Modeling bird species distribution change in fire prone Mediterranean landscapes: incorporating species dispersal and landscape dynamics

Species distribution models (SDMs) have traditionally been founded on the assumption that species distributions are in equilibrium with environmental conditions and that these species–environment relationships can be used to estimate species responses to environmental changes. Insight into the validity of this assumption can be obtained from comparing the performance of correlative species distribution models with more complex hybrid approaches, i.e. correlative and process‐based models that explicitly include ecological processes, thereby accounting for mismatches between habitat suitability and species occupancy patterns. Here we compared the ability of correlative SDMs and hybrid models, which can accommodate non‐equilibrium situations arising from dispersal constraints, to reproduce the distribution dynamics of the ortolan bunting Emberiza hortulana in highly dynamic, early successional, fire driven Mediterranean landscapes. Whereas, habitat availability was derived from a correlative statistical SDM, occupancy was modeled using a hybrid approach combining a grid‐based, spatially‐explicit population model that explicitly included bird dispersal with the correlative model. We compared species occupancy patterns under the equilibrium assumption and different scenarios of species dispersal capabilities. To evaluate the predictive capability of the different models, we used independent species data collected in areas affected to different degree by fires. In accordance with the view that disturbance leads to a disparity between the suitable habitat and the occupancy patterns of the ortolan bunting, our results indicated that hybrid modeling approaches were superior to correlative models in predicting species spatial dynamics. Furthermore, hybrid models that incorporated short dispersal distances were more likely to reproduce the observed changes in ortolan bunting distribution patterns, suggesting that dispersal plays a key role in limiting the colonization of recently burnt areas. We conclude that SDMs used in a dynamic context can be significantly improved by using combined hybrid modeling approaches that explicitly account for interactions between key ecological constraints such as dispersal and habitat suitability that drive species response to environmental changes.     

The fate of European breeding birds under climate,land‐use and dispersal scenarios

Many species have already shifted their distributions in response to recent climate change. Here, we aimed at predicting the future breeding distributions of European birds under climate, land‐use, and dispersal scenarios. We predicted current and future distributions of 409 species within an ensemble forecast framework using seven species distribution models (SDMs), five climate scenarios and three emission and land‐use scenarios. We then compared results from SDMs using climate‐only variables, habitat‐only variables or both climate and habitat variables. In order to account for a species’ dispersal abilities, we used natal dispersal estimates and developed a probabilistic method that produced a dispersal scenario intermediate between the null and full dispersal scenarios generally considered in such studies. We then compared results from all scenarios in terms of future predicted range changes, range shifts, and variations in species richness. Modeling accuracy was better with climate‐only variables than with habitat‐only variables, and better with both climate and habitat variables. Habitat models predicted smaller range shifts and smaller variations in range size and species richness than climate models. Using both climate and habitat variables, it was predicted that the range of 71% of the species would decrease by 2050, with a 335 km median shift. Predicted variations in species richness showed large decreases in the southern regions of Europe, as well as increases, mainly in Scandinavia and northern Russia. The partial dispersal scenario was significantly different from the full dispersal scenario for 25% of the species, resulting in the local reduction of the future predicted species richness of up to 10%. We concluded that the breeding range of most European birds will decrease in spite of dispersal abilities close to a full dispersal hypothesis, and that given the contrasted predictions obtained when modeling climate change only and land‐use change only, both scenarios must be taken into consideration.     

Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal‐dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long‐lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species–environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species’ traits.     

Contribution of disturbance to distribution and abundance in a fire‐adapted system

Species distribution modeling (SDM) is an essential tool in understanding species ranges, but models haven't incorporated disturbance‐related variables. This is true even for regions where long histories of disturbance have resulted in disturbance‐adapted species. Therefore, the degree to which including disturbance‐related variables in SDMs might improve their performance is unclear. We used hierarchical partitioning to determine how fire patterns contribute to variation in species abundance and presence, examining both the total variation disturbance‐related variables explained, and how much of this variation is independent of soil and climate variables. For 27 Proteaceae species in the fire‐adapted Cape Floristic Region of South Africa , we found that fire variability, frequency, and area burned tended to have explanatory power similar in size to that of soil and climate variables. Importantly, for SDMs of abundance, fire‐related variables explained additional variation not captured by climatic variables, resulting in markedly increased model performance. In systems with high disturbance rates, species are less likely to be in equilibrium with their environment, and SDMs including variables describing disturbance regimes may be better able to capture the probability of a species being present at a site. Finally, the differential effect of fire on species abundance and presence suggests functional differences between these responses, which could hamper attempts to make predictions about species abundances using models of presence.     

