (mean 0.94) (Fig. 6). Specificity and predictive value of a positive test were generally lower (means 0.71 and 0.53, respectively) over most percent decline predictions with specificity increasing with decline value and predictive value of a positive test decreasing (Fig. 6a). These patterns were generally driven by few false negatives (FN; population declined more than it was projected to within predicted timeframe) (mean 2.7, range 0–5) across all percent decline predictions. The number of false negatives appears in the denominator of the sensitivity and predictive value of a negative test calculations. False positives (FP; population was projected to decline by a greater percentage than it did within predicted timeframe) were more common across percent declines (mean 62, range = 43–78). However, a number of the trajectories we initially classified as false positives did, in fact, decline by the projected percentage between 1980 and 2012, only faster or slower than the projected timeframe. Across 4 of the 10 projected decline percentages a maximum of one species declined 1–2 years faster than the projected decline window and a mean of 9.5 species declined slower by a mean of 4.8 years (range 2–8 years). Taking the precautionary approach of reclassifying these false positives as true positives improved both the specificity and predictive value of a positive test measures across all decline percentages (Fig. 6b). 3.5. Degenerate models Our state-space formulation occasionally resulted in models where the process variance for a BCR was estimated to be equal to zero (14% of the species by BCR combinations, 433 of 3204). This can sometimes occur when there is not enough information in the data

Accuracy measure

Chestnut-collared Longspur

0.8 0.6

Sensivity Specificity PredVal + PredVal -

0.4 0.2 0.0 5

10

15

20

Fig. 6. Predictive accuracy measures of estimated time to percent declines based on observed 10 year trends in 1980 evaluated against maximum observed percent declines since 1980. The predictive accuracy measures were calculated for each percent decline ranging from 5% to 50% in 5% increments. Sensitivity is the probability of a decline being correctly predicted and was calculated as true positives/(true positives + false negatives). Specificity is the probability that a population that did not decline by a given percentage was correctly predicted and was calculated as true negatives/(true negatives + false positives). The predictive value of a positive test (PredVal +) is the probability that a positive result is correct and was calculated as true positives/(true positives + false positives). The predictive value of a negative test (PredVal −) is the probability that a negative result is correct and was calculated as true negatives/(true negatives + false negatives). A positive prediction was defined as having a median time to a given percent decline between 1980 and 2012 and a defined upper confidence limit (i.e., less than 150 years). In (a) a positive prediction was considered to be true if the observed percent decline fell within the predicted window. In (b) the measures were calculated with a precautionary interpretation that predicted declines that were observed to reach the threshold, but outside of the predicted timeframe (either faster or slower), were also considered as correct. Measures were calculated based on the survey-wide fitted states.

to estimate both process and observation error (Holmes et al., 2012) or when variance does not show strong temporal auto-correlation. Projecting the TMT or TPD from a model with zero process variance resulted in time estimates with zero ranges on the confidence intervals. 4. Discussion Data from the NABBS illustrate how this framework might assist in developing region-specific metrics for conservation planning, species prioritization, and monitoring. Depending on the scope and aims of individual projects, the metrics can be tailored to address specific goals. For example, a TMT-type metric might aid in planning species-specific or guild-specific monitoring protocols, whereas, TPD aids in identifying species in rapid decline regardless of current conservation status or abundance. As an example of how these metrics might inform future conservation or the need for further data collection, we highlight a few case studies below. We found five species ranked highly in both TMT and TPD risk metrics showing agreement at both regional and survey-wide geographic scales. They are Rusty Blackbird, Bendire’s Thrasher (Toxostoma bendirei), Blackpoll Warbler (Setophaga striata), Black Swift (Cypseloides niger), and Allen’s Hummingbird (Selasphorus

