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.     

A simple method to estimate the probable distribution of species

Species distribution models (SDMs) are broadly used to predict species distributions from available presence data. However, SDMs results have been criticized for several reasons mainly related to two basic characteristics of most SDMs: 1) general lack of reliable species absence information, 2) the frequent use of an arbitrary geographical extent (GE) or accessible area of the species. These impediments have motivated us to generate a procedure called niche of occurrence (NOO). NOO provides the probable distribution of species (realized niche) relying solely on partial information about presence of species. It operates within a natural geographical extent delimited by available observations and avoids using misleading thresholds to obtain binary presence–absence estimations when the species prevalence is unknown. In this study the main characteristics of NOO are presented, comparing its performance with other recognized and more complex SDMs by using virtual species to avoid the omnipresent error sources of real data sets.     

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.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.     

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.     

Bio‐ORACLE: a global environmental dataset for marine species distribution modelling

Aim The oceans harbour a great diversity of organisms whose distribution and ecological preferences are often poorly understood. Species distribution modelling (SDM) could improve our knowledge and inform marine ecosystem management and conservation. Although marine environmental data are available from various sources, there are currently no user‐friendly, high‐resolution global datasets designed for SDM applications. This study aims to fill this gap by assembling a comprehensive, uniform, high‐resolution and readily usable package of global environmental rasters. Location Global, marine. Methods We compiled global coverage data, e.g. satellite‐based and in situ measured data, representing various aspects of the marine environment relevant for species distributions. Rasters were assembled at a resolution of 5 arcmin (c. 9.2 km) and a uniform landmask was applied. The utility of the dataset was evaluated by maximum entropy SDM of the invasive seaweed Codium fragile ssp. fragile. Results We present Bio‐ORACLE (ocean rasters for analysis of climate and environment), a global dataset consisting of 23 geophysical, biotic and climate rasters. This user‐friendly data package for marine species distribution modelling is available for download at http://www.bio‐oracle.ugent.be . The high predictive power of the distribution model of C. fragile ssp. fragile clearly illustrates the potential of the data package for SDM of shallow‐water marine organisms. Main conclusions The availability of this global environmental data package has the potential to stimulate marine SDM. The high predictive success of the presence‐only model of a notorious invasive seaweed shows that the information contained in Bio‐ORACLE can be informative about marine distributions and permits building highly accurate species distribution models.     

The roles of stochasticity and biotic interactions in the spatial patterning of plant species in alpine communities

Questions

Plant community composition can be influenced by multiple biotic, abiotic, and stochastic factors acting on the local species pool to determine their establishment success and abundance and subsequently the diversity of the community. We asked if the influences of biotic interactions on the composition of plant species in communities, as indicated by patterns of plant species spatial associations (independent, positive or negative), vary across a productivity gradient within a single ecosystem type. Do dominant species of communities show spatial patterning suggestive of competitive interactions with interspecific neighbors? Do species that span multiple community types exhibit the same heterospecific interactions with neighbours in each community?

Location

Three alpine communities in the southern Rocky Mountains.

Methods

We measured the occurrence of species in a 1‐cm spatial grid within 2 m × 2 m plots to determine the spatial patterns of species pairs in the three communities. A null model of independent species spatial arrangements was used to determine whether species pairs were positively, negatively or independently associated, and how these patterns differed among the communities across the gradient of resource supply and environmental stress.

Results

Positive associations, indicative of facilitation between species, were most common in the most resource‐poor and least productive community. However negative associations, suggestive of competitive interactions among species, were not more common in the two more resource‐rich, productive communities. The dominant species of these communities did exhibit higher negative than positive associations with neighbours relative to positive patterning. Independent interspecific patterning was equally common relative to positive and negative patterns in all communities. Species that previously were shown to either facilitate other species or compete with neighbours exhibited spatial patterning consistent with the earlier experimental work.

Conclusions

A large number of species exhibit a lack of net biotic interactions, and stochastic factors appear to be as important as competition and facilitation in shaping the structure of the three alpine plant communities we studied.

