Lags in the response of mountain plant communities to climate change

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: “dispersal lags” affecting plant species’ spread along elevational gradients, “establishment lags” following their arrival in recipient communities, and “extinction lags” of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species’ range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide.     

No species is an island: testing the effects of biotic interactions on models of avian niche occupation

Traditionally, the niche of a species is described as a hypothetical 3D space, constituted by well‐known biotic interactions (e.g. predation, competition, trophic relationships, resource–consumer interactions, etc.) and various abiotic environmental factors. Species distribution models (SDMs), also called “niche models” and often used to predict wildlife distribution at landscape scale, are typically constructed using abiotic factors with biotic interactions generally been ignored. Here, we compared the goodness of fit of SDMs for red‐backed shrike Lanius collurio in farmlands of Western Poland, using both the classical approach (modeled only on environmental variables) and the approach which included also other potentially associated bird species. The potential associations among species were derived from the relevant ecological literature and by a correlation matrix of occurrences. Our findings highlight the importance of including heterospecific interactions in improving our understanding of niche occupation for bird species. We suggest that suite of measures currently used to quantify realized species niches could be improved by also considering the occurrence of certain associated species. Then, an hypothetical “species 1” can use the occurrence of a successfully established individual of “species 2” as indicator or “trace” of the location of available suitable habitat to breed. We hypothesize this kind of biotic interaction as the “heterospecific trace effect” (HTE): an interaction based on the availability and use of “public information” provided by individuals from different species. Finally, we discuss about the incomes of biotic interactions for enhancing the predictive capacities on species distribution models.     

Climate shifts the interaction web of a marine plankton community

Climatic effects in the ocean at the community level are poorly described, yet accurate predictions about ecosystem responses to changing environmental conditions rely on understanding biotic responses in a food‐web context to support knowledge about direct biotic responses to the physical environment. Here we conduct time‐series analyses with multivariate autoregressive (MAR) models of marine zooplankton abundance in the Northern California Current from 1996 to 2009 to determine the influence of climate variables on zooplankton community interactions. Autoregressive models showed different community interactions during warm vs. cool ocean climate conditions. Negative ecological interactions among zooplankton groups characterized the major warm phase during the time series, whereas during the major cool phase, ocean transport largely structured zooplankton communities. Local environmental conditions (sea temperature) and large‐scale climate indices (El Niño/Southern Oscillation) were associated with changes in zooplankton abundance across the full time series. Secondary environmental correlates of zooplankton abundance varied with ocean climate phase, with most support during the warm phase for upwelling as a covariate, and most support during the cool phase for salinity. Through simultaneous quantitation of community interactions and environmental covariates, we show that marine zooplankton community structure varies with climate, suggesting that predictions about ecosystem responses to future climate scenarios in the Northern California Current should include potential changes to the base of the pelagic food.     

Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks

Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub‐disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species’ presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change.     

Context‐dependent biotic interactions control plant abundance across altitudinal environmental gradients

Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context‐dependent across abiotic gradients. For example, plant–plant interactions can grade from competition to facilitation over temperature gradients. We used a hierarchical Bayesian framework to predict the abundances of 12 plant species across a mountain landscape and test hypotheses on the context‐dependency of biotic interactions over abiotic gradients. We combined field‐based estimates of six biotic interactions (foliar herbivory and pathogen damage, fungal root colonization, fossorial mammal disturbance, plant cover and plant diversity) with abiotic data on climate and soil depth, nutrients and moisture. All biotic interactions were significantly context‐dependent along temperature gradients. Results supported the stress gradient hypothesis: as abiotic stress increased, the strength or direction of the relationship between biotic variables and plant abundance generally switched from negative (suggesting suppressed plant abundance) to positive (suggesting facilitation/mutualism). For half of the species, plant cover was the best predictor of abundance, suggesting that the prior focus on plant–plant interactions is well‐justified. Explicitly incorporating the context‐dependency of biotic interactions generated novel hypotheses about drivers of plant abundance across abiotic gradients and may improve the accuracy of niche models.     

