Modelling biophysical controls on stream organic matter standing stocks under a range of forest harvesting impacts

Limnologica 78 (2019) 125714

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Modelling biophysical controls on stream organic matter standing stocks under a range of forest harvesting impacts

T

⁎

Alex C.Y. Yeunga, , Karolina Stenrothb, John S. Richardsona a b

Department of Forest and Conservation Sciences, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden

A R T I C LE I N FO

A B S T R A C T

Keywords: Forestry Headwater streams Benthic organic matter Discharge Litter inputs Temperature

Forest harvesting could induce diverse responses of terrestrial-derived coarse particulate organic matter (CPOM) quantity in small streams. Understanding the basis of such variation requires the assessment of the independent and interactive effects of the controlling processes of stream CPOM quantity. Here we simulated post-harvest responses of leaf litter-derived CPOM quantity in a coastal rainforest stream in British Columbia, Canada, using a published process-based model. We compared the relative importance of major biophysical controls of CPOM quantity, including riparian litterfall, discharge, and stream temperature, across a range of severity of forest harvesting disturbance, using a sensitivity analysis. This range represented published post-harvest responses of these model drivers in temperate North America. We then varied the values of model drivers to examine possible changes in CPOM quantity (within ˜4 years post-harvest) under different harvesting scenarios, and to characterise the interactions among pairs of drivers. The effects of litterfall reductions due to forest harvesting on depleting CPOM quantity were at least an order of magnitude greater than those of elevated peak flows. Summer stream warming of 4 °C or more could lead to a smaller magnitude of CPOM reductions, possibly due to decreases in CPOM consumption and shredder biomass that lasted until fall. Warming-induced CPOM increases could counteract the effects of reduced litterfall and elevated peak flows on lowering CPOM quantity, depending on disturbance severity. CPOM depletions were highly likely when litterfall was below 50% of that in undisturbed conditions. Our heuristic modelling revealed that non-additive, antagonistic interactions between paired model drivers could emerge at higher severity levels of disturbance. We suggest that establishing riparian buffer zones would more likely mitigate post-harvest changes in CPOM quantity through minimising alterations in litter inputs and stream summer temperature. Our study illustrates the utility of process-based simulations and scenario analysis in evaluating the ecological impacts of biophysical processes operating at reach to catchment scales. A wider adoption of these modelling approaches can improve the predictions of stream ecological responses to watershed disturbances.

1. Introduction Land-use change and disturbances in watersheds can differentially alter physical, chemical, and biological processes in streams (e.g., Mellina and Hinch, 2009; Richardson and Béraud, 2014; Ferreira et al., 2016; Rogger et al., 2017). Understanding the relative importance of the controlling processes and their interactions is paramount to better assessing and forecasting the ecological impacts of disturbances (Evans et al., 2012). Such process-based understanding can be formulated into more targeted management and/or restoration actions to mitigate anthropogenic disturbances and facilitate recovery (e.g., Quinn et al., 2007; Beechie et al., 2010; Cuddington et al., 2013). In forested headwater streams, terrestrial-derived organic matter ⁎

(e.g., leaf litter, fruits, seeds) provides critical energy and resource subsidies to consumers, and controls many ecosystem processes. In particular, the quantity and quality of coarse particulate organic matter (CPOM, > 1 mm in diameter) can strongly influence stream food-web productivity through detritivorous invertebrates (shredders) and higher trophic levels (Richardson, 1991; Wallace et al., 1999; Kominoski et al., 2012). CPOM breakdown, through shredder feeding, microbial decomposition, and physical abrasion (Graça et al., 2015), produces fineparticulate organic matter (FPOM; < 0.45 μm – 1 mm in diameter), which can subsidise invertebrate collector-gatherers in local reaches and further downstream (Richardson and Neill, 1991; Bundschuh and McKie, 2016). CPOM can also provide substrates for microbial activity, and indirectly mediates stream metabolism and nutrient retention

Corresponding author at: Department of Forest and Conservation Sciences, The University of British Columbia, Vancouver, BC, V6T 1Z4, Canada. E-mail address: [email protected] (A.C.Y. Yeung).

https://doi.org/10.1016/j.limno.2019.125714 Received 19 February 2019; Received in revised form 1 August 2019; Accepted 15 August 2019 Available online 16 September 2019 0075-9511/ © 2019 Elsevier GmbH. All rights reserved.

