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.     

Biotic predictors with phenological information improve range estimates for migrating monarch butterflies in Mexico

Although long-standing theory suggests that biotic variables are only relevant at local scales for explaining the patterns of species' distributions, recent studies have demonstrated improvements to species distribution models (SDMs) by incorporating predictor variables informed by biotic interactions. However, some key methodological questions remain, such as which kinds of interactions are permitted to include in these models, how to incorporate the effects of multiple interacting species, and how to account for interactions that may have a temporal dependence. We addressed these questions in an effort to model the distribution of the monarch butterfly Danaus plexippus during its fall migration (September–November) through Mexico, a region with new monitoring data and uncertain range limits even for this well-studied insect. We estimated species richness of selected nectar plants (Asclepias spp.) and roosting trees (various highland species) for use as biotic variables in our models. To account for flowering phenology, we additionally estimated nectar plant richness of flowering species per month. We evaluated three types of models: climatic variables only (abiotic), plant richness estimates only (biotic) and combined (abiotic and biotic). We selected models with AICc and additionally determined if they performed better than random on spatially withheld data. We found that the combined models accounting for phenology performed best for all three months, and better than random for discriminatory ability but not omission rate. These combined models also produced the most ecologically realistic spatial patterns, but the modeled response for nectar plant richness matched ecological predictions for November only. These results represent the first model-based monarch distributional estimates for the Mexican migration route and should provide foundations for future conservation work. More generally, the study demonstrates the potential benefits of using SDM-derived richness estimates and phenological information for biotic factors affecting species distributions.     

The abiotic and biotic factors limiting establishment of predatory fishes at their expanding northern range boundaries in Ontario,Canada

There is a poor understanding of the importance of biotic interactions in determining species distributions with climate change. Theory from invasion biology suggests that the success of species introductions outside of their historical ranges may be either positively (biotic acceptance) or negatively (biotic resistance) related to native biodiversity. Using data on fish community composition from two survey periods separated by approximately 28 years during which climate was warming, we examined the factors influencing the establishment of three predatory centrarchids: Smallmouth Bass (Micropterus dolomieu), Largemouth Bass (M. salmoides), and Rock Bass (Ambloplites rupestris) in lakes at their expanding northern range boundaries in Ontario. Variance partitioning demonstrated that, at a regional scale, abiotic factors play a stronger role in determining the establishment of these species than biotic factors. Pairing lakes within watersheds where each species had established with lakes sharing similar abiotic conditions where the species had not established revealed both positive and negative relationships between the establishment of centrarchids and the historical presence of other predatory species. The establishment of these species near their northern range boundaries is primarily determined by abiotic factors at a regional scale; however, biotic factors become important at the lake‐to‐lake scale. Studies of exotic species invasions have previously highlighted how spatial scale mediates the importance of abiotic vs. biotic factors on species establishment. Our study demonstrates how concepts from invasion biology can inform our understanding of the factors controlling species distributions with changing climate.     

Improving species distribution models using biotic interactions: a case study of parasites,pollinators and plants

Biotic interactions have been considered as an important factor to be included in species distribution modelling, but little is known about how different types of interaction or different strategies for modelling affect model performance. This study compares different methods for including interspecific interactions in distribution models for bees, their brood parasites, and the plants they pollinate. Host–parasite interactions among bumble bees (genus Bombus: generalist pollinators and brood parasites) and specialist plant–pollinator interactions between Centris bees and Krameria flowers were used as case studies. We used 7 different modelling algorithms available in the BIOMOD R package. For Bombus, the inclusion of interacting species distributions generally increased model predictive accuracy. The improvement was better when the interacting species was included with its raw distribution rather than with its modeled suitability. However, incorporating the distributions of non‐interacting species sometimes resulted in similarly increased model accuracy despite their being no significance of any interaction for the distribution. For the Centris‐Krameria system the best strategy for modelling biotic interactions was to include the interacting species model‐predicted values. However, the results were less consistent than those for Bombus species, and most models including biotic interactions showed no significant improvement over abiotic models. Our results are consistent with previous studies showing that biotic interactions can be important in structuring species distributions at regional scales. However, correlations between species distributions are not necessarily indicative of interactions. Therefore, choosing the correct biotic information, based on biological and ecological knowledge, is critical to improve the accuracy of species distribution models and forecast distribution change.     

