Occupancy versus colonization–extinction models for projecting population trends at different spatial scales

Understanding spatiotemporal population trends and their drivers is a key aim in population ecology. We further need to be able to predict how the dynamics and sizes of populations are affected in the long term by changing landscapes and climate. However, predictions of future population trends are sensitive to a range of modeling assumptions. Deadwood‐dependent fungi are an excellent system for testing the performance of different predictive models of sessile species as these species have different rarity and spatial population dynamics, the populations are structured at different spatial scales, and they utilize distinct substrates. We tested how the projected large‐scale occupancies of species with differing landscape‐scale occupancies are affected over the coming century by different modeling assumptions. We compared projections based on occupancy models against colonization–extinction models, conducting the modeling at alternative spatial scales and using fine‐ or coarse‐resolution deadwood data. We also tested effects of key explanatory variables on species occurrence and colonization–extinction dynamics. The hierarchical Bayesian models applied were fitted to an extensive repeated survey of deadwood and fungi at 174 patches. We projected higher occurrence probabilities and more positive trends using the occupancy models compared to the colonization–extinction models, with greater difference for the species with lower occupancy, colonization rate, and colonization:extinction ratio than for the species with higher estimates of these statistics. The magnitude of future increase in occupancy depended strongly on the spatial modeling scale and resource resolution. We encourage using colonization–extinction models over occupancy models, modeling the process at the finest resource‐unit resolution that is utilizable by the species, and conducting projections for the same spatial scale and resource resolution at which the model fitting is conducted. Further, the models applied should include key variables driving the metapopulation dynamics, such as the availability of suitable resource units, habitat quality, and spatial connectivity.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

When and where to move: Dynamic occupancy models explain the range dynamics of a food nomadic bird under climate and land cover change

Globally, long‐term research is critical to monitor the responses of tropical species to climate and land cover change at the range scale. Citizen science surveys can reveal the long‐term persistence of poorly known nomadic tropical birds occupying fragmented forest patches. We applied dynamic occupancy models to 13 years (2002–2014) of citizen science‐driven presence/absence data on Cape parrot (Poicephalus robustus), a food nomadic bird endemic to South Africa. We modeled its underlying range dynamics as a function of resource distribution, and change in climate and land cover through the estimation of colonization and extinction patterns. The range occupancy of Cape parrot changed little over time (ψ = 0.75–0.83) because extinction was balanced by recolonization. Yet, there was considerable regional variability in occupancy and detection probability increased over the years. Colonizations increased with warmer temperature and area of orchards, thus explaining their range shifts southeastwards in recent years. Although colonizations were higher in the presence of nests and yellowwood trees (Afrocarpus and Podocarpus spp.), the extinctions in small forest patches (≤227 ha) and during low precipitation (≤41 mm) are attributed to resource constraints and unsuitable climatic conditions. Loss of indigenous forest cover and artificial lake/water bodies increased extinction probabilities of Cape parrot. The land use matrix (fruit farms, gardens, and cultivations) surrounding forest patches provides alternative food sources, thereby facilitating spatiotemporal colonization and extinction in the human‐modified matrix. Our models show that Cape parrots are vulnerable to extreme climatic conditions such as drought which is predicted to increase under climate change. Therefore, management of optimum sized high‐quality forest patches is essential for long‐term survival of Cape parrot populations. Our novel application of dynamic occupancy models to long‐term citizen science monitoring data unfolds the complex relationships between the environmental dynamics and range fluctuations of this food nomadic species.     

Ecological and methodological drivers of non-stationarity in tree growth response to climate

Radial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non-stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non-stationarity. To systematically assess potential drivers of non-stationarity, we compiled tree-ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non-linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate–growth models were tested in independent verification periods to quantify their non-stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non-stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non-stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non-stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non-stationarity compared with monthly-resolved non-linear models. We conclude that non-stationarity in climate–growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non-stationarity, we recommend that temporal non-stationarity rather than stationarity should be considered as the baseline model of climate–growth response for temperate forests.     