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2. Methods

(Crenshaw et al., 2002; Aldridge et al., 2009). Given the ecological importance of CPOM in shaping stream foodweb dynamics, previous studies assessing the ecological impacts of watershed disturbances, including forest harvesting, have measured changes in CPOM standing stocks. Among studies of forest harvesting impacts, post-harvest increases (Garman and Moring, 1991), decreases (Golladay et al., 1989; Garman and Moring, 1991; Göthe et al., 2009; Santiago et al., 2011), or insignificant changes (Smolders et al., 2018) in CPOM standing stocks have been reported. Such diverse responses could be attributed to differences in wood inputs after harvesting (Garman and Moring, 1991), harvesting practices (e.g., timing, retention of varying riparian buffer configurations), and/or environmental context across study sites (Richardson and Béraud, 2014). In order to better explain and forecast the spatial variability in stream CPOM responses to forest harvesting (and other disturbances), it is necessary to quantitatively relate these responses to the key biophysical factors controlling CPOM standing stocks, including riparian litter inputs, discharge, and temperature (Webster, 1983; Webster et al., 2001; Stenroth et al., 2014). It is logistically prohibitive to deploy longterm (e.g., multi-year), replicated manipulations of these reach- and catchment-scale factors in combination to understand their relative importance in mediating CPOM standing stocks (Bennett and Adams, 2004). Furthermore, it is unclear how these factors may interact to yield diverse CPOM responses under forest harvesting, as it is challenging to perform field-based correlative and manipulative studies that involve multiple controlling factors. To achieve these goals, we adopted process-based modelling to simulate CPOM responses to forest harvesting, based on their causal links to key biophysical factors. In this study, we used a mass-balance, ecosystem model to simulate temporal dynamics of leaf litter-derived CPOM (excluding wood) on the streambed. This process-based model was formulated by Stenroth et al. (2014), and was developed specifically for small streams (< 10 m bankfull width). Key external biophysical parameters of this model (i.e., model drivers) include aerial litter inputs, discharge, and stream temperature. We advanced the application of this model by quantifying how changes in the model drivers, reflecting different forest harvesting practices, could influence CPOM standing stocks in similar stream-riparian systems. Our emphasis was on the short-term responses (≤5 years after forest harvesting) of model drivers, which are typically greatest and vary little within this timeframe (see also Grant et al., 2008; Sweeney and Newbold, 2014). Our first study objective was to compare the relative importance of the model drivers in influencing CPOM standing stocks in a small, forested stream in coastal British Columbia, Canada, by performing a sensitivity analysis. We examined a broad range of changes in model drivers in this sensitivity analysis, which differs fundamentally from the one by Stenroth et al. (2014) in which only two levels for each model driver were considered to represent contrasting stream types (i.e., small forested and open-canopy streams). To establish a broad range of disturbance severity, we conducted a literature summary and analysis of the post-harvest responses of aerial litter inputs, discharge, and temperature in small temperate streams in North America. The second study objective was to heuristically vary model drivers in different combinations to examine specific forest harvesting scenarios under which CPOM standing stocks in the focal stream would more (or less) likely be lower than in undisturbed conditions. We hypothesised that logging-induced reductions in litterfall and increases in discharge would lead to depletions in CPOM standing stocks, whereas the effects of stream warming depended on the relative thermal responses of shredder consumption and microbial processing of leaf litter (see Stenroth et al., 2014). This heuristic modelling also allowed us to detect complex, previously unknown interactions between multiple model drivers. Our study findings can reveal which model driver(s) should be the management focus to more effectively minimise the impacts of anthropogenic disturbances in similar forested watersheds on stream CPOM standing stocks.

2.1. Literature analysis of post-harvest responses of model drivers To evaluate the relative influence of post-harvest changes in discharge and stream temperature on CPOM standing stocks, we focussed on their major components, which were peak flows, and stream summer temperature, respectively. This is because post-harvest increases in peak flows could enhance the advection input of CPOM and its re-entrainment more than changes in low-flow conditions (e.g., Gurtz et al., 1988; Kiffney et al., 2000; Richardson et al., 2005). Post-harvest increases in stream summer temperature potentially affect many biological processes related to CPOM dynamics (Moore et al., 2005; Leach et al., 2012). Furthermore, this approach limited the number of drivers in the model to ensure an informative sensitivity analysis (Cuddington et al., 2013). We reviewed published responses of riparian litterfall, peak flows, and stream temperature during the northern summer months (from June or July to September) within 5 years after forest harvesting in small temperate streams in North America. Data selection was restricted to low-elevation (≤1100 m above sea level), rain-dominated streams, as their physical habitats are most comparable to those of our focal study stream, East Creek in Malcolm Knapp Research Forest (MKRF; 49°16′N, 122°34′W), in coastal British Columbia. Candidate streams were considered to be rain-dominated based on site-specific descriptions of precipitation and hydrograph. Streams in the Pacific Northwest that are in the coastal ranges of British Columbia, and Oregon and Washington west of the crest of the Cascade range (Moore and Wondzell, 2005), and below the upper boundary of the transient snow zone (approximately 1100 m; Grant et al., 2008) were broadly classified to be within the rain-dominated zone. For each measured response, the characteristics of watershed disturbances associated with forest harvesting, including the type of harvesting practices (e.g., clearcut, partial cut), percentage of the watershed area harvested, the presence and the width of riparian buffers, were also recorded. The first phase of literature search was conducted by cross-checking references in relevant review studies of logging-associated changes in peak flows (Guillemette et al., 2005; Moore and Wondzell, 2005) and stream summer temperature (Moore et al., 2005; Sweeney and Newbold, 2014). The reported values of changes were verified by checking the original papers cited in these studies. To identify additional literature, the second phase of bibliographic searches using online databases (including Web of Science™ and Google Scholar®) were performed in September 2018. Keywords used in the searches were stream*, harvest*, logging*, clearcut*, thinning*, and retention*, in combination with litterfall*, litter input*, with peak flow*, peak discharge*, and with temperature*. Studies with data for at least one model driver were included in the literature analysis. Very few studies on riparian litterfall responses to forest harvesting within 5 years of harvests were returned in the initial search. For this reason, the search extended to all literature of post-harvest responses of riparian litterfall in temperate North America, regardless of the timing of measurements taken. For peak flows and stream temperature responses, when they were analysed across time periods since harvesting (e.g., 0–8 years), the mid-point of the specified period (i.e., 4 years) was noted to determine whether the response qualified for inclusion in the analysis (i.e., within a 5-year post-harvest window). The literature range of responses of model drivers was considered to broadly encompass the range of uncertainty surrounding these drivers, or the severity of forest harvesting disturbance affecting them, that could occur in East Creek. The 25th percentile, 50th percentile, 75th percentile, and the maximum of the data distribution of published responses of stream temperature and peak flows were computed. Peak flows responses varied greatly with the extent of watershed area harvested, therefore these percentile values were calculated separately for response distributions specific to the groups of watershed area logged 2

