Models Of Ecological Processes Research Articles

Exploring resilience in the affect regulation of family violence-exposed adolescents

Objectives: The study explores the presence of the three components of Ungar’s (2019) biopsychosocial process definition of resilience in the context of family violence-exposed adolescents’ descriptions of affect regulation when experiencing high affect arousal. Methods: A convenience sample of 16 youth, age 15-25 with histories of family psychological, and/or physical violence exposure, completed semi-structured qualitative interviews describing affect regulation during arousal states in past stressful situations. Interviews were recorded and transcribed verbatim. Utilising deductive framework analysis, predefined thematic coding was conducted in NVivo. Results: Rich descriptions were generated of youth’s adaptive capacities to regulate affect while under stress. We explored the presence of the three components of Ungar’s (2019) resilience definition in the data: 1) Risk affect regulation during hyper-/hypo-arousal states, 2) Navigation of access to and negotiation for meaningful promotive and protective internal and external factors, and 3) Resilience outcomes of recovery, adaptation, and transformation. The framework analysis of Ungar’s (2019) resilience definition illuminated differential interactions between adolescents and access to resources in their environments. Despite some resource deficits, participants demonstrated adaptive resilience when regulating affect. Implications: Ungar’s (2019) process resilience definition highlights the interconnection between youth’s resource needs and the capacity of their environments to provide them to enhance resilience. Results suggest that interventions to increase resilience should incorporate the full biopsychosocial ecological process model with a focus on regulation capacity. The knowledge gained from youth perspectives of affect regulation processes is directly applicable to complex trauma-informed interventions to increase self-regulation and resilience while reducing behavioural reactivity for violence-exposed adolescents.

Managing interacting disturbances: Lessons from a case study in Australian forests

Abstract Ecosystems are shaped by a range of drivers including human and natural disturbances. They also may be subject to interactions between disturbances which can affect ecological processes, biodiversity and ecosystem condition; yet few ecosystems have been subject to multiple studies of the effects of interacting disturbances. This limits the understanding of ways to mitigate the impacts of interacting disturbances. Over the past 37 years, we have completed a range of studies of interacting effects in the Mountain Ash Eucalyptus regnans forests of south‐eastern Australia. Here we summarize evidence for interacting disturbances in this ecosystem. This includes evidence of linked or coupled disturbances (termed interaction chains; sensu Foster et al., Global Change Biology, 22, 2016, 1325) between logging and subsequent fire severity. We also describe effects of other interacting disturbances such as those resulting from post‐fire (salvage) logging as well as landscape‐level, spatio‐temporal changes in forest cover associated with logging and wildfires. Policy implications. Insights from research in Mountain Ash forests provide broader lessons for managing interacting disturbances in forest ecosystems. These include the importance of cataloguing and mapping multiple disturbances in both space and time and developing conceptual models of ecosystem dynamics and ecological processes. Where there is a high risk of interactions between disturbances, appropriate management actions could include: (a) eliminating some drivers or re‐assessing levels of human extraction of resources, (b) reducing the spatial and/or temporal overlap of drivers and (c) identifying leverage points where management actions are most likely to be effective.

Habitat maps to enhance monitoring and management of the Great Barrier Reef

The Great Barrier Reef (GBR) is of immense biological, cultural and economic importance, but has also rapidly degraded over the last 30 years. Improved spatial information on reef geomorphic zonation and benthic cover type (including coral type) is critical to support scientific work to understand how the GBR is changing, and to support resource management decisions that enable conservation of the reef and its essential ecosystem services. Yet, no comprehensive maps exist that detail the geomorphic zonation or benthic cover for the GBR’s ~ 3000 reefs. This study presents three new types of shallow reef maps for 237 reefs in the central Cairns Management Region of the GBR Marine Park (GBRMP), explores how the detailed habitat maps created compared to current maps and posits how the new maps may support and refine current critical key science outputs and management challenges. Geomorphic Zonation, Benthic Cover and Coral Type habitat maps were created using a unique combined object-based image analysis and ecological modelling approach that incorporated satellite imagery, limited field data and key reef physical attributes (depth, slope, waves) using a previously peer-reviewed mapping approach developed for the Capricorn Bunker Group reefs, Southern GBR. The mapping approach was consistent and repeatable, suggesting applicability to mapping all 3000 reefs in the GBRMP. Compared to existing maps that only outline each reef, the increase in detail provided by these new habitat maps enabled discrete characterisation of each reef’s geomorphology and benthic composition. With the new habitat maps, areas within each reef can be identified as either coral habitat or not coral habitat. This has not been possible previously. As such, a model of coral ecological and biophysical processes that depends on bottom reflectance of sand and coral areas can be fine-tuned. Similarly, for reef restoration, nursery structures are commonly placed in non-coral habitats, and/or coral larvae are dispersed in areas of known coral habitat. The new habitat maps presented permit more accurate identification of these areas such that restoration projects can be targeted more effectively. These findings confirm the need to now apply this mapping approach to the full extent of the GBR.