     

Habitat shifts of endangered species under altered climate conditions: importance of biotic interactions

Predicting changes in potential habitat for endangered species as a result of global warming requires considering more than future climate conditions; it is also necessary to evaluate biotic associations. Most distribution models predicting species responses to climate change include climate variables and occasionally topographic and edaphic parameters, rarely are biotic interactions included. Here, we incorporate biotic interactions into niche models to predict suitable habitat for species under altered climates. We constructed and evaluated niche models for an endangered butterfly and a threatened bird species, both are habitat specialists restricted to semiarid shrublands of southern California. To incorporate their dependency on shrubs, we first developed climate‐based niche models for shrubland vegetation and individual shrub species. We also developed models for the butterfly's larval host plants. Outputs from these models were included in the environmental variable dataset used to create butterfly and bird niche models. For both animal species, abiotic–biotic models outperformed the climate‐only model, with climate‐only models over‐predicting suitable habitat under current climate conditions. We used the climate‐only and abiotic–biotic models to calculate amounts of suitable habitat under altered climates and to evaluate species' sensitivities to climate change. We varied temperature (+0.6, +1.7, and +2.8 °C) and precipitation (50%, 90%, 100%, 110%, and 150%) relative to current climate averages and within ranges predicted by global climate change models. Suitable habitat for each species was reduced at all levels of temperature increase. Both species were sensitive to precipitation changes, particularly increases. Under altered climates, including biotic variables reduced habitat by 68–100% relative to the climate‐only model. To design reserve systems conserving sensitive species under global warming, it is important to consider biotic interactions, particularly for habitat specialists and species with strong dependencies on other species.     

Species interactions weakly modify climate‐induced tree co‐occurrence patterns

Aims

Species distributions are hypothesized to be underlain by a complex association of processes that span multiple spatial scales including biotic interactions, dispersal limitation, fine‐scale resource gradients and climate. Species disequilibrium with climate may reflect the effects of non‐climatic processes on species distributions, yet distribution models have rarely directly considered non‐climatic processes. Here, we use a Joint Species Distribution Model (JSDM) to investigate the influence of non‐climatic factors on species co‐occurrence patterns and to directly quantify the relative influences of climate and alternative processes that may generate correlated responses in species distributions, such as species interactions, on tree co‐occurrence patterns.

Location

US Rocky Mountains.

Methods

We apply a Bayesian JSDM to simultaneously model the co‐occurrence patterns of ten dominant tree species across the Rocky Mountains, and evaluate climatic and residual correlations from the fitted model to determine the relative contribution of each component to observed co‐occurrence patterns. We also evaluate predictions generated from the fitted model relative to a single‐species modelling approach.

Results

For most species, correlation due to climate covariates exceeded residual correlation, indicating an overriding influence of broad‐scale climate on co‐occurrence patterns. Accounting for covariance among species did not significantly improve predictions relative to a single‐species approach, providing limited evidence for a strong independent influence of species interactions on distribution patterns.

Conclusions

Overall, our findings indicate that climate is an important driver of regional biodiversity patterns and that interactions between dominant tree species contribute little to explain species co‐occurrence patterns among Rocky Mountain trees.     

The role of climate,water and biotic interactions in shaping biodiversity patterns in arid environments across spatial scales

Aim

Desert ecosystems, with their harsh environmental conditions, hold the key to understanding the responses of biodiversity to climate change. As desert community structure is influenced by processes acting at different spatial scales, studies combining multiple scales are essential for understanding the conservation requirements of desert biota. We investigated the role of environmental variables and biotic interactions in shaping broad and fine‐scale patterns of diversity and distribution of bats in arid environments to understand how the expansion of nondesert species can affect the long‐term conservation of desert biodiversity.

Location

Levant, Eastern Mediterranean.

Methods

We combine species distribution modelling and niche overlap statistics with a statistical model selection approach to integrate interspecific interactions into broadscale distribution models and fine‐scale analysis of ecological requirements. We focus on competition between desert bats and mesic species that recently expanded their distribution into arid environment following anthropogenic land‐use changes.