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across three streams close to East Creek, which added up to an annual input of 310 g AFDM m−2 yr-1 (Richardson, 1992). Daily mean stream temperatures of an average year were derived from temperature records for East Creek (empirically measured from May 1997 to August 2002). The value for each day of the annual temperature curve was smoothed using the temperature of that day and seven subsequent days (see Fig. 2a in Stenroth et al., 2014). Hydrographs used in the model were generated from discharge data from East Creek (empirically measured from 1989 to 2002). A 1500-day hydrograph of average daily discharge (L s-1) was used for each simulation, and the first 90 days of modelling results – as the spin-up period – were discarded to accommodate for the initial stabilization for the model components. Daily CPOM values of the rest of the period were then averaged. An ensemble of ten simulations, each associated with a hydrograph starting on 1st January of a different year (i.e., 1989–1993, 1990–1994,…, 1998–2002), were performed. Therefore, ten hydrograph-specific values of average daily CPOM standing stocks were simulated for each scenario. In the heuristic modelling, for each scenario, we assumed constancy of severity of forest harvesting disturbance, with no recovery of the model drivers throughout the duration of model runs (Fig. 1). Annual (and daily) litterfall values were perturbed to the same extent for each level of disturbance severity, without altering the time-specificity of litterfall (i.e., unchanged percentage contribution by individual days to annual litterfall). Peak flow events driven by storm runoff were delimited from the hydrograph, prior to perturbations. Two adjacent local maxima of discharge were considered storm-induced, when the smaller of these peaks was more than double the intervening local minimum. Exploratory runs of this arbitrary splitting procedure identified distinct discharge pulses, whose timing closely tracked that of rainfall episodes recorded at a climate station in MKRF (see example in supplementary material Appendix A Fig. A.1). This procedure effectively avoided generating false-positive ‘peak flows’ due to minor, natural fluctuations of flows. In each simulation, all peak flows identified (˜30-40 events in each annual hydrograph) were perturbed to the same extent for each level of disturbance severity, while all the other components of the hydrograph were kept unchanged. Daily stream temperature from June to September of the smoothed annual temperature curve was also perturbed to the same extent for each level of disturbance severity.

(i.e., 0–40%; 41–80%, 81–100%). These percentage harvest group ranges were established by Grant et al. (2008), which considered (1) the conceptual scaling of changes in hydrologic processes in relation to the percentage of watershed harvested, and (2) the data spread of peak flow responses to harvesting. Post-harvest stream temperature responses tended to be much attenuated when riparian buffers were present, hence these temperature responses were excluded from percentile calculations. This was to ensure that these percentiles would not be skewed towards lower values by a considerable portion of studies of post-harvest thermal responses that addressed the mitigating effects of riparian protection. The percentile values of peak flows and stream temperature response distributions, and the range of litterfall responses to forest harvesting, provided an environmentally relevant basis for assigning five disturbance severity levels (i.e., undisturbed, low, medium, high and very high severity) to the model drivers. 2.2. Stream CPOM model The stream CPOM model employed in this study incorporates important pathways of CPOM inputs and outputs, such as riparian litter inputs, re-entrainment (which refers to the loss and downstream transport of previously retained CPOM due to increases in discharge), consumption by shredders, and microbial respiration (Stenroth et al., 2014). This model estimates the probability of litter re-entrainment as a function of the relative change in stream discharge (to account for antecedent hydrologic conditions), rather than discharge. The contributions of in-stream wood to CPOM standing stocks are not considered in this model. The parameterization of the stream CPOM model in this study was identical to that in Stenroth et al. (2014), using the same set of empirical data from representative small forest streams (including East Creek) in coastal British Columbia. These streams typically have wetted width < 2.5 m, and riparian vegetation dominated by red alder (Alnus rubra Bong.) and coniferous tree species (for descriptions of climate and other habitat characteristics, see Karlsson et al., 2005; Kiffney and Richardson, 2010). 2.3. Sensitivity analysis and heuristic modelling We performed a simple sensitivity analysis to quantify the effects of individual model drivers relative to others on CPOM standing stocks in East Creek, along a severity gradient of forest harvesting disturbance (see Fig. 1). This gradient was established based on the results of the literature analysis (Section 2.1). One driver was adjusted by an ordinal level of disturbance severity, while all other drivers were kept unchanged. Note that in this sensitivity analysis, systematic adjustment by the same level of disturbance was applied to model drivers, but not by the same ratio. This is because stream temperature is an interval scale, and was hence altered by absolute change in the sensitivity analysis. In contrast, litterfall and peak flows are measured on a ratio scale, and were thus adjusted by ratio of change. In the heuristic modelling, we considered scenarios under the effects of all three model drivers across all severity levels of forest harvesting disturbance (Fig. 1). Model drivers were manipulated across five levels of disturbance severity in a full-factorial design. For undisturbed scenarios, data of model drivers that were empirically measured under noharvest conditions at East Creek or nearby streams were used. Among a total of 125 possible combinations of hypothetical disturbance scenarios generated, 15 of these scenarios (with changes in single drivers only) were the same as those considered in our sensitivity analysis. The procedures of modelling CPOM standing stocks followed Stenroth et al. (2014), and was run in Stella version 10.0.4 (ISEE Systems, Lebanon, NH, USA). For each scenario, the model was set to generate outputs with 1500 time steps (i.e., between 4 to 5 years), with each time step set to one day. CPOM standing stocks were expressed as ash-free dry mass per unit area of streambed (g AFDM m−2). Daily input values of leaf litter were empirically determined and averaged

2.4. Data analyses For each scenario, the natural log of the ratio of hydrograph-specific daily CPOM standing stocks in East Creek to that of the undisturbed (i.e., control) scenario was calculated. Values of this response ratio of CPOM were then averaged across hydrographs (i.e., simulations) to give a mean response ratio, which represented the average effect size of each scenario on CPOM standing stocks. A three-way ANOVA was used to test the main effects of the three model drivers across levels of disturbance severity, and their two-way and three-way interaction terms, on the hydrograph-specific response ratio of CPOM. This response ratio was analysed as the dependent variable in ANOVA (rather than daily CPOM standing stocks) to isolate the confounding effects of interannual variations in discharge. The effects of two-way interactions of model drivers on CPOM standing stocks at each level of disturbance severity was described, including synergism (cumulative effects of multiple drivers being greater than the additive sum of the effects of drivers acting in insolation) and antagonism (cumulative effects being less than the additive effects). These interactions were then classified into various directional types (i.e., additive, positive/negative synergistic, positive/ negative antagonistic), according to the conceptual framework by Piggott et al. (2015). Directional interaction types were determined based on the magnitude and response direction of daily CPOM standing stocks, and the interaction effect. For each scenario, hydrograph-specific daily CPOM standing stocks predicted by a simple additive null model were calculated according to Schäfer and Piggott (2018). The 3