How to tell a shrub from a tree: A life‐history perspective from a South African savanna

The ecological differences between ‘shrubs’ and ‘trees’ are surprisingly poorly understood and clear ecological definitions of these two constructs do not exist. It is not clear whether a shrub is simply a small tree or whether shrubs represent a distinct life‐history strategy. This question is of special interest in African savannas, where shrubs and trees often co‐dominate, but are often treated uniformly as ‘woody plants’ even though the tree to shrub ratio is an important determinant of ecosystem functioning. In this study we use data from a long‐term fire experiment, together with a trait‐based approach to test (i) if woody species usually classified as shrubs or trees in African savanna differ in key traits related to disturbance and resource use; and (ii) if these differences justify the interpretation of the two growth forms as distinct life‐history strategies. We measured for 22 of the most common woody plant species of a South African savanna 27 plant traits related to plant architecture, life‐history, leaf characteristics, photosynthesis and resprouting capacity. Furthermore we evaluated their performance during a long‐term fire experiment. We found that woody plants authors call (i) shrubs; (ii) shrubs sometimes small trees; and (3) trees responded differently to long‐term fire treatments. We additionally found significant differences in architecture, diameter‐height‐allometry, foliage density, resprouting vigour after fire, minimum fruiting height and foliar δ¹³C between these three woody plant types. We interpret these findings as evidence for at least two different life‐history‐strategies: an avoidance/adaptation strategy for shrubs (early reproduction + adaptation to minor disturbance) and an escape strategy for trees (promoted investment in height growth + delayed reproduction).     

Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors

Prediction maps produced by species distribution models (SDMs) influence decision‐making in resource management or designation of land in conservation planning. Many studies have compared the prediction accuracy of different SDM modeling methods, but few have quantified the similarity among prediction maps. There has also been little systematic exploration of how the relative importance of different predictor variables varies among model types and affects map similarity. Our objective was to expand the evaluation of SDM performance for 45 plant species in southern California to better understand how map predictions vary among model types, and to explain what factors may affect spatial correspondence, including the selection and relative importance of different environmental variables. Four types of models were tested. Correlation among maps was highest between generalized linear models (GLMs) and generalized additive models (GAMs) and lowest between classification trees and GAMs or GLMs. Correlation between Random Forests (RFs) and GAMs was the same as between RFs and classification trees. Spatial correspondence among maps was influenced the most by model prediction accuracy (AUC) and species prevalence; map correspondence was highest when accuracy was high and prevalence was intermediate (average prevalence for all species was 0.124). Species functional type and the selection of climate variables also influenced map correspondence. For most (but not all) species, climate variables were more important than terrain or soil in predicting their distributions. Environmental variable selection varied according to modeling method, but the largest differences were between RFs and GLMs or GAMs. Although prediction accuracy was equal for GLMs, GAMs, and RFs, the differences in spatial predictions suggest that it may be important to evaluate the results of more than one model to estimate the range of spatial uncertainty before making planning decisions based on map outputs. This may be particularly important if models have low accuracy or if species prevalence is not intermediate.     

Integrating anthropogenic factors into regional‐scale species distribution models—A novel application in the imperiled sagebrush biome

Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: ?6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: ?47.9%, ?41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species.     

Forecasting the future of biodiversity: a test of single‐ and multi‐species models for ants in North America

The geographic distributions of many taxonomic groups remain mostly unknown, hindering attempts to investigate the response of the majority of species on Earth to climate change using species distributions models (SDMs). Multi‐species models can incorporate data for rare or poorly‐sampled species, but their application to forecasting climate change impacts on biodiversity has been limited. Here we compare forecasts of changes in patterns of ant biodiversity in North America derived from ensembles of single‐species models to those from a multi‐species modeling approach, Generalized Dissimilarity Modeling (GDM). We found that both single‐ and multi‐species models forecasted large changes in ant community composition in relatively warm environments. GDM predicted higher turnover than SDMs and across a larger contiguous area, including the southern third of North America and notably Central America, where the proportion of ants with relatively small ranges is high and where data limitations are most likely to impede the application of SDMs. Differences between approaches were also influenced by assumptions regarding dispersal, with forecasts being more similar if no‐dispersal was assumed. When full‐dispersal was assumed, SDMs predicted higher turnover in southern Canada than did GDM. Taken together, our results suggest that 1) warm rather than cold regions potentially could experience the greatest changes in ant fauna under climate change and that 2) multi‐species models may represent an important complement to SDMs, particularly in analyses involving large numbers of rare or poorly‐sampled species. Comparisons of the ability of single‐ and multi‐species models to predict observed changes in community composition are needed in order to draw definitive conclusions regarding their application to investigating climate change impacts on biodiversity.     