Results

We show that both climate and water availability limit bat distributions and diversity across spatial scales. The broadscale distribution of bats was determined by proximity to water and high temperatures, although the latter did not affect the distribution of mesic species. At the fine‐scale, high levels of bat activity and diversity were associated with increased water availability and warmer periods. Desert species were strongly associated with warmer and drier desert types. Range and niche overlap were high among potential competitors, but coexistence was facilitated through fine‐scale spatial partitioning of water resources.

Main conclusions

Adaptations to drier and warmer conditions allow desert‐obligate species to prevail in more arid environments. However, this competitive advantage may disappear as anthropogenic activities encroach further into desert habitats. We conclude that reduced water availability in arid environments under future climate change projections pose a major threat to desert wildlife because it can affect survival and reproductive success and may increase competition over remaining water resources.     

Modelled spatial distribution of marine fish and projected modifications in the North Atlantic Ocean

The objectives of this work were to examine the past, current and potential influence of global climate change on the spatial distribution of some commercially exploited fish and to evaluate a recently proposed new ecological niche model (ENM) called nonparametric probabilistic ecological niche model (NPPEN). This new technique is based on a modified version of the test called Multiple Response Permutation Procedure (MRPP) using the generalized Mahalanobis distance. The technique was applied in the extratropical regions of the North Atlantic Ocean on eight commercially exploited fish species using three environmental parameters (sea surface temperature, bathymetry and sea surface salinity). The numerical procedure and the model allowed a better characterization of the niche (sensu Hutchinson) and an improved modelling of the spatial distribution of the species. Furthermore, the technique appeared to be robust to incomplete or bimodal training sets. Despite some potential limitations related to the choice of the climatic scenarios (A2 and B2), the type of physical model (ECHAM 4) and the absence of consideration of biotic interactions, modelled changes in species distribution explained some current observed shifts in dominance that occurred in the North Atlantic sector, and particularly in the North Sea. Although projected changes suggest a poleward movement of species, our results indicate that some species may not be able to track their climatic envelope and that climate change may have a prominent influence on fish distribution during this century. The phenomenon is likely to trigger locally major changes in the dominance of species with likely implications for socio‐economical systems. In this way, ENMs might provide a new management tool against which changes in the resource might be better anticipated.     

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.     

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

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Combined effects of climate and biotic interactions on the elevational range of a phytophagous insect

1. The ranges of many species have expanded in cool regions but contracted at warm margins in response to recent climate warming, but the mechanisms behind such changes remain unclear. Particular debate concerns the roles of direct climatic limitation vs. the effects of interacting species in explaining the location of low latitude or low elevation range margins. 2. The mountains of the Sierra de Guadarrama (central Spain) include both cool and warm range margins for the black-veined white butterfly, Aporia crataegi, which has disappeared from low elevations since the 1970s without colonizing the highest elevations. 3. We found that the current upper elevation limit to A. crataegi's distribution coincided closely with that of its host plants, but that the species was absent from elevations below 900 m, even where host plants were present. The density of A. crataegi per host plant increased with elevation, but overall abundance of the species declined at high elevations where host plants were rare. 4. The flight period of A. crataegi was later at higher elevations, meaning that butterflies in higher populations flew at hotter times of year; nevertheless, daytime temperatures for the month of peak flight decreased by 6.2 degrees C per 1 km increase in elevation. 5. At higher elevations A. crataegi eggs were laid on the south side of host plants (expected to correspond to hotter microclimates), whereas at lower sites the (cooler) north side of plants was selected. Field transplant experiments showed that egg survival increased with elevation. 6. Climatic limitation is the most likely explanation for the low elevation range margin of A. crataegi, whereas the absence of host plants from high elevations sets the upper limit. This contrasts with the frequent assumption that biotic interactions typically determine warm range margins, and thermal limitation cool margins. 7. Studies that have modelled distribution changes in response to climate change may have underestimated declines for many specialist species, because range contractions will be exacerbated by mismatch between the future distribution of suitable climate space and the availability of resources such as host plants.