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Fig. 1. Flow chart of process-based modelling of CPOM standing stocks in East Creek, British Columbia, under forest harvesting impacts, using the stream CPOM model by Stenroth et al. (2014). Graphical illustrations of the manipulation of (a) litterfall (L), (b) peak flows (P), and (c) stream temperature (T) across five severity levels in the modelling are given for the first 365 time steps (i.e., one year), and colour-coded by severity level. Daily aerial inputs of leaf litter averaged across three streams near East Creek are shown in (a). Daily discharge values that were identified as peak flows in East Creek during 1998 are shown in (b), and are in log scale. Non-peak flows discharge values were not manipulated in the modelling, and are thus omitted for clarity. Smoothed daily mean stream temperatures of an average year in East Creek are shown in (c) (see Fig. 2a in Stenroth et al., 2014). Note that only stream temperature from June to September was manipulated.

scenario, using a Wilcoxon signed-rank test across ten simulations. At a given level of disturbance severity, statistically significant results of this test indicate that the directional interaction type classified for each paired driver interaction is significantly non-additive. Data were ln(x +1)-transformed as appropriate to meet assumptions of normality. All data analyses were carried out using R 3.5.1 (R Development Core Team, 2018).

additive null model assumes that the interaction effects of two drivers are absent, and therefore predicts the cumulative effects of two drivers based on the effects of individual drivers (Schäfer and Piggott, 2018). The difference in hydrograph-specific daily CPOM standing stocks between the values simulated under a given scenario and the undisturbed scenario was compared with the difference between the values predicted using the additive null model and the undisturbed 4

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Fig. 2. Simulated daily CPOM standing stocks (g ashfree dry mass [AFDM] m−2) in East Creek, British Columbia, from model runs under undisturbed conditions and with logging-associated changes of high severity in (a) single model drivers, including litterfall (L), peak flows (P), and stream temperature (T), and (b) multiple model drivers (L + P, P + T, L + T, L + P + T). Model outputs of 730 time steps (i.e., across two years) averaged across an ensemble of ten simulations are shown, and results from the first 90 days of each model run (spin-up period) are excluded to allow for the model to stabilise. Refer to Table 1 for changes in individual model drivers for each scenario.

3. Results

East Creek for the process-based modelling of CPOM, we referred to the quartiles and maximum values of the response distributions of stream temperature (1–6 °C increases), and peak flows (20–300% increases; considered across groups of percentage watershed area harvested). Representative values of litterfall reductions (10–90% reductions) reported across studies were selected to form the gradient of disturbance severity (Table 1).

3.1. Published post-harvest responses of model drivers Most compiled data of logging-associated changes in peak flows and stream summer temperature in low-elevation watersheds in North America were collected from the Pacific Northwest region, particularly MKRF in British Columbia, Canada, and H.J. Andrews Experimental Forest, Alsea and Trask Watersheds in Oregon, USA (supplementary material Appendix C Tables C.1 and C.2). Studies of these changes in low-elevation forested watersheds were lacking in some North American regions (e.g., upper Midwest, southern Coastal Plain in USA). Post-harvest litterfall changes were measured in a relatively small number of studies, which reported greater litterfall reductions in streams without riparian buffers than with buffers (supplementary material Appendix C Table C.3). The range of litterfall responses within 5 years after forest harvesting was from -98% to 44%. Post-harvest changes in peak flows ranged from -20% to 300%, and the magnitude of peak flow increases tended to be greater for sites with 41–80% of watershed area harvested than for sites with less than 40% of watershed area harvested (supplementary material Appendix D Table D.1). There were variations in the designation of summer months by studies of postharvest changes in stream temperature, but they typically included the period from June to August and/or September, thus matching the period of temperature perturbation in the heuristic modelling. The range of responses of stream summer temperature to forest clear-cutting without riparian buffers was from -1.6 °C to 6.0 °C (supplementary material Appendix D Table D.1). To assign the levels of disturbance severity of forest harvesting at

3.2. Sensitivity analysis and heuristic modelling of CPOM Modelled average daily CPOM standing stocks at East Creek decreased under the independent effects of reduced litterfall and elevated peak flows, and increased with higher stream temperature (Table 2). The magnitude of these effects increased considerably with the severity of forest harvesting disturbance (Table 2 and Fig. 2). Along this severity gradient, the effects of litterfall reductions on depleting CPOM standing stocks were at least an order of magnitude greater than those of elevated peak flows. For example, litterfall reductions at low severity led Table 1 Changes in litterfall, peak flows, and stream temperature assigned to each level of severity of forest harvesting disturbance for the heuristic modelling of CPOM standing stocks in East Creek, British Columbia.

