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Aim: The geographic range and ecological niche of species are widely used concepts in ecology, evolution and conservation and many modelling approaches have been developed to quantify each. Niche and distribution modelling methods require a litany of design choices; differences among subdisciplines have created communication barriers that increase isolation of scientific advances. As a result, understanding and reproducing the work of others is difficult, if not impossible. It is often challenging to evaluate whether a model has been built appropriately for its intended application or subsequent reuse. Here, we propose a standardized model metadata framework that enables researchers to understand and evaluate modelling decisions while making models fully citable and reproducible. Such reproducibility is critical for both scientific and policy reports, while international standardization enables better comparison between different scenarios and research groups. Innovation: Range modelling metadata (RMMS) address three challenges: they (a) are designed for convenience to encourage use, (b) accommodate a wide variety of applications, and (c) are extensible to allow the research community to steer them as needed. RMMS are based on a metadata dictionary that specifies a hierarchical structure to catalogue different aspects of the range modelling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to modify and extend. To facilitate use, we have developed an R package, rangeModelMetaData, to build templates, automatically fill values from common modelling objects, check for inconsistencies with standards, and suggest values. Main conclusions: Range Modelling Metadata tools foster cross‐disciplinary advances in biogeography, conservation and allied disciplines by improving evaluation, model sharing, model searching, comparisons and reproducibility among studies. Our initially proposed standards here are designed to be modified and extended to evolve with research trends and needs. [ABSTRACT FROM AUTHOR]

Aims: To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. Location: Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. Methods: We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled‐taxa distribution model to explore local differences in species–environment relationships based on the spatial residuals of the pooled‐taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species–environment relationships that can vary across the landscape. We identify multivariate clusters in these local species–environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. Results: We find non‐identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species–environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. Main conclusions: Exploring local species–environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here. [ABSTRACT FROM AUTHOR]

Despite decades of study, the relative importance of niche-based versus neutral processes in community assembly remains largely ambiguous. Recent work suggests niche-based processes are more easily detectable at coarser spatial scales, while neutrality dominates at finer scales. Analyses of functional traits with multi-year multi-site biodiversity inventories may provide deeper insights into assembly processes and the effects of spatial scale. We examined associations between community composition, species functional traits, and environmental conditions for plant communities in the Kouga-Baviaanskloof region, an area within South Africa's Cape Floristic Region (CFR) containing high α and β diversity. This region contains strong climatic gradients and topographic heterogeneity, and is comprised of distinct vegetation classes with varying fire histories, making it an ideal location to assess the role of niche-based environmental filtering on community composition by examining how traits vary with environment. We combined functional trait measurements for over 300 species with observations from vegetation surveys carried out in 1991/1992 and repeated in 2011/2012. We applied redundancy analysis, quantile regression, and null model tests to examine trends in species turnover and functional traits along environmental gradients in space and through time. Functional trait values were weakly associated with most spatial environmental gradients and only showed trends with respect to vegetation class and time since fire. However, survey plots showed greater compositional and functional stability through time than expected based on null models. Taken together, we found clear evidence for functional distinctions between vegetation classes, suggesting strong environmental filtering at this scale, most likely driven by fire dynamics. In contrast, there was little evidence of filtering effects along environmental gradients within vegetation classes, suggesting that assembly processes are largely neutral at this scale, likely the result of very high functional redundancy among species in the regional species pool. [ABSTRACT FROM AUTHOR]

Aim Accurate spatial information on species occurrence is essential to address global change. Models for presence-only data are central to predicting species distributions because these represent the only geographical information available for many species. In this paper we introduce extensions to incorporate a variety of types of additional spatially explicit sources of information in Maxent and Poisson point process models. This spatial information comes from the output of other statistical or conceptual models. Innovation Our approach relies on minimizing the relative (or cross) entropy (known as Minxent) between the predicted distribution and a prior distribution. In many scenarios, researchers have some additional information or expectations about the species distribution, such as outputs from previous models. Here, we show how to use this information to improve predictions of both niche models and spatial distributions, depending on what types of spatially explicit prior information is available and how it is incorporated in the model. Main conclusions We illustrate applications of Minxent that include models for sampling bias, explicitly incorporating dispersal/other ecological processes, combining native and invasive range data, incorporating expert maps, and borrowing strength across taxonomic relatives. These applications focus on addressing biological scenarios where range modelling is extremely challenging - non-equilibrium species distributions and rare and narrowly distributed species - due to data limitations. When data are limited, we are typically forced to make informal assumptions or lean on predictions of other models in order to obtain useful predictions; our applications of Minxent provide a formal way of describing these assumptions and connections to other models. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges. [ABSTRACT FROM AUTHOR]

Hutchinson defined species' realized niche as the set of environmental conditions in which populations can persist in the presence of competitors. In terms of demography, the realized niche corresponds to the environments where the intrinsic growth rate ( r) of populations is positive. Observed species occurrences should reflect the realized niche when additional processes like dispersal and local extinction lags do not have overwhelming effects. Despite the foundational nature of these ideas, quantitative assessments of the relationship between range-wide demographic performance and occurrence probability have not been made. This assessment is needed both to improve our conceptual understanding of species' niches and ranges and to develop reliable mechanistic models of species geographic distributions that incorporate demography and species interactions. The objective of this study is to analyse how demographic parameters (intrinsic growth rate r and carrying capacity K ) and population density ( N ) relate to occurrence probability ( Pocc ). We hypothesized that these relationships vary with species' competitive ability. Demographic parameters, density, and occurrence probability were estimated for 108 tree species from four temperate forest inventory surveys (Québec, western USA, France and Switzerland). We used published information of shade tolerance as indicators of light competition strategy, assuming that high tolerance denotes high competitive capacity in stable forest environments. Interestingly, relationships between demographic parameters and occurrence probability did not vary substantially across degrees of shade tolerance and regions. Although they were influenced by the uncertainty in the estimation of the demographic parameters, we found that r was generally negatively correlated with Pocc, while N, and for most regions K, was generally positively correlated with Pocc. Thus, in temperate forest trees the regions of highest occurrence probability are those with high densities but slow intrinsic population growth rates. The uncertain relationships between demography and occurrence probability suggests caution when linking species distribution and demographic models. [ABSTRACT FROM AUTHOR]

The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt's calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt's outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions. [ABSTRACT FROM AUTHOR]