Shifting Regimes and Changing Interactions in the Lake Washington,U.S.A., Plankton Community from 1962–1994

Understanding how changing climate, nutrient regimes, and invasive species shift food web structure is critically important in ecology. Most analytical approaches, however, assume static species interactions and environmental effects across time. Therefore, we applied multivariate autoregressive (MAR) models in a moving window context to test for shifting plankton community interactions and effects of environmental variables on plankton abundance in Lake Washington, U.S.A. from 1962–1994, following reduced nutrient loading in the 1960s and the rise of Daphnia in the 1970s. The moving-window MAR (mwMAR) approach showed shifts in the strengths of interactions between Daphnia, a dominant grazer, and other plankton taxa between a high nutrient, Oscillatoria-dominated regime and a low nutrient, Daphnia-dominated regime. The approach also highlighted the inhibiting influence of the cyanobacterium Oscillatoria on other plankton taxa in the community. Overall community stability was lowest during the period of elevated nutrient loading and Oscillatoria dominance. Despite recent warming of the lake, we found no evidence that anomalous temperatures impacted plankton abundance. Our results suggest mwMAR modeling is a useful approach that can be applied across diverse ecosystems, when questions involve shifting relationships within food webs, and among species and abiotic drivers.     

Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances

Although abiotic factors, together with dispersal and biotic interactions, are often suggested to explain the distribution of species and their abundances, species distribution models usually focus on abiotic factors only. We propose an integrative framework linking ecological theory, empirical data and statistical models to understand the distribution of species and their abundances together with the underlying community assembly dynamics. We illustrate our approach with 21 plant species in the French Alps. We show that a spatially nested modelling framework significantly improves the model's performance and that the spatial variations of species presence-absence and abundances are predominantly explained by different factors. We also show that incorporating abiotic, dispersal and biotic factors into the same model bring new insights to our understanding of community assembly. This approach, at the crossroads between community ecology and biogeography, is a promising avenue for a better understanding of species co-existence and biodiversity distribution.     

Phytoplankton community interactions and environmental sensitivity in coastal and offshore habitats

Assessing the relative importance of environmental conditions and community interactions is necessary for evaluating the sensitivity of biological communities to anthropogenic change. Phytoplankton communities have a central role in aquatic food webs and biogeochemical cycles, therefore, consequences of differing community sensitivities may have broad ecosystem effects. Using two long‐term time series (28 and 20 years) from the Baltic Sea, we evaluated coastal and offshore major phytoplankton taxonomic group biovolume patterns over annual and monthly time‐scales and assessed their response to environmental drivers and biotic interactions. Overall, coastal phytoplankton responded more strongly to environmental anomalies than offshore phytoplankton, although the specific environmental driver changed with time scale. A trend indicating a state shift in annual biovolume anomalies occurred at both sites and the shift's timing at the coastal site closely tracked other long‐term Baltic Sea ecosystem shifts. Cyanobacteria and the autotrophic ciliate Mesodinium rubrum were more strongly related than other groups to this trend with opposing relationships that were consistent across sites. On a monthly scale, biotic interactions within communities were rare and did not overlap between the coastal and offshore sites. Annual scales may be better able to assess general patterns across habitat types in the Baltic Sea, but monthly community dynamics may differ at relatively small spatial scales and consequently respond differently to future change.     

Phylogenetic distance–decay patterns are not explained by local community assembly processes in freshwater lake microbial communities

While water and sediment microbial communities exhibit pronounced spatio-temporal patterns in freshwater lakes, the underlying drivers are yet poorly understood. Here, we evaluated the importance of spatial and temporal variation in abiotic environmental factors for bacterial and microeukaryotic community assembly and distance–decay relationships in water and sediment niches in Hongze Lake. By sampling across the whole lake during both Autumn and Spring sampling time points, we show that only bacterial sediment communities were governed by deterministic community assembly processes due to abiotic environmental drivers. Nevertheless, consistent distance–decay relationships were found with both bacterial and microeukaryotic communities, which were relatively stable with both sampling time points. Our results suggest that spatio-temporal variation in environmental factors was important in explaining mainly bacterial community assembly in the sediment, possibly due lesser disturbance. However, clear distance–decay patterns emerged also when the community assembly was stochastic. Together, these results suggest that abiotic environmental factors do not clearly drive the spatial structuring of lake microbial communities, highlighting the need to understand the role of other potential drivers, such as spatial heterogeneity and biotic species interactions.     