Modeling nonbreeding distributions of shorebirds and waterfowl in response to climate change

To identify areas on the landscape that may contribute to a robust network of conservation areas, we modeled the probabilities of occurrence of several en route migratory shorebirds and wintering waterfowl in the southern Great Plains of North America, including responses to changing climate. We predominantly used data from the eB ird citizen‐science project to model probabilities of occurrence relative to land‐use patterns, spatial distribution of wetlands, and climate. We projected models to potential future climate conditions using five representative general circulation models of the Coupled Model Intercomparison Project 5 (CMIP 5). We used Random Forests to model probabilities of occurrence and compared the time periods 1981–2010 (hindcast) and 2041–2070 (forecast) in “model space.” Projected changes in shorebird probabilities of occurrence varied with species‐specific general distribution pattern, migration distance, and spatial extent. Species using the western and northern portion of the study area exhibited the greatest likelihoods of decline, whereas species with more easterly occurrences, mostly long‐distance migrants, had the greatest projected increases in probability of occurrence. At an ecoregional extent, differences in probabilities of shorebird occurrence ranged from ?0.015 to 0.045 when averaged across climate models, with the largest increases occurring early in migration. Spatial shifts are predicted for several shorebird species. Probabilities of occurrence of wintering Mallards and Northern Pintail are predicted to increase by 0.046 and 0.061, respectively, with northward shifts projected for both species. When incorporated into partner land management decision tools, results at ecoregional extents can be used to identify wetland complexes with the greatest potential to support birds in the nonbreeding season under a wide range of future climate scenarios.     

Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change

Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (E_occupied), the instability of suitable area (E_instability) and the overlap between the current and future spatial distribution (E_overlap). The current dominance and occurrence were modelled in relation to a set of environmental variables using boosted regression tree (BRT) models, under two scenarios of seedling establishment: unrestricted and highly restricted. While forecasts of spatial change were qualitatively similar for species occurrence and dominance, the models of species dominance exhibited higher metrics of model fit and predictive performance, and the spatial pattern of future dominance was less similar to the current pattern than was the case for the distributions of species occurrence. This highlights the possibility of greater changes in the spatial patterning of mangrove tree species dominance under future sea level rise. Under the restricted seedling establishment scenario, the area occupied by or dominated by a species declined between 42.1% and 93.8%, while for unrestricted seedling establishment, the area suitable for dominance or occurrence of each species varied from a decline of 68.4% to an expansion of 99.5%. As changes in the spatial patterning of dominance are likely to cause a cascade of effects throughout the ecosystem, forecasting spatial changes in dominance provides new and complementary information in addition to that provided by forecasts of species occurrence.     

To Be Or Not to Be? Variable selection can change the projected fate of a threatened species under future climate

Species distribution models (SDMs) are commonly used to project future changes in the geographic ranges of species, to estimate extinction rates and to plan biodiversity conservation. However, these models can produce a range of results depending on how they are parameterized, and over‐reliance on a single model may lead to overconfidence in maps of future distributions. The choice of predictor variable can have a greater influence on projected future habitat than the range of climate models used. We demonstrate this in the case of the Ptunarra Brown Butterfly, a species listed as vulnerable in Tasmania, Australia. We use the Maxent model to develop future projections for this species based on three variable sets; all 35 commonly used so‐called ‘bioclimatic’ variables, a subset of these based on expert knowledge, and a set of monthly climate variables relevant to the species’ primary activity period. We used a dynamically downscaled regional climate model based on three global climate models. Depending on the choice of variable set, the species is projected either to experience very little contraction of habitat or to come close to extinction by the end of the century due to lack of suitable climate. The different conclusions could have important consequences for conservation planning and management, including the perceived viability of habitat restoration. The output of SDMs should therefore be used to define the range of possible trajectories a species may be on, and ongoing monitoring used to inform management as changes occur.     

Metapopulation dynamics of a beetle species confined to burned forest sites in a managed forest region

Despite increasing awareness of the theoretical importance of habitat dynamics on metapopulations, only a few empirical studies have been conducted. We aimed to increase our understanding of how patch size, dynamics and connectivity affect colonization–extinction dynamics and the occurrence patterns of a beetle (Stephanopachys linearis), which breeds only in burned trees, existing as dynamic habitat patches that have become rare in managed forest landscapes. We assessed species’ presence/absence twice in all known habitat patches (i.e. > 1 ha sites where forest fires had occurred during the previous 2–15 yr) in a 200 × 150 km region of central Sweden, dominated by managed boreal forest. Evaluated over six years, the colonization rate was 47% and the local extinction risk was 65%. Probability of colonization increased with patch size (number of suitable trees in a site) and connectivity to occupied patches within 30 km, and decreased with increasing time since fire. Local extinction risk decreased with habitat patch size but increased, unexpectedly, with connectivity. Occurrence increased with patch size and decreased with increasing time since fire. At a regional scale, S. linearis tracks the fire dynamics by colonising sites with burned trees and by becoming extinct at rates which make the species rare at sites where burnt trees are more than eight years old. In managed boreal forest landscapes, a large proportion of sites may be created by prescribed burning (in our study area: 82%), and consequently human decisions strongly affect the future amount of habitat for fire‐dependent species and its spatial distribution. Stephanopachys linearis uses burned sites more often if more trees are retained and, to some extent, if sites are concentrated in those parts of a region that already support high population densities of the species.     