Static species distribution models in dynamically changing systems: how good can predictions really be?

It is widely acknowledged that species respond to climate change by range shifts. Robust predictions of such changes in species’ distributions are pivotal for conservation planning and policy making, and are thus major challenges in ecological research. Statistical species distribution models (SDMs) have been widely applied in this context, though they remain subject to criticism as they implicitly assume equilibrium, and incorporate neither dispersal, demographic processes nor biotic interactions explicitly. In this study, the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections were tested. A spatially explicit multi‐species dynamic population model was built, incorporating species‐specific and interspecific ecological processes, environmental stochasticity and climate change. Species distributions were sampled in different scenarios, and SDMs were estimated by applying generalised linear models (GLMs) and boosted regression trees (BRTs). Resulting model performances were related to prevailing ecological processes and temporal dynamics. SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far‐dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short‐dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.     

Biotic and abiotic variables show little redundancy in explaining tree species distributions

Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large‐scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (~9%) compared to the contribution of each predictor set individually (~20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo‐climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.     

Simulating climate change impacts on forests and associated vascular epiphytes in a subtropical island of East Asia

Aim This study aims to assess the impact of climate change on forests and vascular epiphytes, using species distribution models (SDMs). Location Island of Taiwan, subtropical East Asia. Methods A hierarchical modelling approach incorporating forest migration velocity and forest type–epiphyte interactions with classical SDMs was used to model the responses of eight forest types and 237 vascular epiphytes for the year 2100 under two climate change scenarios. Forest distributions were modelled and modified by dominant tree species’ dispersal limitations and hypothesized persistence under unfavourable climate conditions (20 years for broad‐leaved trees and 50 years for conifers). The modelled forest projections together with 16 environmental variables were used as predictors in models of epiphyte distributions. A null method was applied to validate the significance of epiphyte SDMs, and potential vulnerable species were identified by calculating range turnover rates. Results For the year 2100, the model predicted a reduction in the range of most forest types, especially for Picea and cypress forests, which shifted to altitudes c. 400 and 300 m higher, respectively. The models indicated that epiphyte distributions are highly correlated with forest types, and the majority (77–78%) of epiphyte species were also projected to lose 45–58% of their current range, shifting on average to altitudes c. 400 m higher than currently. Range turnover rates suggested that insensitive epiphytes were generally lowland or widespread species, whereas sensitive species were more geographically restricted, showing a higher correlation with temperature‐related factors in their distributions. Main conclusions The hierarchical modelling approach successfully produced interpretable results, suggesting the importance of considering biotic interactions and the inclusion of terrain‐related factors when developing SDMs for dependant species at a local scale. Long‐term monitoring of potentially vulnerable sites is advised, especially of those sites that fall outside current conservation reserves where additional human disturbance is likely to exacerbate the effect of climate change.     

Life-history traits are correlated with geographical distribution patterns of western European forest herb species

Aim To investigate whether six plant life‐history traits that have been related to colonization ability at local scales are also related to the geographical range characteristics of 273 forest plant species. Location Continental western Europe, five countries in particular: France, Luxemburg, Belgium, the Netherlands and Germany. The region is situated between 42° and 55°N and 5°W and 15°E and has a summed total area of 971,404 km². Methods Distribution data were compiled from five national data bases and converted to a 10′ grid. Life‐history traits were taken from existing compilations of autecological information of European species. The spatial arrangement of occupied grid cells was investigated using Ripley's K. Cross‐species correlations and phylogenetically independent contrasts were used to investigate the relationships between plant life‐history traits and three range characteristics: area of occupancy, latitudinal extent and centroid latitude. Results For herbaceous species, seed dispersal mode, seed production and seed bank longevity exhibited significant associations with geographical range characteristics, including area of occupancy. Woody plant species exhibited fewer significant associations, although maximum height was positively associated with range centroid latitude within the study area. Furthermore, the ranges of species with limited dispersal ability were found to be more clustered than the ranges of species with morphological adaptations for long‐distance seed dispersal. Main conclusions For western European forest plant species, life‐history traits that are related to colonization ability at local scales are associated with variation in large‐scale geographical range characteristics. This finding implies that the distributions of some forest plant species in the study area may be limited by seed dispersal and colonization capacity rather than climate or other environmental factors.     