Freshwater pearl mussels from northern Sweden serve as long-term, high-resolution stream water isotope recorders

Abstract. The stable isotope composition of lacustrine sediments is routinely used to infer Late Holocene changes in precipitation over Scandinavia and, ultimately, atmospheric circulation dynamics in the North Atlantic realm. However, such archives only provide a low temporal resolution (ca. 15 years), precluding the ability to identify changes on inter-annual and quasi-decadal timescales. Here, we present a new, high-resolution reconstruction using shells of freshwater pearl mussels, Margaritifera margaritifera, from three streams in northern Sweden. We present seasonally to annually resolved, calendar-aligned stable oxygen and carbon isotope data from 10 specimens, covering the time interval from 1819 to 1998. The bivalves studied formed their shells near equilibrium with the oxygen isotope signature of ambient water and, thus, reflect hydrological processes in the catchment as well as changes, albeit damped, in the isotope signature of local atmospheric precipitation. The shell oxygen isotopes were significantly correlated with the North Atlantic Oscillation index (up to 56 % explained variability), suggesting that the moisture that winter precipitation formed from originated predominantly in the North Atlantic during NAO+ years but in the Arctic during NAO− years. The isotope signature of winter precipitation was attenuated in the stream water, and this damping effect was eventually recorded by the shells. Shell stable carbon isotope values did not show consistent ontogenetic trends, but rather oscillated around an average that ranged from ca. −12.00 to −13.00 ‰ among the streams studied. Results of this study contribute to an improved understanding of climate dynamics in Scandinavia and the North Atlantic sector and can help to constrain eco-hydrological changes in riverine ecosystems. Moreover, long isotope records of precipitation and streamflow are pivotal to improve our understanding and modeling of hydrological, ecological, biogeochemical and atmospheric processes. Our new approach offers a much higher temporal resolution and superior dating control than data from existing archives.

Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation

Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red–green–blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.

Simulation of Spatiotemporal Land Use Changes for Integrated Model of Socioeconomic and Ecological Processes in China

Land use/land cover changes (LULCC) have been affected by ecological processes as well as socioeconomic and human activities, resulting in several environmental problems. The study of the human–environment system combined with land use/land cover dynamics has received considerable attention in recent decades. We aimed to provide an integrated model that couples land use, socioeconomic influences, and ecosystem processes to explore the future dynamics of land use under two scenarios in China. Under Scenario A, the yield of grain continues to increase, and under Scenario B, the yield of grain remains constant. This study created a LULCC model by integrating a simple global socioeconomic model, a Terrestrial ecosystem simulator (TESim), and a land use allocation model. The results were analyzed by comparing spatiotemporal differences under predicted land use conditions in the two alternative scenarios. The simulation results showed patterns that varied between the two scenarios. In Scenario A, grassland will expand in the future and a large reduction in cropland will be observed. In Scenario B, the augmented expansion of cropland and a drastic shrinkage of forest area will be the main land use conversion features. Scenario A is more promising because more land is preserved for ecological restoration and urbanization, which is in line with China’s Grain for Green Program. Economic development should be based on ecological protection. The results are expected to add insight to sustainable land use development and regional natural resource management in China.