5

Severity

Litterfall

Peak flows

Stream temperature (June to September)

Low Moderate High Very high

−10% −30% −50% −90%

+20% +40% +100% +300%

+1 °C +2 °C +4 °C +6 °C

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Table 2 Difference in average daily CPOM standing stocks (g AFDM m−2) between the values simulated using the heuristic model of forest harvesting disturbance and the undisturbed scenario in East Creek, British Columbia. For each pair of model drivers, individual effects of drivers (litterfall, L; peak flows, P; stream temperature, T) and the cumulative effects of drivers were modelled. Positive values indicate that daily CPOM standing stocks in the given scenario are higher than in the undisturbed scenario, and vice versa. Percent change in daily CPOM standing stocks under the individual effects of drivers are shown. For each scenario with cumulative effects, the difference in daily CPOM standing stocks between values expected from the additive null model and the undisturbed scenario are given in parentheses. Also shown are the directional interaction types of paired drivers (+A: positive antagonistic, -A: negative antagonistic; sensu Piggott et al. (2015)), and the results of Wilcoxon’s signed-rank tests indicating whether these directional interaction types were significantly non-additive. P-values < 0.05 are in bold font. Refer to Table 1 for changes in model drivers associated with each category of disturbance severity. Model drivers

Individual effects L P T Cumulative effects L+P L+T P+T

Severity of disturbance Low

% change

Moderate

% change

High

% change

Very high

% change

−1.06 −0.05 0.09

−13.50 −0.69 1.10 Interaction type -A; p = 0.08 -A; p = 0.43 +A; p = 0.06

−2.92 −0.10 0.46

−37.34 −1.32 5.91 Interaction type -A; p < 0.01 +A; p = 0.56 +A; p = 0.02

−4.41 −0.20 2.27

−56.35 −2.60 29.00 Interaction type -A; p < 0.01 +A; p < 0.01 +A; p = 0.02

−7.06 −0.37 5.64

−90.24 −4.69 72.07 Interaction type -A; p < 0.01 +A; p < 0.01 +A; p < 0.01

−1.11 (-1.11) −0.96 (-0.97) 0.03 (0.03)

−2.97 (-3.02) −2.49 (-2.46) 0.35 (0.36)

−4.46 (-4.61) −3.18 (-2.14) 2.01 (2.07)

−7.09 (-7.43) −6.27 (-1.42) 1.84 (5.27)

Note: Simulated average daily CPOM standing stocks under undisturbed conditions are 7.82 g AFDM m−2.

to greater CPOM decreases on average (-14%) than did peak flow increases at very high severity of disturbance (-5%; Table 2). The magnitude of CPOM changes induced by litterfall reductions was consistently greater than stream temperature increases, but their differences in magnitude became smaller at higher levels of disturbance severity (Table 2). Warming-induced decreases in shredder biomass and consumption rates led to increases in CPOM standing stocks (supplementary material Appendix B Fig. B.1). The associated alterations in the temporal dynamics of modelled CPOM standing stocks depended on the identity of model driver(s) affected, and the level of disturbance severity. With litterfall reductions and peak flow increases, CPOM standing stocks were depleted faster in winter (from November to January) to reach near-zero levels than in undisturbed conditions (Fig. 2). CPOM accumulation was slower in late summer (especially from August to September) with reduced litterfall in general, and faster with increased temperatures only at high or very high levels of disturbance severity. With simultaneous increases in peak flows and temperature, CPOM standing stocks tended to decline more slowly during winter, in comparison with undisturbed conditions (see Fig. 2). For all other scenarios involving changes in multiple drivers, the accumulation of CPOM standing stocks in late summer was more delayed, and their depletion in winter was faster than in undisturbed conditions (Fig. 2). For scenarios involving perturbations of multiple model drivers, the effect size of disturbance was significantly negative (indicating significantly lower CPOM standing stocks than in undisturbed conditions) whenever litterfall reductions reached 50% or above (i.e., high severity; Fig. 3). When litterfall reductions were 30% or below, the effect size of disturbance varied with the relative changes in peak flows and stream temperature (Fig. 3). For instance, a 4 or 6 °C-increase in stream temperature could reverse decreases in CPOM standing stocks caused by litterfall reductions and peak flow increases (Fig. 3).

were additive (p = 0.56; Table 2). The considerations involved in determining the directional interaction types for these interactions are given in the following two representative scenarios under high severity of disturbance (shown in Table 2). First, for litterfall-peak flows interactions, the individual effects of changing litterfall and peak flows on CPOM standing stocks were both negative, and their sum of effects expected from the additive null model would lead to an average reduction of 4.61 g AFDM m−2 relative to the undisturbed conditions. Their cumulative effects, on average, resulted in a reduction of 4.46 g AFDM m−2 relative to the undisturbed conditions (Table 2), which were less negative than predicted from the additive null model, and thus their interactions were classified as negative antagonistic. Secondly, for litterfall-temperature interactions, the individual effects of changing litterfall and stream temperature had opposing directions (which was also the case for peak flows-temperature interactions). The cumulative effects of litterfall and temperature (-3.18 g AFDM m−2 relative to the undisturbed conditions) were more negative than the expected sum of effects (-2.14 g AFDM m−2), and thus determined as positive antagonistic. For their cumulative effects to be classified as synergistic, they would have to increase CPOM standing stocks by more than 2.27 g AFDM m−2 on average (i.e., exceeding the individual effects of temperature; classified as positive synergistic), or reduce them by more than 4.41 g AFDM m−2 (i.e., exceeding the individual effects of litterfall; classified as negative synergistic).

4. Discussion Our model-based analysis revealed that litterfall rate was more influential than stream summer temperature and peak flows as biophysical controls on the standing stocks of leaf litter-derived CPOM in a small forested stream, across a broad range of severity of forest harvesting disturbances. Post-harvest litterfall reductions were simulated to cause greater decreases in CPOM standing stocks than did elevated peak flows. Stream warming enhanced CPOM standing stocks, and could counteract the negative effects of litterfall and peak flows at lower severity levels of forest harvesting disturbance. In the heuristic modelling of forest harvesting scenarios, the interplay of model drivers yielded diverse CPOM responses, except at high or very high severity of litterfall reductions (i.e., ≥50%) under which CPOM reductions were more probable. Our modelling results showed that non-additive, antagonistic interactions between paired model drivers could emerge at higher levels of disturbance severity. It is therefore necessary to limit changes in multiple drivers of concern to minimise forest harvesting impacts on CPOM standing stocks.