Fire severity mediates climate‐driven shifts in understorey community composition of black spruce stands of interior Alaska

Question: How do pre‐fire conditions (community composition and environmental characteristics) and climate‐driven disturbance characteristics (fire severity) affect post‐fire community composition in black spruce stands? Location: Northern boreal forest, interior Alaska. Methods: We compared plant community composition and environmental stand characteristics in 14 black spruce stands before and after multiple, naturally occurring wildfires. We used a combination of vegetation table sorting, univariate (ANOVA, paired t‐tests), and multivariate (detrended correspondence analysis) statistics to determine the impact of fire severity and site moisture on community composition, dominant species and growth forms. Results: Severe wildfires caused a 50% reduction in number of plant species in our study sites. The largest species loss, and therefore the greatest change in species composition, occurred in severely burned sites. This was due mostly to loss of non‐vascular species (mosses and lichens) and evergreen shrubs. New species recruited most abundantly to severely burned sites, contributing to high species turnover on these sites. As well as the strong effect of fire severity, pre‐fire and post‐fire mineral soil pH had an effect on post‐fire vegetation patterns, suggesting a legacy effect of site acidity. In contrast, pre‐fire site moisture, which was a strong determinant of pre‐fire community composition, showed no relationship with post‐fire community composition. Site moisture was altered by fire, due to changes in permafrost, and therefore post‐fire site moisture overrode pre‐fire site moisture as a strong correlate. Conclusions: In the rapidly warming climate of interior Alaska, changes in fire severity had more effect on post‐fire community composition than did environmental factors (moisture and pH) that govern landscape patterns of unburned vegetation. This suggests that climate change effects on future community composition of black spruce forests may be mediated more strongly by fire severity than by current landscape patterns. Hence, models that represent the effects of climate change on boreal forests could improve their accuracy by including dynamic responses to fire disturbance.     

Role of plant functional traits in determining vegetation composition of abandoned grazing land in north‐eastern Victoria,Australia

Question: In the Northern Hemisphere, species with dispersal limitations are typically absent from secondary forests. In Australia, little is known about dispersal mechanisms and other traits that drive species composition within post‐agricultural, secondary forest. We asked whether mode of seed dispersal, nutrient uptake strategy, fire response, and life form in extant vegetation differ according to land‐use history. We also asked whether functional traits of Australian species that confer tolerance to grazing and re‐colonisation potential differ from those in the Northern Hemisphere. Location: Delatite Peninsula, NE Victoria, Australia. Methods: The vegetation of primary and secondary forests was surveyed using a paired‐plot design. Eight traits were measured for all species recorded. ANOSIM tests and Non‐metric Multi‐dimensional Scaling were used to test differences in the abundance of plant attributes between land‐use types. Results: Land‐use history had a significant effect on vegetation composition. Specific leaf area (SLA) proved to be the best predictor of response to land‐use change. Primary forest species were typically myrmecochorous phanerophytes with low SLA. In the secondary forest, species were typically therophytes with epizoochorous dispersal and high SLA. Conclusions: The attributes of species in secondary forests provide tolerance to grazing suggesting that disturbance caused by past grazing activity determined the composition of these forests. Myrmecochores were rare in secondary forests, suggesting that species had failed to re‐colonise due to dispersal limitations. Functional traits that resulted in species loss through disturbance and prevented re‐colonisation were different to those in the Northern Hemisphere and were attributable to the sclerophyllous nature of the primary forest.     

Response of plant species and life form diversity to variable fire histories and biomass in the jarrah forest of south‐west Australia

Frequent fires reduce the abundance of woody plant species and favour herbaceous species. Plant species richness also tends to increase with decreasing vegetation biomass and cover due to reduced competition for light. We assessed the influence of variable fire histories and site biomass on the following diversity measures: woody and herbaceous species richness, overall species richness and evenness, and life form evenness (i.e. the relative abundance or dominance among six herbaceous and six woody plant life forms), across 16 mixed jarrah (Eucalyptus marginata) and marri (Corymbia calophylla) forest stands in south‐west Australia. Fire frequency was defined as the total number of fires over a 30‐year period. Overall species richness and species evenness did not vary with fire frequency or biomass. However, there were more herbaceous species (particularly rushes, geophytes and herbs) where there were fewer shrubs and low biomass, suggesting that more herbaceous species coexist where dominance by shrubs is low. Frequently burnt plots also had lower number and abundance of shrub species. Life form evenness was also higher at both high fire frequency and low biomass sites. These results suggest that the impact of fire frequency and biomass on vegetation composition is mediated by local interactions among different life forms rather than among individual species. Our results demonstrate that measuring the variation in the relative diversity of different woody and herbaceous life forms is crucial to understanding the compositional response of forests and other structurally complex vegetation communities to changes in disturbance regime such as increased fire frequency.     