Land use changes could modify future negative effects of climate change on old‐growth forest indicator species

Aim

Climate change is expected to have major impacts on terrestrial biodiversity at all ecosystem levels, including reductions in species‐level distribution and abundance. We aim to test the extent to which land use management, such as setting‐aside forest from production, could reduce climate‐induced biodiversity impacts for specialist species over large geographical gradients.

Location

Sweden.

Methods

We applied ensembles of species distribution models based on citizen science data for six species of red‐listed old‐forest indicator fungi confined to spruce dead wood. We tested the effect on species habitat suitabilities of alternative climate change scenarios and varying amounts of forest set‐aside from production over the coming century.

Results

With 3.6% of forest area set‐aside from production and assuming no climate change, overall habitat suitabilities for all six species were projected to increase in response to maturing spruce in set‐aside forest. However, overall habitat suitabilities for all six species were projected to decline under climate change scenario RCP4.5 (intermediate–low emissions), with even greater declines projected under RCP 8.5 (high emissions). Increasing the amount of forest set‐aside to 16% resulted in significant increases in overall habitat suitability, with one species showing an increase. A further increase to 32% forest set‐aside resulted in considerably more positive trends, with three of six species increasing.

Main conclusions

There is interspecific variation in the importance of future macroclimate and resource availability on species occurrence. However, large‐scale conservation measures, such as increasing resource availability through setting aside forest from production, could reduce future negative effects from climate change, and early investment in conservation is likely to reduce the future negative impacts of climate change on specialist species.     

Estimating indices of range shifts in birds using dynamic models when detection is imperfect

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near‐term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.     

Possible effects of habitat fragmentation and climate change on the range of forest plant species

Global circulation models predict an increase in mean annual temperature between 2.1 and 4.6 °C by 2080 in the northern temperate zone. The associated changes in the ratio of extinctions and colonizations at the boundaries of species ranges are expected to result in northward range shifts for a lot of species. However, net species colonization at northern boundary ranges, necessary for a northward shift and for range conservation, may be hampered because of habitat fragmentation. We report the results of two forest plant colonization studies in two fragmented landscapes in central Belgium. Almost all forest plant species (85%) had an extremely low success of colonizing spatially segregated new suitable forest habitats after c . 40 years. In a landscape with higher forest connectivity, colonization success was higher but still insufficient to ensure large-scale colonization. Under the hypothesis of net extinction at southern range boundaries, forest plant species dispersal limitation will prevent net colonization at northern range boundaries required for range conservation.     

Monitoring occurrence,extinction, and colonization probabilities for gibbon populations

All gibbon species (Family: Hylobatidae) are considered threatened with extinction and recognized on the International Union for Conservation of Nature Red List of Threatened Species. Because gibbons are one of the most threatened families of primates, monitoring their status is now critically important. Long-term monitoring programs applying occupancy approaches, in addition to assessing occurrence probability, improves understanding of other population parameters such as site extinction or colonization probabilities, which elucidate temporal and spatial changes and are therefore important for guiding conservation efforts. In this study, we used multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities for northern yellow-cheeked crested gibbon Nomascus annamensis in three adjacent protected areas in the Central Annamites mountain range, Vietnam. We collected data at 30 listening posts in 2012, 2014, and 2016 using the auditory point count method. Occurrence probabilities were highest in 2012 (0.74, confidence interval [CI]: 0.56–0.87) but slightly lower in 2014 (0.66, CI: 0.51–0.79) and 2016 (0.67, CI: 0.49–0.81). Extinction probabilities during the 2012–2014 and 2014–2016 intervals were 0.26 (0.14–0.44) and 0.25 (0.12–0.44), respectively. Colonization probabilities during 2012–2014 were 0.44 (0.19–0.73) and between 2014 and 2016 was 0.51 (0.26–0.75). Although local site extinctions have occurred, high recolonization probability helped to replenish the unoccupied sites and kept the occurrence probability stable. Long-term monitoring programs which use occurrence probability alone might not fully reveal the true dynamics of gibbon populations. We strongly recommend including multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities in long-term gibbon monitoring programs.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Response of Sierra Nevada forests to projected climate–wildfire interactions