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sasin). The declining trends of some of these species have been noted (Greenberg and Matsuoka, 2010; Shuford and Gardali, 2008) though the causes are generally not well understood. Partners in Flight, a consortium of governmental and non-governmental organizations associated with North American avian conservation (Beissinger et al., 2000), lists Bendire’s Thrasher, Black Swift and Allen’s Hummingbird as WatchList species (Panjabi et al., 2012; Partners in Flight Science Committee, 2012). Rusty Blackbird and Bendire’s Thrasher are listed as vulnerable by IUCN (others are least concern; IUCN, 2014). As far as we are aware, of these species, only Rusty Blackbird currently has a dedicated working group to coordinate monitoring across the entire species range (Greenberg and Matsuoka, 2010). NABBS monitoring may already present some data deficiencies for these species due to low abundance, few routes, or imprecise abundance indices (Sauer et al., 2014). We anticipate NABBS to become an even less appropriate monitoring tool for these species as the monitoring threshold is approached. The risk metrics we propose for aiding in ranking priority species are based on data that may be compromised by data quality issues as species become rare. Therefore, sound population monitoring programs appropriate for the species of concern help to ensure more accurate risk metrics. In some instances, improved data collection or consultation of a different data source may determine that priority status is not warranted (Dunn, 2002). Additionally, metrics based on NABBS data may be biased for species for which a large proportion of their breeding range is to the north or south of the survey area (i.e., Blackpoll Warbler and Black Swift; Butchart, 2003). While relatively few of the continental North American landbirds we analyzed are projected to reach the monitoring threshold across their entire NABBS coverage range, approximately 40% of the species have nonetheless been declining for the past 4 decades; this is similar to the percentage of bird species declining globally (SCBD, 2010). In addition, nearly 40 species are at risk of decline to the monitoring threshold over some portion of their range (i.e., a currently occupied BCR) within the next 50 years. Approximately 5% (up to 14%) of landbird species currently breeding within a given BCR may require alternative approaches to monitoring in the near future. Managers within an individual BCR may decide not to implement species-specific monitoring for some species if the BCR is on the margin of current distribution and the population is abundant and not declining in other regions, or if the species is not native to that region. However, targeted monitoring within BCRs may improve the resolution for multiple species simultaneously (e.g., Howe and Roberts, 2005). Targeted regional monitoring approaches may involve off-road transects (Deluca and King, 2014) for improved sampling of specific land cover types (Bart et al., 1995; Thogmartin et al., 2006), higher density of sample locations, longer sampling periods, multiple visits to sampling locations within a season, or surveying at different times of day to observe species with nocturnal or crepuscular activity periods. Surveys for rare species should involve methodology allowing for estimation of detection probability (MacKenzie et al., 2005). Targeted regional monitoring may also allow for estimation of management-level habitat needs (Beaudry et al., 2010) and response to localized disturbance events or management practices (Hutto, 2005). Our estimates of risk are based upon historical patterns in abundance, trend, and variability. These historical patterns may or may not hold in the future. Trajectories can occasionally shift from an increasing trajectory to a decreasing trajectory. Projections based on longer-term (since 1970) trend estimates may underestimate short-term risks if current trends show more severe declines than historical averages, or overestimate risk if populations are currently stable or in a recovery phase. Likewise, projections based on only the most recent trends may fail to account for changing

conditions and may be inappropriate for projecting far into the future. Therefore, it is useful to consider both short-term projections based on the most recent trends as well as projections based on trends observed over a longer time-frame. Due to the length of time series data, we were only able to evaluate the utility of using short-term trends (10-year) projected over relatively short time horizons (approximately the next 30 years). Recently it has been suggested that caution is necessary when interpreting risk estimates based on population trends in variable environments (Connors et al., 2014; D’Eon-Eggertson et al., 2015). However, we found that past decline provided a robust prediction of future declines (Fig. 6). Decisions about the whether to base the metric on long-term or short-term trends, the time horizons for future projections, and the decline thresholds would ideally result from conversations with experts in the taxa being analyzed, as well as project managers and other decision makers. The possibility to use these metrics as a communication tool could also be taken into consideration. For example, some regions may routinely make 30 year plans, so presenting the results in terms of projections over the next 3 decades may be useful for those projects. Alternately, decisions about the percent decline thresholds may be considered in term of the level of tolerable risk. For instance, there may be a lower threat tolerance for species that are already rare or of special concern. In those cases, the time to an expected decline of 10% may be of interest. Whereas, species that are still considered common may have a higher risk tolerance, and a higher decline threshold (e.g., 30% or 50%), may be more relevant. The species prioritization framework we outlined is relatively easy to apply to the individual needs and goals of regional conservation projects. We do caution that because the framework lacks potentially relevant species-specific biological detail, and given the possibility of data deficiency problems as species approach the monitoring threshold, it is most appropriate for aiding prioritizations or rankings of large numbers of species or identifying species for further investigation rather than as predictions of individual species risks on their own. Details such as non-linearity in observed trends, changing conditions and threat factors, detectability, differences in intrinsic recovery capacity, and relative responsiveness to conservation measures are ignored. Further, we do not intend this framework to replace or supplant other programmatic assessments that may already be in place for regional conservation planning or species prioritizations such as sub-global application of the IUCN Red List (IUCN, 2012) or the State of the Birds Report (NABCI, 2009), as those assessments typically include species-specific information and considerations that this framework does not accommodate. However, those in-depth assessments tend to be data and labor intensive or reliant on expert input, and therefore may be infrequently implemented or fully reevaluated. Metrics based on our framework are quantitative and can be updated as frequently as the abundance monitoring data are normally collected, with minimal additional labor. We recommend this framework in the context of a quantitative approach to aid in identifying species in need of further investigation for the consideration of conservation priority status, common species in steep decline, and species for which current monitoring methodology is (or is soon likely to become) inadequate for monitoring purposes. This framework can also be useful to identify co-incidental shifts in risk patterns across habitat or feeding guilds possibly heralding an emerging threat with targeted impacts (e.g., diseases, pesticides, changes in land-use); and geographic regions showing unusually high proportions of species at risk. In addition, these metrics can be generated annually (or with the frequency of abundance survey data) and therefore can be used for tracking changes in the conditions of priority species or the effectiveness of conservation actions.

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