Evaluation of species distribution models by resampling of sites surveyed a century ago by Joseph Grinnell

Species distribution models (SDMs) are commonly applied to predict species’ responses to anticipated global change, but lack of data from future time periods precludes assessment of their reliability. Instead, performance against test data in the same era is assumed to correlate with accuracy in the future. Moreover, high‐confidence absence data is required for testing model accuracy but is often unavailable since a species may be present when undetected. Here we evaluate the performance of eight SDMs trained with historic (1900–1939) or modern (1970–2009) climate data and occurrence records for 18 mammalian species. Models were projected to the same or the opposing time period and evaluated with data obtained from surveys conducted by Joseph Grinnell and his colleagues in the Sierra Nevada of California from 1900 to 1939 and modern resurveys from 2003 to 2011. Occupancy modeling was used to confidently assign absences at test sites where species were undetected. SDMs were evaluated using species’ presences combined with this high‐confidence absence (HCA) set, a low‐confidence set in which non‐detections were assumed to indicate absence (LCA), and randomly located ‘pseudoabsences’ (PSA). Model performance increased significantly with the quality of absences (mean AUC ± SE: 0.76 ± 0.01 for PSA, 0.79 ± 0.01 for LCA, and 0.81 ± 0.01 for HCA), and apparent differences between SDMs declined as the quality of test absences increased. Models projecting across time performed as well as when projecting within the same time period when assessed with threshold‐independent metrics. However, accuracy of presence and absence predictions sometimes declined in cross‐era projections. Although most variation in performance occurred among species, autecological traits were only weakly correlated with model accuracy. Our study indicates that a) the quality of evaluation data affects assessments of model performance; b) within‐era performance correlates positively but unreliably with cross‐era performance; and c) SDMs can be reliably but cautiously projected across time.     

Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important?

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus‐field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an information‐theoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co‐occurrences are a stronger driver of plant distributions than plant–bacteria co‐occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.     

Do ecological differences between taxonomic groups influence the relationship between species’ distributions and climate? A global meta‐analysis using species distribution models

Understanding whether and how ecological traits affect species’ geographic distributions is a fundamental issue that bridges ecology and biogeography. While climate is thought to be the major determinant of species’ distributions, there is considerable variation in the strength of species’ climate–distribution relationships. One potential explanation is that species with relatively low dispersal ability cannot reach all geographic areas where climatic conditions are suitable. We tested the hypothesis that species from different taxonomic groups varied in their climate–distribution relationships because of differences in life history strategies, in particular dispersal ability. We conducted a meta‐analysis by combining the discrimination ability (AUC values) from 4317 species distribution models (SDMs) using fit as an indication of the strength of the species’ climate–distribution relationship. We found significant differences in the strength of species’ climate–distribution relationships across taxonomic groups, however we did not find support for the dispersal hypothesis. Our results suggest that relevant ecological trait variation among broad taxonomic groups may be related to differences in species’ climate–distribution relationships, however which ecological traits are important remains unclear.     

Benchmarking novel approaches for modelling species range dynamics

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species’ range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process‐based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process‐based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species’ response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.     

Life‐history traits predict perennial species response to fire in a desert ecosystem

The Mojave Desert of North America has become fire‐prone in recent decades due to invasive annual grasses that fuel wildfires following years of high rainfall. Perennial species are poorly adapted to fire in this system, and post‐fire shifts in species composition have been substantial but variable across community types. To generalize across a range of conditions, we investigated whether simple life‐history traits could predict how species responded to fire. Further, we classified species into plant functional types (PFTs) based on combinations of life‐history traits and evaluated whether these groups exhibited a consistent fire‐response. Six life‐history traits varied significantly between burned and unburned areas in short (up to 4 years) or long‐term (up to 52 years) post‐fire datasets, including growth form, lifespan, seed size, seed dispersal, height, and leaf longevity. Forbs and grasses consistently increased in abundance after fire, while cacti were reduced and woody species exhibited a variable response. Woody species were classified into three PFTs based on combinations of life‐history traits. Species in Group 1 increased in abundance after fire and were characterized by short lifespans, small, wind‐dispersed seeds, low height, and deciduous leaves. Species in Group 2 were reduced by fire and distinguished from Group 1 by longer lifespans and evergreen leaves. Group 3 species, which also decreased after fire, were characterized by long lifespans, large non‐wind dispersed seeds, and taller heights. Our results show that PFTs based on life‐history traits can reliably predict the responses of most species to fire in the Mojave Desert. Dominant, long‐lived species of this region possess a combination of traits limiting their ability to recover, presenting a clear example of how a novel disturbance regime may shift selective environmental pressures to favor alternative life‐history strategies.