Climate influences forests directly and indirectly through disturbance. The interaction of climate change and increasing area burned has the potential to alter forest composition and community assembly. However, the overall forest response is likely to be influenced by species‐specific responses to environmental change and the scale of change in overstory species cover. In this study, we sought to quantify how projected changes in climate and large wildfire size would alter forest communities and carbon (C) dynamics, irrespective of competition from nontree species and potential changes in other fire regimes, across the Sierra Nevada, USA. We used a species‐specific, spatially explicit forest landscape model (LANDIS‐II) to evaluate forest response to climate–wildfire interactions under historical (baseline) climate and climate projections from three climate models (GFDL, CCSM3, and CNRM) forced by a medium–high emission scenario (A2) in combination with corresponding climate‐specific large wildfire projections. By late century, we found modest changes in the spatial distribution of dominant species by biomass relative to baseline, but extensive changes in recruitment distribution. Although forest recruitment declined across much of the Sierra, we found that projected climate and wildfire favored the recruitment of more drought‐tolerant species over less drought‐tolerant species relative to baseline, and this change was greatest at mid‐elevations. We also found that projected climate and wildfire decreased tree species richness across a large proportion of the study area and transitioned more area to a C source, which reduced landscape‐level C sequestration potential. Our study, although a conservative estimate, suggests that by late century, forest community distributions may not change as intact units as predicted by biome‐based modeling, but are likely to trend toward simplified community composition as communities gradually disaggregate and the least tolerant species are no longer able to establish. The potential exists for substantial community composition change and forest simplification beyond this century.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

Comparison of reintroduction and enhancement effects on metapopulation viability

Metapopulation viability depends upon a balance of extinction and colonization of local habitats by a species. Mechanisms that can affect this balance include physical characteristics related to natural processes (e.g. succession) as well as anthropogenic actions. Plant restorations can help to produce favorable metapopulation dynamics and consequently increase viability; however, to date no studies confirm this is true. Population viability analysis (PVA) allows for the use of empirical data to generate theoretical future projections in the form of median time to extinction and probability of extinction. In turn, PVAs can inform and aid the development of conservation, recovery, and management plans. Pitcher's thistle (Cirsium pitcheri) is a dune endemic that exhibited metapopulation dynamics. We projected viability of three natural and two restored populations with demographic data spanning 15–23 years to determine the degree the addition of reintroduced population affects metapopulation viability. The models were validated by comparing observed and projected abundances and adjusting parameters associated with demographic and environmental stochasticity to improve model performance. Our chosen model correctly predicted yearly population abundance for 60% of the population‐years. Using that model, 50‐year projections showed that the addition of reintroductions increases metapopulation viability. The reintroduction that simulated population performance in early‐successional habitats had the maximum benefit. In situ enhancements of existing populations proved to be equally effective. This study shows that restorations can facilitate and improve metapopulation viability of species dependent on metapopulation dynamics for survival with long‐term persistence of C. pitcheri in Indiana likely to depend on continued active management.     

Climate change,woodpeckers, and forests: Current trends and future modeling needs

The structure and composition of forest ecosystems are expected to shift with climate‐induced changes in precipitation, temperature, fire, carbon mitigation strategies, and biological disturbance. These factors are likely to have biodiversity implications. However, climate‐driven forest ecosystem models used to predict changes to forest structure and composition are not coupled to models used to predict changes to biodiversity. We proposed integrating woodpecker response (biodiversity indicator) with forest ecosystem models. Woodpeckers are a good indicator species of forest ecosystem dynamics, because they are ecologically constrained by landscape‐scale forest components, such as composition, structure, disturbance regimes, and management activities. In addition, they are correlated with forest avifauna community diversity. In this study, we explore integrating woodpecker and forest ecosystem climate models. We review climate–woodpecker models and compare the predicted responses to observed climate‐induced changes. We identify inconsistencies between observed and predicted responses, explore the modeling causes, and identify the models pertinent to integration that address the inconsistencies. We found that predictions in the short term are not in agreement with observed trends for 7 of 15 evaluated species. Because niche constraints associated with woodpeckers are a result of complex interactions between climate, vegetation, and disturbance, we hypothesize that the lack of adequate representation of these processes in the current broad‐scale climate–woodpecker models results in model–data mismatch. As a first step toward improvement, we suggest a conceptual model of climate–woodpecker–forest modeling for integration. The integration model provides climate‐driven forest ecosystem modeling with a measure of biodiversity while retaining the feedback between climate and vegetation in woodpecker climate change modeling.     