Comparative Assessment of Vegetation Dynamics under the Influence of Climate Change and Human Activities in Five Ecologically Vulnerable Regions of China from 2000 to 2015

Ongoing climate change and human activities have a great effect on vegetation dynamics. Understanding the impact of climate change and human activities on vegetation dynamics in different ecologically vulnerable regions has great significance in ecosystem management. In this study, the predicted NPP (Net Primary Productivity) and the actual NPP based on different ecological process data and models were combined to estimate the vegetation dynamics and their driving forces in the Northern Wind-sand, Loess Plateau, Arid Desert, Tibetan Plateau, and Karst regions from 2000 to 2015. The results indicated that the NPP in all ecologically vulnerable regions showed a restoration trend, except for that in the Karst region, and the percentage of areas in which NPP increased were, in order, 78% for the Loess Plateau, 71% for the Northern Wind-sand, 69% for the Arid Desert, 54% for the Tibetan Plateau, and 31% for the Karst regions. Vegetation restorations in the Northern Wind-sand and Arid Desert regions were primarily attributable to human activities (86% and 61% of the restoration area, respectively), indicating the success of ecological restoration programs. The Loess Plateau had the largest proportion of vegetation restoration area (44%), which was driven by combined effects of climate and human factors. In the Tibetan Plateau, the vegetation changes due to climate factors were primarily distributed in the west, while those due to human factors were primarily distributed in the east. Human activities caused nearly 60% of the vegetation degradation in the Karst region. Based on these results, it is recognizable that regional climate conditions are the key factor that limits ecological restoration. Therefore, future policy-making should pay more attention to the local characteristics of different ecological vulnerable regions in regional ecosystem management to select reasonable restoration measures, improve restoration efficiency, and maximize the benefits of ecological restoration programs.

Hard-substrate habitats in the German Bight (South-Eastern North Sea) observed using drift videos

Comprehensive empirical data to inform benthic species distribution models for marine hard-substrate-dominated environments, are pivotal. However, such data are difficult to obtain. These data are crucial to the definition and demarcation of protected areas and for assessment of the ecological status and function of hard-substrate habitats.In this study, underwater video-observations of hard-substrate habitats within four target areas in the sand-dominated German Bight (SE North Sea) were investigated to obtain comprehensive information on hard-substrate distribution patterns, on the amount and sizes of stones and on the presence of sessile organisms.Based on three size classes (cobbles, boulders, large boulders) three hard-substrate distribution classes were identified: (1) widely scattered stones, (2) accumulations of stones and (3) dense stone fields. The ratios between cobbles, boulders and large boulders differed significantly between the investigated areas. Boulders and large boulders were largely colonized by sessile organisms, whereas cobbles in coastal areas were least frequently colonized. Physical disturbances of epibenthos resulting from abrasion and coverage by mobile sediments are discussed as a possible explanation for the proportional differences in the colonization of stones. Hard substrates in shallower, coastal areas appeared to be strongly influenced by sand abrasion because of higher current velocities and storm-induced waves. In deeper areas, located further offshore, disturbances caused by migrating sandy ripples mobilized by storm-events seemed to be more relevant.Habitat modelling of hard substrates and resultant ecological studies require sound information on the probability of epifaunal colonization for different substrate sizes, hard-substrate distribution patterns combined with hydrodynamic and physicochemical properties of the marine environment to produce valid results. We used a structured approach for the video-based analysis of hard-substrate habitats and present estimates of the colonization probability of differently-sized stones. Our study shows that the analysis of drift videos provides basic data at a suitable resolution to contribute to the monitoring and modelling of marine ecological processes.

Community Structure, Dynamics and Interactions of Bacteria, Archaea and Fungi in Subtropical Coastal Wetland Sediments

Bacteria, archaea and fungi play crucial roles in wetland biogeochemical processes. However, little is known about their community structure, dynamics and interactions in subtropical coastal wetlands. Here, we examined communities of the three kingdoms in mangrove and mudflat sediments of a subtropical coastal wetland using Ion Torrent amplicon sequencing and co-occurrence network analysis. Bacterial, archaeal and fungal communities comprised mainly of members from the phyla Proteobacteria and Bacteroidetes, Bathyarchaeota and Euryarchaeota, and Ascomycota, respectively. Species richness and Shannon diversity were highest in bacteria, followed by archaea and were lowest in fungi. Distinct spatiotemporal patterns were observed, with bacterial and fungal communities varying, to different extent, between wet and dry seasons and between mangrove and mudflat, and archaeal community remaining relatively stable between seasons and regions. Redundancy analysis revealed temperature as the major driver of the seasonal patterns of bacterial and fungal communities but also highlighted the importance of interkingdom biotic factors in shaping the community structure of all three kingdoms. Potential ecological interactions and putative keystone taxa were identified based on co-occurrence network analysis. These findings facilitate current understanding of the microbial ecology of subtropical coastal wetlands and provide a basis for better modelling of ecological processes in this important ecosystem.