3.3. Interactive effects of model drivers on CPOM Only the effects of litterfall-temperature interactions on CPOM standing stocks were significant (F4,1116 = 4.79; p < 0.001), whereas the effects of all other two-way and three-way interactions were nonsignificant overall (Table 3). The cumulative effects of paired model drivers were additive at low disturbance severity (Table 2). From moderate to very high levels of disturbance severity, paired driver interactions were significantly non-additive, and either positive or negative antagonistic (Wilcoxon signed-rank tests: p < 0.05). The only exception was the effects of litterfall-temperature interactions which 6

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Fig. 3. Effect size of logging-associated changes in litterfall, peak flows (P), and stream temperature (T) on average daily CPOM standing stocks (ln[response ratio of CPOM]) in East Creek, British Columbia, simulated by full-factorial heuristic modelling of forest harvesting impacts. Effect size for each scenario averaged across ten model simulations, and its associated 95% confidence interval (represented by error ranges which are in most cases very small), are shown. Effect sizes with corresponding error ranges overlapping zero (the solid line) are not significant. Panels are arranged by increasing severity of logging-associated changes in litterfall: (a) undisturbed conditions (i.e., “Litterfall0%”, “P + 0%”, and “T+0 °C”), (b) low, (c) moderate, (d) high, and (e) very high severity. Note that the scale of y-axis in (e) is different from other panels.

peak flow increases corresponding to the higher levels of disturbance severity that we specified, even when the entire watershed is clear-cut (see Storck et al., 1998; Lewis et al., 2001; Abdelnour et al., 2011). Therefore, it is important to take into account the geographic context of these streams, where the level of disturbance severity may not be linearly related to the response of model drivers. Nevertheless, we expected to have determined broad ranges of post-harvest responses of model drivers for the purpose of the sensitivity analysis and heuristic modelling, and the published responses we compiled could be a useful reference for additional modelling studies that simulate CPOM (or OM) responses to disturbances in other small streams.

Table 3 Results of three-way ANOVA testing the modelled main effects and interactions of litterfall (L), peak flows (P), and stream temperature (T) affected by forest harvesting on ln(hydrograph-specific response ratio of CPOM) in East Creek, British Columbia. P-values < 0.05 are in bold font.

L P T L×P L×T P×T L×P×T Error

d.f.

Mean square

F

p

4 4 4 16 16 16 63 1116

220.19 0.09 13.82 0.00 0.03 0.00 0.00 0.01

31450.61 12.62 1974.07 0.09 4.79 0.03 0.002

< 0.001 < 0.001 < 0.001 1.000 < 0.001 1.000 1.000

4.2. Effects of individual drivers on CPOM Results of the sensitivity analysis indicated that litterfall was a more influential model driver of CPOM standing stocks at East Creek than stream temperature and peak flows, under variable impacts of forest harvesting. The extent of modelled reduction of riparian litterfall – as the source of leaf litter-derived CPOM – approximated the extent of depletion of CPOM standing stocks. This occurred along the severity gradient of forest harvesting disturbance, not only with near-complete exclusion of litter inputs (see Eggert et al., 2012). It is probable that when litterfall reductions exceeded 50% (i.e., similar to the open-canopy stream conditions in Stenroth et al., 2014), shredders were subject to intense resource limitation which further supressed their biomass and CPOM consumption (e.g., Baer et al., 2001; Melody and Richardson, 2004; Zhang and Richardson, 2011). Litterfall reductions during early spring could also advance the onset of the period of extremely low availability of CPOM, potentially limiting the growth and productivity of many summer-growing shredder taxa (Richardson,

4.1. Post-harvest response ranges of model drivers The published post-harvest response ranges of model drivers in North American temperate streams reflected the effects of diverse forest harvesting practices and their spatial extent in individual watersheds, as well as the environmental context of study sites (e.g., elevation, bankfull width, aspect). The ranges of post-harvest changes in litterfall and stream summer temperature generally encompass those reported for small streams in the region of MKRF (litterfall: -2 to -91%, temperature: 0.4–3.9 °C; supplementary material Appendix D Table D.1). Responses of peak flows in a MKRF stream to forest clear-cutting were the lowest of the published range and negative (-22%; Cheng et al., 1975), although variable extent of post-harvest increases (up to 233%) in peak flows have been observed in the Pacific Northwest. It is possible that small, forested streams similar to East Creek would not experience 7