Recurrent Seasonal Variations in Abundance and Composition of Filamentous SOL Cluster Bacteria (Saprospiraceae, Bacteroidetes) in Oligomesotrophic Lake Mondsee (Austria)

The spatial and temporal variation of SOL cluster bacteria was assessed in oligomesotrophic Lake Mondsee and adjacent lakes by fluorescence in situ hybridization over two annual cycles. The filamentous SOL bacteria were present in Lake Mondsee throughout the study period, and the seasonal dynamics of the SOL community were remarkably similar with respect to both abundance and composition in the two consecutive years. Only two of the three SOL subclusters were detected in Lake Mondsee and four connected lakes. These two populations significantly differed in size distribution and demonstrated pronounced but recurrent differences in seasonality and length of period of appearance in Lake Mondsee. Extensive sampling of the lakes in September 2003 revealed low horizontal variation in the composition of the SOL community within Lake Mondsee but marked variations with depth. Between connected habitats pronounced differences in the composition and abundance of the SOL community were detected. The interaction of SOL bacteria with bacterivorous protists, mesozooplankton, and phytoplankton was investigated in order to reveal variables controlling the structure and dynamics of SOL communities. No strong indication for a bottom-up influence of phytoplankton was found, while the estimated community grazing rates of mesozooplankton on SOL bacteria indicated a top-down control of SOL abundance during mesozooplankton peaks in spring and early autumn. Furthermore, species-specific differences in grazing of mesozooplankton on SOL bacteria were observed. In general, the overall composition of SOL communities was controlled by abiotic factors (water chemistry), while their dynamics seemed to be controlled by abiotic and biotic interactions.     

Recurrent seasonal variations in abundance and composition of filamentous SOL cluster bacteria (Saprospiraceae, Bacteroidetes) in oligomesotrophic Lake Mondsee (Austria)

The spatial and temporal variation of SOL cluster bacteria was assessed in oligomesotrophic Lake Mondsee and adjacent lakes by fluorescence in situ hybridization over two annual cycles. The filamentous SOL bacteria were present in Lake Mondsee throughout the study period, and the seasonal dynamics of the SOL community were remarkably similar with respect to both abundance and composition in the two consecutive years. Only two of the three SOL subclusters were detected in Lake Mondsee and four connected lakes. These two populations significantly differed in size distribution and demonstrated pronounced but recurrent differences in seasonality and length of period of appearance in Lake Mondsee. Extensive sampling of the lakes in September 2003 revealed low horizontal variation in the composition of the SOL community within Lake Mondsee but marked variations with depth. Between connected habitats pronounced differences in the composition and abundance of the SOL community were detected. The interaction of SOL bacteria with bacterivorous protists, mesozooplankton, and phytoplankton was investigated in order to reveal variables controlling the structure and dynamics of SOL communities. No strong indication for a bottom-up influence of phytoplankton was found, while the estimated community grazing rates of mesozooplankton on SOL bacteria indicated a top-down control of SOL abundance during mesozooplankton peaks in spring and early autumn. Furthermore, species-specific differences in grazing of mesozooplankton on SOL bacteria were observed. In general, the overall composition of SOL communities was controlled by abiotic factors (water chemistry), while their dynamics seemed to be controlled by abiotic and biotic interactions.     

A seasonal alternation of coherent and compensatory dynamics occurs in phytoplankton

Functional groups with diverse responses to environmental factors sum to produce communities with less temporal variability in their biomass than those lacking this diversity. The detection of these compensatory dynamics can be complicated by a spatio-temporal alternation in the environmental factors limiting growth (both abiotic and biotic), which restricts the occurrence of compensatory dynamics to certain periods or locations. Hence, resolving the spatio-temporal scale may uncover important spatial and/or temporal components in community variability. Using long-term data from Lake Constance (Bodensee), we find that a reduction in grazing pressure and relaxed competition for nutrients during winter and spring generates coherent dynamics among edible and less edible phytoplankton. During summer and fall, when both grazing pressure and nutrient limitation are present, edible and less edible phytoplankton exhibit compensatory dynamics. This study supports recent work suggesting that both abiotic and biotic interactions promote compensatory dynamics and to our knowledge, this is the first example of a system where compensatory and coherent dynamics seasonally alternate.     