Feedback enhances greening during disaster recovery: A model of social and ecological processes in neighborhood scale investment

In disaster recovery situations, including the post-Katrina recovery in New Orleans, greening behavior (i.e., the planting of trees, flowers and other plants) is thought to play an important role in social, ecological and economic recovery. In this paper, we use agent-based modeling to investigate and understand the roles of green attachment (which encompasses place attachment and biophilia), engagement in local ecological investment (i.e., greening), and social feedback (where individuals who observe greening become more likely to engage in it). We model this social-ecological feedback process, basing our parameters and assumptions on real-world data and grounding our model in the Tremé neighborhood of New Orleans. We find that social-ecological feedback enhances greening overall and leads to larger spatially continuous regions with high rates of greening. We also find that social-ecological feedback leads to tipping points in neighborhood greening, with approximately 30% of households needing to engage in greening for it to reach asymptotically high levels. In addition, when green attachment is high, this leads to more widespread greening behavior. We conclude that social-ecological feedback processes such as those modeled here may play important roles in neighborhood recovery after disasters such as Hurricane Katrina.

Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques

Understanding post-fire forest recovery is pivotal to the study of forest dynamics and global carbon cycle. Field-based studies indicated a convex response of forest recovery rate to burn severity at the individual tree level, related with fire-induced tree mortality; however, these findings were constrained in spatial/temporal extents, while not detectable by traditional optical remote sensing studies, largely attributing to the contaminated effect from understory recovery. Here, we examined whether the combined use of multi-sensor remote sensing techniques (i.e., 1 m simultaneous airborne imaging spectroscopy and LiDAR and 2 m satellite multi-spectral imagery) to separate canopy recovery from understory recovery would enable to quantify post-fire forest recovery rate spanning a large gradient in burn severity over large-scales. Our study was conducted in a mixed pine-oak forest in Long Island, NY, three years after a top-killing fire. Our studies remotely detected an initial increase and then decline of forest recovery rate to burn severity across the burned area, with a maximum canopy area-based recovery rate of 10% per year at moderate forest burn severity class. More intriguingly, such remotely detected convex relationships also held at species level, with pine trees being more resilient to high burn severity and having a higher maximum recovery rate (12% per year) than oak trees (4% per year). These results are one of the first quantitative evidences showing the effects of fire adaptive strategies on post-fire forest recovery, derived from relatively large spatial-temporal scales. Our study thus provides the methodological advance to link multi-sensor remote sensing techniques to monitor forest dynamics in a spatially explicit manner over large-scales, with important implications for fire-related forest management and constraining/benchmarking fire effect schemes in ecological process models.

Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation with the spatial heterogeneity under the three vegetation types. According to the temporal and spatial heterogeneity of the optimal values, the parameters of the BIOME-BGC model could be classified in order to adopt different parameter strategies in practical application. The conclusion could help to deeply understand the parameters and the optimal values of the ecological process models, and provide a way or reference for obtaining the reasonable values of parameters in models application.

Simulation-based modeling of wild blueberry pollination

The high variability of wild (lowbush) blueberry plants in spatial and genetic structure, in combination with bee foraging behavior varying between species, and the complexity of these factors interacting over time and space, are major obstacles to understanding of pollination dynamics subject to environmental change. The bottom-up modeling paradigm provides an ideal approach to bridging the gap between known mechanisms of individual organisms and unknown spatial–temporal dynamics of pollination at the field scale. By linking empirical data to stochastically-based ecological process modeling, we present a spatially-explicit agent-based simulation model that enables exploration of how various factors, including plant spatial arrangements, outcrossing and self-pollination, bee species compositions and weather conditions, in isolation and combination, affect pollination efficiency throughout a blueberry bloom season. The firmly validated open-source model is a useful tool for hypothesis testing and theory development for wild blueberry pollination researches. Sensitivity analysis suggested that fruit set and resulting measures of productivity such as fruit mass and viable seeds per fruit were sensitive to parameterization of blueberry genotype or clone size and the amount of blueberry plant cover in a field. Fruit set due to pollen compatibility was sensitive to ovule number per flower and foraging bee density. Simulation experiments allowed us to compare bee pollination efficiencies at the bee taxon population level (honey bees, bumble bees, and native solitary bees), the effect of foraging distance from bee nest or colony site on fruit set, and test whether the mechanism of gametophytic self-incompatibility (pre- vs. post-zygotic decision making by the plant) in wild blueberry pollination at the field level matters in estimating yield.