Integrating spatially explicit habitat projections into extinction risk assessments: a reassessment of Amazonian avifauna incorporating projected deforestation

Aim We aimed to complete the first systematic assessment of extinction risk based on projected population declines derived from spatially explicit habitat projections for any taxonomic group at a regional scale, to use the outputs to ascertain the efficacy of an existing protected area network in covering species of conservation concern, and identify gaps therein. Location This study focused on Amazonia; an area of exceptional biodiversity, currently experiencing the highest absolute rate of forest loss globally but where the proportion of species assessed as ‘threatened’ on the International Union for the Conservation of Nature (IUCN) Red List in the region is below global averages. Methods For all forest‐dependent Amazonian bird species (814), we revised extinction risk estimates by combining data from a spatially explicit deforestation model with generation length estimates. By overlaying distribution maps for these revised threatened species, we identified crisis areas (areas of projected deforestation supporting the highest numbers of threatened species), refugia (areas projected to retain forest supporting the highest numbers of threatened species) and areas of high irreplaceability: short‐ and long‐term priorities for new protected areas (PAs). Results The number of species qualifying as threatened rose substantially from 24 (3%) to 64–92 (8–11%). Areas of particular concern are the crisis and highly irreplaceable areas within the ‘arc of deforestation’ in the southern Brazilian Amazon states of Rondônia, Mato Grosso and Pará. Main conclusions Through a novel application of the IUCN Red List criteria, we present a spatially accurate rendering of the extinction risks of Amazonian birds. Important areas in the Amazon are not secure. We identify priorities for expansion of the PAs network and key locations where protection should be enforced. We recommend a collaborative approach employing our methods to repeat this process for other taxonomic groups.     

Evolutionary tracking is determined by differential selection on demographic rates and density dependence

Recent ecological forecasts predict that ~25% of species worldwide will go extinct by 2050. However, these estimates are primarily based on environmental changes alone and fail to incorporate important biological mechanisms such as genetic adaptation via evolution. Thus, environmental change can affect population dynamics in ways that classical frameworks can neither describe nor predict. Furthermore, often due to a lack of data, forecasting models commonly describe changes in population demography by summarizing changes in fecundity and survival concurrently with the intrinsic growth rate (r). This has been shown to be an oversimplification as the environment may impose selective pressure on specific demographic rates (birth and death) rather than directly on r (the difference between the birth and death rates). This differential pressure may alter population response to density, in each demographic rate, further diluting the information combined to produce r. Thus, when we consider the potential for persistence via adaptive evolution, populations with the same r can have different abilities to persist amidst environmental change. Therefore, we cannot adequately forecast population response to climate change without accounting for demography and selection on density dependence. Using a continuous‐time Markov chain model to describe the stochastic dynamics of the logistic model of population growth and allow for trait evolution via mutations arising during birth events, we find persistence via evolutionary tracking more likely when environmental change alters birth rather than the death rate. Furthermore, species that evolve responses to changes in the strength of density dependence due to environmental change are less vulnerable to extinction than species that undergo selection independent of population density. By incorporating these key demographic considerations into our predictive models, we can better understand how species will respond to climate change.     

Anthropogenic‐driven rapid shifts in tree distribution lead to increased dominance of broadleaf species

Over the past century, major shifts in the geographic distribution of tree species have occurred in response to changes in land use and climate. We analyse species distribution and abundance from about 33 000 forest inventory plots in Spain sampled twice over a period of 10–12 years. We show a dominance of range contraction (extinction), and demographic decline over range expansion (colonization), with seven of 11 species exhibiting extinction downhill of their distribution. Contrary to expectations, these dynamics are not always consistent with climate warming over the study period, but result from legacies in forest structure due to past land use change and fire occurrence. We find that these changes have led to the expansion of broadleaf species (i.e. family Fagaceae) over areas formerly dominated by conifer species (i.e. family Pinaceae), due to the greater capacity of the former to respond to most disturbances and their higher competitive ability. This recent and rapid transition from conifers to broadleaves has important implications in forest dynamics and ecosystem services they provide. The finding raises the question as to whether the increasing dominance of relatively drought‐sensitive broadleaf species will diminish resilience of Mediterranean forests to very likely drier conditions in the future.