Enhanced effects of biotic interactions on predicting multispecies spatial distribution of submerged macrophytes after eutrophication

Water eutrophication creates unfavorable environmental conditions for submerged macrophytes. In these situations, biotic interactions may be particularly important for explaining and predicting the submerged macrophytes occurrence. Here, we evaluate the roles of biotic interactions in predicting spatial occurrence of submerged macrophytes in 1959 and 2009 for Dianshan Lake in eastern China, which became eutrophic since the 1980s. For the four common species occurred in 1959 and 2009, null species distribution models based on abiotic variables and full models based on both abiotic and biotic variables were developed using generalized linear model (GLM) and boosted regression trees (BRT) to determine whether the biotic variables improved the model performance. Hierarchical Bayesian‐based joint species distribution models capable of detecting paired biotic interactions were established for each species in both periods to evaluate the changes in the biotic interactions. In most of the GLM and BRT models, the full models showed better performance than the null models in predicting the species presence/absence, and the relative importance of the biotic variables in the full models increased from less than 50% in 1959 to more than 50% in 2009 for each species. Moreover, co‐occurrence correlation of each paired species interaction was higher in 2009 than that in 1959. The findings suggest biotic interactions that tend to be positive play more important roles in the spatial distribution of multispecies assemblages of macrophytes and should be included in prediction models to improve prediction accuracy when forecasting macrophytes’ distribution under eutrophication stress.     

Applying population and community ecology theory to advance understanding of belowground biogeochemistry

Approaches to quantifying and predicting soil biogeochemical cycles mostly consider microbial biomass and community composition as products of the abiotic environment. Current numerical approaches then primarily emphasise the importance of microbe–environment interactions and physiology as controls on biogeochemical cycles. Decidedly less attention has been paid to understanding control exerted by community dynamics and biotic interactions. Yet a rich literature of theoretical and empirical contributions highlights the importance of considering how variation in microbial population ecology, especially biotic interactions, is related to variation in key biogeochemical processes like soil carbon formation. We demonstrate how a population and community ecology perspective can be used to (1) understand the impact of microbial communities on biogeochemical cycles and (2) reframe current theory and models to include more detailed microbial ecology. Through a series of simulations we illustrate how density dependence and key biotic interactions, such as competition and predation, can determine the degree to which microbes regulate soil biogeochemical cycles. The ecological perspective and model simulations we present lay the foundation for developing empirical research and complementary models that explore the diversity of ecological mechanisms that operate in microbial communities to regulate biogeochemical processes.     

Seasonal dietary shifts enhance parasite transmission to lake salmonids during ice cover

Changes in abiotic and biotic factors between seasons in subarctic lake systems are often profound, potentially affecting the community structure and population dynamics of parasites over the annual cycle. However, few winter studies exist and interactions between fish hosts and their parasites are typically confined to snapshot studies restricted to the summer season whereas host‐parasite dynamics during the ice‐covered period rarely have been explored. The present study addresses seasonal patterns in the infections of intestinal parasites and their association with the diet of sympatric living Arctic charr (Salvelinus alpinus) and brown trout (Salmo trutta) in Lake Takvatn, a subarctic lake in northern Norway. In total, 354 Arctic charr and 203 brown trout were sampled from the littoral habitat between June 2017 and May 2018. Six trophically transmitted intestinal parasite taxa were identified and quantified, and their seasonal variations were contrasted with dietary information from both stomachs and intestines of the fish. The winter period proved to be an important transmission window for parasites, with increased prevalence and intensity of amphipod‐transmitted parasites in Arctic charr and parasites transmitted through fish prey in brown trout. In Arctic charr, seasonal patterns in parasite infections resulted mainly from temporal changes in diet toward amphipods, whereas host body size and the utilization of fish prey were the main drivers in brown trout. The overall dynamics in the community structure of parasites chiefly mirrored the seasonal dietary shifts of their fish hosts.     

Ecology under lake ice

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer ‘growing seasons’. We executed the first global quantitative synthesis on under‐ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter‐summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake‐specific, species‐specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.     

Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities.     