Modeling forest above-ground biomass dynamics using multi-source data and incorporated models: A case study over the qilian mountains

In this work, we present a strategy for obtaining forest above-ground biomass (AGB) dynamics at a fine spatial and temporal resolution. Our strategy rests on the assumption that combining estimates of both AGB and carbon fluxes results in a more accurate accounting for biomass than considering the terms separately, since the cumulative carbon flux should be consistent with AGB increments. Such a strategy was successfully applied to the Qilian Mountains, a cold arid region of northwest China.Based on Landsat Thematic Mapper 5 (TM) data and ASTER GDEM V2 products (GDEM), we first improved the efficiency of existing non-parametric methods for mapping regional forest AGB for 2009 by incorporating the Random Forest (RF) model with the k-Nearest Neighbor (k-NN). Validation using forest measurements from 159 plots and the leave-one-out (LOO) method indicated that the estimates were reasonable (R2=0.70 and RMSE=24.52tonesha−1). We then obtained one seasonal cycle (2011) of GPP (R2=0.88 and RMSE=5.02gCm−28d−1) using the MODIS MOD_17 GPP (MOD_17) model that was calibrated to Eddy Covariance (EC) flux tower data (2010). After that, we calibrated the ecological process model (Biome-BioGeochemical Cycles (Biome-BGC)) against above GPP estimates (for 2010) for 30 representative forest plots over an ecological gradient in order to simulate AGB changes over time. Biome-BGC outputs of GPP and net ecosystem exchange (NEE) were validated against EC data (R2=0.75 and RMSE=1. 27gCm−2d−1 for GPP, and R2=0.61 and RMSE=1.17gCm−2d−1 for NEE). The calibrated Biome-BGC was then applied to produce a longer time series for net primary productivity (NPP), which, after conversion into AGB increments according to site-calibrated coefficients, were compared to dendrochronological measurements (R2=0.73 and RMSE=46.65gm−2year−1). By combining these increments with the AGB map of 2009, we were able to model forest AGB dynamics. In the final step, we conducted a Monte Carlo analysis of uncertainties for interannual forest AGB estimates based on errors in the above forest AGB map, NPP estimates, and the conversion of NPP to an AGB increment.

Soil nitrate accumulation explains the nonlinear responses of soil CO2 and CH4 fluxes to nitrogen addition in a temperate needle-broadleaved mixed forest

The responses of soil-atmosphere carbon (C) exchange fluxes to growing atmospheric nitrogen (N) deposition are controversial, leading to large uncertainty in the estimated C sink of global forest ecosystems experiencing substantial N inputs. However, it is challenging to quantify critical load of N input for the alteration of the soil C fluxes, and what factors controlled the changes in soil CO2 and CH4 fluxes under N enrichment. Nine levels of urea addition experiment (0, 10, 20, 40, 60, 80, 100, 120, 140kgNha−1yr−1) were conducted in the needle-broadleaved mixed forest in Changbai Mountain, Northeast China. Soil CO2 and CH4 fluxes were monitored weekly using the static chamber and gas chromatograph technique. Environmental variables (soil temperature and moisture in the 0–10cm depth) and dissolved N (NH4+-N, NO3−-N, total dissolved N (TDN), and dissolved organic N (DON)) in the organic layer and the 0–10cm mineral soil layer were simultaneously measured. High rates of N addition (≥60kgNha−1 yr−1) significantly increased soil NO3−-N contents in the organic layer and the mineral layer by 120%-180% and 56.4%-84.6%, respectively. However, N application did not lead to a significant accumulation of soil NH4+-N contents in the two soil layers except for a few treatments. N addition at a low rate of 10kgNha−1 yr−1 significantly stimulated, whereas high rate of N addition (140kgNha−1yr−1) significantly inhibited soil CO2 emission and CH4 uptake. Significant negative relationships were observed between changes in soil CO2 emission and CH4 uptake and changes in soil NO3−-N and moisture contents under N enrichment. These results suggest that soil nitrification and NO3−-N accumulation could be important regulators of soil CO2 emission and CH4 uptake in the temperate needle-broadleaved mixed forest. The nonlinear responses to exogenous N inputs and the critical level of N in terms of soil C fluxes should be considered in the ecological process models and ecosystem management.