Taxonomy-testing and the ‘Goldilocks Hypothesis’: morphometric analysis of species diversity in living and extinct Hispaniolan hutias

Understanding the dynamics of the Late Quaternary Caribbean mammal extinction event is complicated by continuing uncertainty over the taxonomic status of many species. Hispaniola is one of the few Caribbean islands to retain native non-volant mammals; however, there has been little consensus over past or present levels of diversity in Hispaniolan hutias (Capromyidae: Plagiodontinae). Craniodental measurement data from modern hutia specimens, previously classified as both Plagiodontia aedium and P. hylaeum, display morphological differences between Hispaniola's northern and southern palaeo-islands using MANOVA and PCA. Although attempts to amplify mitochondrial DNA from the holotype of P. aedium were unsuccessful, this specimen is morphometrically associated with southern palaeo-island specimens. The mandibular size distribution of recent Plagiodontia specimens is unimodal, but the Late Quaternary mandibular size distribution is multimodal and displays much broader measurement spread, representing multiple extinct species. Finite Mixture Analysis was used to assess the best fit of different taxonomic hypotheses to the fossil mandibular size distribution. All retained FMA models include living hutias and P. spelaeum as distinct taxa; PCA further demonstrates that levels of morphological variation between modern hutia populations are lower than levels between living hutias and P. spelaeum, so that living hutias are interpreted as the single species P. aedium. Taxonomic differentiation for larger-bodied hutias is less well defined, but most retained models show only one larger species, for which the only available name is P. velozi. ‘Plagiodontia’ araeum is morphologically distinct from other species and is reassigned to Hyperplagiodontia. Hispaniola's plagiodontine fauna has lost its largest and smallest representatives; similar trends of body size selectivity in extinction risk are shown more widely across the Caribbean mammal fauna, possibly due to different regional anthropogenic threats (invasive mammals, hunting) affecting small-bodied and large-bodied mammals during the recent past. This apparent pattern of extinction selectivity is named the ‘Goldilocks Hypothesis’.     

Cultural and climatic changes shape the evolutionary history of the Uralic languages

Quantitative phylogenetic methods have been used to study the evolutionary relationships and divergence times of biological species, and recently, these have also been applied to linguistic data to elucidate the evolutionary history of language families. In biology, the factors driving macroevolutionary processes are assumed to be either mainly biotic (the Red Queen model) or mainly abiotic (the Court Jester model) or a combination of both. The applicability of these models is assumed to depend on the temporal and spatial scale observed as biotic factors act on species divergence faster and in smaller spatial scale than the abiotic factors. Here, we used the Uralic language family to investigate whether both ‘biotic’ interactions (i.e. cultural interactions) and abiotic changes (i.e. climatic fluctuations) are also connected to language diversification. We estimated the times of divergence using Bayesian phylogenetics with a relaxed‐clock method and related our results to climatic, historical and archaeological information. Our timing results paralleled the previous linguistic studies but suggested a later divergence of Finno‐Ugric, Finnic and Saami languages. Some of the divergences co‐occurred with climatic fluctuation and some with cultural interaction and migrations of populations. Thus, we suggest that both ‘biotic’ and abiotic factors contribute either directly or indirectly to the diversification of languages and that both models can be applied when studying language evolution.     

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.     

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

Species distribution models (SDMs) have been widely used in the scientific literature. The majority of SDMs use climate data or other abiotic variables to forecast the potential distribution of a species in geographic space. Biotic interactions can affect the predicted spatial distribution of a species in many ways across multiple spatial scales, and incorporating these predictors in an SDM is a current topic in the scientific literature. Constrictotermes cyphergaster is a widely distributed termite in the Neotropics. This termite species nests in plants and more frequently nests in some arboreal species. Thus, this species is an excellent model to evaluate the influence of biotic interactions in SDMs. We evaluate the influences of climate and the geographic distribution of host plants on the potential distribution of C. cyphergaster. Three correlative models (MaxEnt) were built to predict the geographic distribution of the termite: (1) climate data, (2) biotic data (i.e., the geographic distribution of host plants), and (3) climate and biotic data. The models that were generated indicate that the potential geographic distribution of C. cyphergaster is concentrated in the Cerrado and Caatinga regions. In addition, path analysis and multiple regression revealed the importance of the direct effects of biological interactions in the geographic distribution of the termite, while climate affected the distribution of the termite mainly through indirect effects by influencing the geographic distributions of host plants. The current study endorses the importance of including biological interactions in SDMs. We recommend using biotic predictors in SDM studies of insect species, mainly because insects have important environmental services and biotic interaction data can improve the macroecological studies of this group.     