Long‐term population dynamics of a migrant bird suggests interaction of climate change and competition with resident species

The separation of abiotic and biotic factors affecting populations and communities is an important step in understanding how climate change can influence ecological processes, but quantifying their relative contribution to community changes is a challenge. We assessed the effect of temperature and species interactions on the population dynamics of a forest bird community with a hierarchical dynamic population model in a Bayesian framework. We used a long‐term time‐series (1956–2012) of four secondary cavity‐nesting birds with similar food and nesting requirements but different migration habits, to analyse the effects of the four species population size and the local weather fluctuations on each species’ population dynamics. We found clear evidence of a negative effect of two resident species (blue tit and great tit) on a long‐distance migrant (pied flycatcher). Among the residents we only found a competition effect of the great tit on the marsh tit. The birds showed opposite responses to weather: the pied flycatcher favoured colder springs whereas the blue tit and great tit favoured warmer springs. Although alternative mechanisms cannot be ruled out, our results suggest that the resident species (blue tit and great tit) could adjust to increasing spring temperature while the migrant species (pied flycatcher) could not, leading progressively to the exclusion of the pied flycatcher from the area. These results point out the potential role of competitive interactions by providing insightful clues, call for refined research, and support recent efforts to include population dynamics in species distribution models.     

Microhabitat selection in the common lizard: implications of biotic interactions,age, sex,local processes,and model transferability among populations

Modeling species' habitat requirements are crucial to assess impacts of global change, for conservation efforts and to test mechanisms driving species presence. While the influence of abiotic factors has been widely examined, the importance of biotic factors and biotic interactions, and the potential implications of local processes are not well understood. Testing their importance requires additional knowledge and analyses at local habitat scale. Here, we recorded the locations of species presence at the microhabitat scale and measured abiotic and biotic parameters in three different common lizard (Zootoca vivipara) populations using a standardized sampling protocol. Thereafter, space use models and cross‐evaluations among populations were run to infer local processes and estimate the importance of biotic parameters, biotic interactions, sex, and age. Biotic parameters explained more variation than abiotic parameters, and intraspecific interactions significantly predicted the spatial distribution. Significant differences among populations in the relationship between abiotic parameters and lizard distribution, and the greater model transferability within populations than between populations are in line with effects predicted by local adaptation and/or phenotypic plasticity. These results underline the importance of including biotic parameters and biotic interactions in space use models at the population level. There were significant differences in space use between sexes, and between adults and yearlings, the latter showing no association with the measured parameters. Consequently, predictive habitat models at the population level taking into account different sexes and age classes are required to understand a specie's ecological requirements and to allow for precise conservation strategies. Our study therefore stresses that future predictive habitat models at the population level and their transferability should take these parameters into account.     

Spatio‐temporal variation of biotic factors underpins contemporary range dynamics of congeners

Species’ ranges are complex often exhibiting multidirectional shifts over space and time. Despite the strong fingerprint of recent historical climate change on species’ distributions, biotic factors such as loss of vegetative habitat and the presence of potential competitors constitute important yet often overlooked drivers of range dynamics. Furthermore, short‐term changes in environmental conditions can influence the underlying processes of local extinction and local colonization that drive range shifts, yet are rarely considered at broad scales. We used dynamic state‐space occupancy models to test multiple hypotheses of the relative importance of major drivers of range shifts of Golden‐winged Warblers (Vermivora chrysoptera) and Blue‐winged Warblers (V. cyanoptera) between 1983 and 2012 across North America: warming temperatures; habitat changes; and occurrence of congeneric species, used here as proxy for biotic interactions. Dynamic occupancies for both species were most influenced by spatial relative to temporal variation in temperature and habitat. However, temporal variation in temperature anomalies and biotic interactions remained important. The two biotic factors considered, habitat change and biotic interactions, had the largest relative effect on estimated extinction rates followed by abiotic temperature anomalies. For the Golden‐winged Warbler, the predicted presence of the Blue‐winged Warbler, a hypothesized competitor, most influenced extinction probabilities, contributing to evidence supporting its role in site‐level species replacement. Given the overall importance of biotic factors on range‐wide dynamic occupancies, their consideration alongside abiotic factors should not be overlooked. Our results suggest that warming compounds the negative effect of habitat loss emphasizing species’ need for habitat to adapt to a changing climate. Notably, even closely related species exhibited individual responses to abiotic and biotic factors considered.