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2001). Logging-associated increases in peak flows could enhance the reentrainment of litter, particularly during winter months with higher precipitation and discharge, and promote the fragmentation of CPOM to FPOM (see supplementary material Appendix C Table C.1; Kiffney and Richardson, 2010). Modelled effects of increasing peak flows on CPOM standing stocks in East Creek were measurable but weak, and were comparable to the effects of the interannual variations in discharge regime. The discharge threshold beyond which bed sediments are mobilised (Qcritical) was considered to approximate 250 L s−1, based on empirical measurements in an experimental stream channel near East Creek (T.M. Hoover, unpublished data). Depending on antecedent flow conditions, when Qcritical is crossed, the probability of litter reentrainment would go up from ˜5-26% (T.M. Hoover, unpublished data) to 95% (Stenroth et al., 2014). Under undisturbed conditions, Qcritical is crossed 1–4 times each year, normally from November to January, and there were numerous high-flow episodes with daily discharge reaching 160-250 L s−1 during this period (supplementary material Appendix A Fig. A.1). Additional events of Qcritical exceedances due to peak flow increases tended to occur at moderate to very high disturbance severity, which led to further CPOM reductions compared with undisturbed conditions. Such further CPOM reductions usually lasted for several days to about one month during winter, compared to the effects of reduced litterfall, which occurred all year round. It is important to recognise that the effects of elevated peak flows on CPOM standing stocks could be greater in certain years (or streams) than modelled in this study, if discharge levels are more frequently near Qcritical in undisturbed conditions (e.g., Papangelakis and Hassan, 2016). Logging-associated summer warming effects on CPOM standing stocks were more apparent at higher levels of disturbance severity, and depended on the thermal regime of the study stream, and trade-offs between changes in shredder feeding and microbial processing of CPOM at higher temperatures. Under undisturbed conditions, the average smoothed stream temperature from June to September at East Creek was about 11.3 °C (see Fig. 2a in Stenroth et al., 2014). It was below the optimum temperature for leaf litter consumption by shredders (consumer Topt), which was parameterised at 15 °C based on a study of one shredder caddisfly species, Sericostoma vittatum (González and Graça, 2003). At higher levels of disturbance severity (e.g., 4–6 °C temperature increases), consumer Topt were exceeded for longer periods of time (Fig. 4), particularly from August to September. This caused greater declines in shredder consumption and biomass, which lasted till fall (supplementary material Appendix B Fig. B.1; see also Stenroth et al., 2014). Under warmer conditions, modelled CPOM gains due to lower shredder consumption far exceeded CPOM losses through accelerated microbial processing (supplementary material Appendix B Table B.1). This is due to a much lower modelled contribution of microbial processing (˜1%) to CPOM losses than that of shredder consumption (˜20-40%) in this stream (see also Stenroth et al., 2014). However, such positive responses of CPOM may not occur in streams where microbial processing contributes to CPOM losses similarly to or more than shredder consumption (see Buzby and Perry, 2000; Yeung et al., 2018). CPOM responses to temperature increases are evidently also contingent on consumer Topt used for parameterising the model. Laboratory feeding trials established consumer Topt to approximate 15 °C for many temperate shredder taxa, particularly caddisflies (e.g., González and Graça, 2003; Rumbos et al., 2010; Batista et al., 2012; Correa-Araneda et al., 2015) which could vary with taxonomic identity and litter quality. In cool streams in Nova Scotia, Canada, Leuctra stoneflies – a numerically dominant shredder (other leuctrid species are also common in East Creek) – were shown to attain the highest density (Taylor and Andrushchenko, 2014) and probably highest total consumption at 14 °C. Indeed, in our study region, the temperature-dependence for litter consumption is still unknown for the numerically dominant and large-bodied taxa, such as Lepidostoma caddisflies and Pteronarcys

Fig. 4. Relationships between the temperature-dependent function for leaf litter consumption by model shredder species and stream temperature. These relationships are part of the CPOM model by Stenroth et al. (2014), and were established according to consumption model 2 for warm-water species (Kitchell et al., 1977) in the Fish Bioenergetics 4.0 model (Deslauriers et al., 2017). Shaded area denotes the range of smoothed daily stream temperature from June 1 st to September 30th at East Creek, British Columbia. The solid line denotes the average daily stream temperature (11.3 °C) of this period, which is below the optimum temperature for litter consumption by shredders (15 °C) used to parameterise the stream CPOM model.

stoneflies (see Ruesink and Srivastava, 2001; Lecerf and Richardson, 2011; Yeung et al., 2018). It is likely that consumer Topt is taxon-specific, and below or above 15 °C. In higher-elevation streams, stream temperature under forest harvesting disturbances may still be much below consumer Topt. This would lead to a lesser extent of net gains of CPOM than what we modelled, and would render temperature a less influential model driver in forest harvesting scenarios. There are other sources of uncertainty associated with warming effects on shredder consumption, for instance, through compositional shifts in litter-associated shredder and microbial communities (Fernandes et al., 2012; Moghadam and Zimmer, 2014; Domingos et al., 2015), which were not incorporated into the model. Despite this uncertainty, our modelling results suggested a potential mechanism by which stream warming could enhance CPOM standing stocks via suppression of shredder consumption, when stream summer temperature is already close to, or above consumer Topt. There is a clear need to empirically evaluate the generality of this mechanism (e.g., using mesocosms) for individual shredder taxa and entire communities across a temperature gradient. Our modelled effects of litterfall and peak flow changes are generally consistent with our hypotheses and the results of modelling studies in other temperate streams. CPOM decreases occurred with simulated litterfall reductions (by 10%) in central Appalachian streams in Virginia, USA (Buzby and Perry, 2000), and during periods of lower litter inputs in a northern Spain stream (Pozo et al., 1997). Peak flow increases (by 10%) had limited effects on CPOM standing stocks (Buzby and Perry, 2000). Simulated warming (by 2 °C) led to reduced CPOM, as the consequence of increased shredder consumption and microbial processing (Buzby and Perry, 2000), which differed from the warming effects modelled for East Creek. This difference is probably due to the modelling assumption of linear increases in shredder consumption rates with temperature, without considering consumer Topt, in Buzby and Perry (2000). We therefore suggest to empirically determine the temperature-dependence of shredder consumption, in order to better understand the influences of stream temperature relative to other model drivers on CPOM standing stocks in any given stream.

4.3. Effects of multiple drivers on CPOM The response pattern of stream CPOM to multiple model drivers 8

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to the influences of leaf litter types (broad leaves vs. conifer needles; Hoover et al., 2010; Marcarelli et al., 2011), and other co-occurring CPOM-related processes, which would have made the model too analytically complex. For instance, post-harvest stream narrowing and loss of large wood can lead to reduced channel roughness (Sweeney et al., 2004), which in turn enhances the probability of CPOM re-entrainment during high-flow events. Forest harvesting-induced changes in stream nutrient concentrations, sediment loads (Feller and Kimmins, 1984; Sweeney and Newbold, 2014), and light availability (Kiffney et al., 2004; Kaylor et al., 2016) can influence shredder consumption and microbial processing (e.g., Gulis and Suberkropp, 2003; Cross et al., 2005; Lagrue et al., 2011; Danger et al., 2012; Connolly and Pearson, 2013). These processes can interact with the model drivers (e.g., temperature) to affect CPOM responses (e.g., Ferreira and Chauvet, 2011; Piggott et al., 2012; Fernandes et al., 2014).