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.     

Invasion biology in non‐free‐living species: interactions between abiotic (climatic) and biotic (host availability) factors in geographical space in crayfish commensals (Ostracoda,Entocytheridae)

In invasion processes, both abiotic and biotic factors are considered essential, but the latter are usually disregarded when modeling the potential spread of exotic species. In the framework of set theory, interactions between biotic (B), abiotic (A), and movement‐related (M) factors in the geographical space can be hypothesized with BAM diagrams and tested using ecological niche models (ENMs) to estimate A and B areas. The main aim of our survey was to evaluate the interactions between abiotic (climatic) and biotic (host availability) factors in geographical space for exotic symbionts (i.e., non‐free‐living species), using ENM techniques combined with a BAM framework and using exotic Entocytheridae (Ostracoda) found in Europe as model organisms. We carried out an extensive survey to evaluate the distribution of entocytherids hosted by crayfish in Europe by checking 94 European localities and 12 crayfish species. Both exotic entocytherid species found, Ankylocythere sinuosa and Uncinocythere occidentalis, were widely distributed in W Europe living on the exotic crayfish species Procambarus clarkii and Pacifastacus leniusculus, respectively. No entocytherids were observed in the remaining crayfish species. The suitable area for A. sinuosa was mainly restricted by its own limitations to minimum temperatures in W and N Europe and precipitation seasonality in circum‐Mediterranean areas. Uncinocythere occidentalis was mostly restricted by host availability in circum‐Mediterranean regions due to limitations of P. leniusculus to higher precipitation seasonality and maximum temperatures. The combination of ENMs with set theory allows studying the invasive biology of symbionts and provides clues about biogeographic barriers due to abiotic or biotic factors limiting the expansion of the symbiont in different regions of the invasive range. The relative importance of abiotic and biotic factors on geographical space can then be assessed and applied in conservation plans. This approach can also be implemented in other systems where the target species is closely interacting with other taxa.     

Factors linked to interannual variation in the metazoan parasite communities of black skipjack,Euthynnus lineatus (Pisces: Scombridae)

Marine parasite communities can exhibit temporal and spatial changes in response to seasonal and local variations in several biotic and abiotic environmental factors. Limited attention has been given to the influence of abiotic factors, so their effects on parasite community structure remain unclear. A total of 496 specimens of Euthynnus lineatus were collected over a 7‐year period (2012–2018) from Acapulco Bay, Mexico. Their parasite communities were analyzed to determine if they experience interannual variations due to local biotic and abiotic factors. Thirty‐three metazoan parasite species were recovered and identified: four species of Monogenea (adults); 16 of Digenea (one larvae and 15 adults); two of Acanthocephala (adults); two of Cestoda (larvae); three of Nematoda (two larvae and one adult); and six of Crustacea (three Copepoda, and three Isopoda). Species richness was greatest among the digeneans, which represented 48% of the total species recovered, followed by the crustaceans (19% of total species). Species richness at the component community level (14–24 species) was similar to reported richness in other small tuna species. The component communities and infracommunities of E. lineatus exhibited a similar pattern: high species richness and diversity, and numerical dominance by a single species, mainly by one of the didymozoids Allopseudocolocyntotrema claviforme or Pseudocolocyntotrema yaito. Parasite community structure and species composition varied among sampling years. Variations were possibly caused by a combination of abiotic and biotic factors which generated notable changes in the infection levels of several component species during the study period. These communities may therefore be unpredictable in terms of structure and species composition, as has been suggested for other communities of marine parasites.     

Complementary strengths of spatially-explicit and multi-species distribution models

Species distribution models (SDMs) project the outcome of community assembly processes – dispersal, the abiotic environment and biotic interactions – onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock Tsuga canadensis and five additional co-occurring overstory tree species in 35 569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability. Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species–environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.     

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Species distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.     