Mathematical modeling of climate and ecological processes in urban areas

Mathematical modeling of climate and ecological processes in urban areas

Evaluating trade-offs in bull trout reintroduction strategies using structured decision making

Structured decision making allows reintroduction decisions to be made despite uncertainty by linking reintroduction goals with alternative management actions through predictive models of ecological processes. We developed a decision model to evaluate the trade-offs between six bull trout (Salvelinus confluentus) reintroduction decisions with the goal of maximizing the number of adults in the recipient population without reducing the donor population to an unacceptable level. Sensitivity analyses suggested that the decision identity and outcome were most influenced by survival parameters that result in increased adult abundance in the recipient population, increased juvenile survival in the donor and recipient populations, adult fecundity rates, and sex ratio. The decision was least sensitive to survival parameters associated with the captive-reared population, the effect of naivety on released individuals, and juvenile carrying capacity of the reintroduced population. The model and sensitivity analyses can serve as the foundation for formal adaptive management and improved effectiveness, efficiency, and transparency of bull trout reintroduction decisions.

Identifying multispecies synchrony in response to environmental covariates.

The importance of multispecies models for understanding complex ecological processes and interactions is beginning to be realized. Recent developments, such as those by Lahoz‐Monfort et al. (2011), have enabled synchrony in demographic parameters across multiple species to be explored. Species in a similar environment would be expected to be subject to similar exogenous factors, although their response to each of these factors may be quite different. The ability to group species together according to how they respond to a particular measured covariate may be of particular interest to ecologists. We fit a multispecies model to two sets of similar species of garden bird monitored under the British Trust for Ornithology's Garden Bird Feeding Survey. Posterior model probabilities were estimated using the reversible jump algorithm to compare posterior support for competing models with different species sharing different subsets of regression coefficients. There was frequently good agreement between species with small asynchronous random‐effect components and those with posterior support for models with shared regression coefficients; however, this was not always the case. When groups of species were less correlated, greater uncertainty was found in whether regression coefficients should be shared or not. The methods outlined in this study can test additional hypotheses about the similarities or synchrony across multiple species that share the same environment. Through the use of posterior model probabilities, estimated using the reversible jump algorithm, we can detect multispecies responses in relation to measured covariates across any combination of species and covariates under consideration. The method can account for synchrony across species in relation to measured covariates, as well as unexplained variation accounted for using random effects. For more flexible, multiparameter distributions, the support for species‐specific parameters can also be measured.

Evaluating future success of whitebark pine ecosystem restoration under climate change using simulation modeling

Major declines of whitebark pine forests throughout western North America from the combined effects of mountain pine beetle (Dendroctonus ponderosae) outbreaks, fire exclusion policies, and the exotic disease white pine blister rust (WPBR) have spurred many restoration actions. However, projected future warming and drying may further exacerbate the species' decline and possibly compromise long‐term success of today's restoration activities. We evaluated successes of restoration treatments under future climate using a comprehensive landscape simulation experiment. The spatially explicit, ecological process model FireBGCv2 was used to simulate whitebark pine populations on two U.S. Northern Rocky Mountain landscapes over 95 years under two climate, three restoration, and two fire management scenarios. Major findings were that (1) whitebark pine can remain on some high mountain landscapes in a future climate albeit at lower basal areas (50% decrease), (2) restoration efforts, such as thinning and prescribed burning, are vital to ensure future whitebark pine forests, and (3) climate change impacts on whitebark pine vary by local setting. Whitebark pine restoration efforts will mostly be successful in the future but only if future populations are somewhat resistant to WPBR. Results were used to develop general guidelines that address climate change impacts for planning, designing, implementing, and evaluating fine‐scale restoration activities.

Temperate Assumptions: How Where We Work Influences How We Think.

Scientists have been observing the natural world for centuries and have long been intrigued by the high biodiversity and complexity of the tropics. They also usually had North American or European-in other words, outsider-perspectives and frequently concluded that the tropics were qualitatively different from the temperate regions in their ecology, evolution, and behavior. In particular, the tropics were seen as having a more benign abiotic environment, which in turn fostered more complex biotic relationships, with increased competition and other interactions. This may or may not be the case. Regardless, these ideas establish the temperate regions of the world as a kind of model system, a norm to which the tropics are compared and seen as different or unusual. The tropics are warmer or more diverse, rather than the temperate zone being cooler or less diverse. Such an attitude makes it difficult both to appreciate the scope of variation in nature and to develop accurate and general models for ecological and evolutionary processes.