highlights the complexity of biophysical controls on CPOM standing stocks, which gives rise to context-dependent effects of forest harvesting disturbance. Modifying three model drivers already yielded countervailing effects (i.e., increase, decrease, or no change) on and tremendous variability in CPOM standing stocks. In addition, interaction effects between pairs of model drivers varied between additive or antagonistic, depending on the severity of forest harvesting disturbance. This illustrated the difficulty of predicting the cumulative effects of multiple drivers from knowledge of single driver effects. We showed that antagonistic interactions were the dominant type of non-additive interactions examined, which are also frequently observed in multiple stressor studies in freshwater ecosystems (Piggott et al., 2015; Jackson et al., 2015). These interactions arose due to the emergent properties of the biophysical processes modelled, and not relevant factors excluded from the CPOM model, such as litter quality (Fernandes et al., 2012; Foucreau et al., 2016), the activity and community composition of shredders, and their interspecific interactions (Tiegs et al., 2008; Ferreira et al., 2014). Taking the scenario with high severity of litterfall reductions and peak flow increases (especially in fall) as an example, a much-reduced quantity of CPOM by one driver would allow a smaller magnitude (in absolute amount) of further decrease by another driver, since reductions were made on a proportional basis. Hence, cumulative CPOM reduction by the two drivers combined would be less than the additive sum of the negative effects of these drivers acting in isolation, thus giving rise to antagonistic interactions (see also interactions between warming and storm events on CPOM in Buzby and Perry, 2000).

4.5. Implications for forest and watershed management This study mainly aimed at identifying the relative importance of biophysical controls on CPOM flows and transport in small, forested streams similar to East Creek in coastal British Columbia, Canada. Our modelling results are relevant not only to forest and watershed management associated with forestry activities, but also with other anthropogenic impacts (e.g., agriculture, urbanization) and climate change in this region that can alter organic matter inputs, discharge, and stream temperature. Establishing riparian buffer zones can most likely mitigate post-harvest changes in CPOM standing stocks, through maintaining litter inputs and stream summer temperature (see also Karlsson et al., 2005; Kiffney and Richardson, 2010). In doing so multiple attributes related to water quality, stream-riparian habitats, and biota therein can also be protected (Dosskey et al., 2010; Sweeney and Newbold, 2014). Under selective harvesting, limiting basal area harvested (e.g., to below 50%), and retaining larger trees within a few meters of the stream channel may also minimise impacts on litter inputs in some streams (Kreutzweiser et al., 2004; Muto et al., 2009). In contrast, reach- and catchment-scale management measures that reduce the extent of peak flow increases are expected to have minimal effects on enhancing stream CPOM retention on an annual basis. Nevertheless, limiting peak flow increases (and debris flows) may be necessary for achieving other management objectives, such as minimising post-harvest changes in stream geomorphology, sediment loads, and biotic integrity (Moore and Wondzell, 2005; Reid et al., 2010; Hawley et al., 2016). For instance, this can be attained by reducing proportion of watershed area harvested to below 40%, and positioning harvested areas and logging roads further away from streams (Rogger et al., 2017). Our exploratory modelling reveals antagonistic interactions between reach- to catchment-scale biophysical processes on an ecological variable at higher disturbance severity, which adds to the limited work showing non-additive cumulative impacts of environmental stressors in small streams (see Buzby and Perry, 2000; Harvey and Railsback, 2011). These results reiterate the need for management actions to limit changes in multiple stressors, especially reductions in litter inputs and seasonal increases in stream temperature (e.g., by setting up riparian buffers), in order to minimise disturbance effects on stream food webs (Jackson et al., 2015). It is typically challenging to quantify the impacts of individual biophysical processes operating at reach to catchment scales (e.g., discharge, substrate composition) on the structural and functional attributes of streams. The present study and others show that processbased model simulations and scenario analysis can provide a mechanistic approach to increase our understanding of the impacts and drivers of these large-scale processes (e.g., Futter et al., 2007; Katsuyama et al., 2009; Harvey and Railsback, 2011; Bussi et al., 2018; Groom et al., 2018). We recommend wider adoption of this approach to forecast stream ecological responses to forest harvesting and other watershed

4.4. Model limitations Our modelling results are more applicable to stream reaches similar to East Creek where in-stream wood is spatially patchy, which has minor contributions to leaf litter retention. In forested streams where large wood is a major type of CPOM retentive structure, its presence would likely reduce the modelled effects of elevated peak flows on CPOM standing stocks (Ehrman and Lamberti, 1992; Small et al., 2008). Moreover, our findings have limited applicability in streams where harvesting practices promote large wood inputs and influence litter retention, subsequent to downslope transport and accumulation of slash, and/or frequent windthrow (Haggerty et al., 2004; Burrows et al., 2012). Nevertheless, we expect the differences between water temperature and consumer Topt, and number of Qcritical exceedances to be important drivers of shredder and discharge influences on CPOM standing stocks, respectively, across many streams systems. Recent studies have found that stream insects (including shredders) are more flexible in their use of detrital and algal resources than previously recognised (Collins et al., 2016; Larson et al., 2018). Our study did not model how differential responses in resource use due to temperature and peak flow changes by insects other than shredders could also influence CPOM standing stocks. We assumed constant alterations in the values of model drivers, and their uniformity over the entire stream, throughout the duration of model runs. Forest harvesting is known to also alter the temporal variability and/or seasonality of litterfall (Kreutzweiser et al., 2004; Kiffney and Richardson, 2010), discharge (Moore and Wondzell, 2005), and stream temperature (Moore et al., 2005; Arismendi and Groom, 2019) within the first few years since harvest, which is also highly variable across and within sites. In particular, we did not model the effects of post-harvest increases in the frequency of large floods on CPOM reduction, which would be considerably greater than those of peak flow increases (see Buzby and Perry, 2000). Incorporating the effects of known changes in these components of model drivers into simulations is important for improving the accuracy of predicting sitespecific responses (and recovery) of CPOM standing stocks to forest harvesting, but it is beyond of the scope of the present study. We also did not quantify the uncertainty of model outcomes related 9

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disturbances, which can help prioritise management strategies to meet multiple ecological targets under limited budgets.

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