Dietary complexity and hidden costs of prey switching in a generalist top predator

1. Variation in predator diet is a critical aspect of food web stability, health, and population dynamics of predator/ prey communities. Quantifying diet, particularly among cryptic species, is extremely challenging, however, and differentiation between demographic subsets of populations is often overlooked.
2. We used prey remains and data taken postmortem from otter Lutra lutra to determine the extent to which dietary variation in a top predator was associated with biotic, spatial, and temporal factors.
3. Biotic data (e.g., sex, weight, and length) and stomach contents were taken from 610 otters found dead across England and Wales between 1994 and 2010. Prey remains were identified to species where possible, using published keys and reference materials. Multi‐model inference followed by model prediction was applied to test for and visualize the nature of associations.
4. Evidence for widespread decline in the consumption of eels (Anguilla anguilla) reflected known eel population declines. An association between eel consumption and otter body condition suggested negative consequences for otter nutrition. Consumption of Cottus gobio and stickleback spp. increased, but was unlikely to compensate (there was no association with body condition). More otters with empty stomachs were found over time. Otter sex, body length, and age‐class were important biotic predictors of the prey species found, and season, region, and distance from the coast were important abiotic predictors.
5. Our study is unique in its multivariate nature, broad spatial scale, and long‐term dataset. Inclusion of biotic data allowed us to reveal important differences in costs and benefits of different prey types, and differences between demographic subsets of the population, overlaid on spatial and temporal variation. Such complexities in otter diet are likely to be paralleled in other predators, and detailed characterization of diet should not be overlooked in efforts to conserve wild populations.

     

Reproductive phenology of Melastomataceae species with contrasting reproductive systems: contemporary and historical drivers

• Flowering and fruiting are key events in the life history of plants, and both are critical to their reproductive success. Besides the role of evolutionary history, plant reproductive phenology is regulated by abiotic factors and shaped by biotic interactions with pollinators and seed dispersers. In Melastomataceae, a dominant Neotropical family, the reproductive systems vary from allogamous with biotic pollination to apomictic, and seed dispersal varies from dry (self‐dispersed) to fleshy (animal‐dispersed) fruits. Such variety in reproductive strategies is likely to affect flowering and fruiting phenologies.
• In this study, we described the reproductive phenology of 81 Melastomataceae species occurring in two biodiversity hotspots: the Atlantic rain forest and the campo rupestre. We aim to disentangle the role of abiotic and biotic factors defining flowering and fruiting times of Melastomataceae species, considering the contrasting breeding and seed dispersal systems, and their evolutionary history.
• In both vegetation types, pollinator‐dependent species had higher flowering seasonality than pollinator‐independent ones. Flowering patterns presented phylogenetic signal regardless of vegetation type. Fruiting of fleshy‐fruited species was seasonal in campo rupestre but not in Atlantic rain forest; the fruiting of dry‐fruited species was also not seasonal in both vegetation types. Fruiting showed a low phylogenetic signal, probably because the influence of environment and dispersal agents on fruiting time is stronger than the phylogenetic affinity.
• Considering these ecophylogenetic patterns, our results indicate that flowering may be shaped by the different reproductive strategies of Melastomataceae lineages, while fruiting patterns may be governed mainly by the seed dispersal strategy and flowering time, with less phylogenetic influence.

     

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.     

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km² to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.     

Rodents of the Caribbean: origin and diversification of hutias unravelled by next-generation museomics

The Capromyidae (hutias) are endemic rodents of the Caribbean and represent a model of dispersal for non-flying mammals in the Greater Antilles. This family has experienced severe extinctions during the Holocene and its phylogenetic affinities with respect to other caviomorph relatives are still debated as morphological and molecular data disagree. We used target enrichment and next-generation sequencing of mitochondrial and nuclear genes to infer the phylogenetic relationships of hutias, estimate their divergence ages, and understand their mode of dispersal in the Greater Antilles. We found that Capromyidae are nested within Echimyidae (spiny rats) and should be considered a subfamily thereof. We estimated that the split between hutias and Atlantic Forest spiny rats occurred 16.5 (14.8–18.2) million years ago (Ma), which is more recent than the GAARlandia land bridge hypothesis (34–35 Ma). This would suggest that during the Early Miocene, an echimyid-like ancestor colonized the Greater Antilles from an eastern South American source population via rafting. The basal divergence of the Hispaniolan Plagiodontia provides further support for a vicariant separation between Hispaniolan and western islands (Bahamas, Cuba, Jamaica) hutias. Recent divergences among these western hutias suggest Plio-Pleistocene dispersal waves associated with glacial cycles.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

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.