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This chapter proposes a formal and operational definition of a particular niche concept, introduces approaches for characterizing and measuring it, and uses it as a conceptual and terminological basis for describing and understanding much of the related practices of ecological niche modeling and species distribution modeling. It begins with a discussion of the themes that are most important in understanding niche concepts, focusing on three interrelated points: the meaning of “exist indefinitely”; what kinds of variables constitute the hypervolume; and the nature of feedback loops between a species and the variables composing the hypervolume. The chapter then considers the Grinnellian and Eltonian niches as well as the practicalities of estimating Grinnellian niches. It also considers two important interpretations of the niche concept, one of which is concerned with geographic and environmental spaces, and the other emphasizes the Eltonian niche.

This chapter considers the practice of modeling ecological niches and estimating geographic distributions. It first introduces the general principles and definitions underlying ecological niche modeling and species distribution modeling, focusing on model calibration and evaluation, before discussing the principal steps to be followed in building niche models. The first task in building a niche model is to collate, process, error-check, and format the data that are necessary as input. Two types of data are required: primary occurrence data documenting known presences (and sometimes absences) of the species, and environmental predictors in the form of raster-format GIS layers summarizing scenopoetic variables that may (or may not) be involved in delineating the ecological requirements of the species. The next step is to use a modeling algorithm to characterize the species’ ecological niche as a function of the environmental variables, followed by model projection and evaluation and finally, model transferability.

The distribution and dynamics of populations reflect the interplay between dispersal and demography with landscape structure. Understanding how landscape structure affects populations is essential to effective habitat management and species conservation, especially within landscapes undergoing habitat loss and fragmentation as a result of human land-use activities. This chapter thus begins with an overview of the effects of habitat loss and fragmentation on populations, followed by a discussion of species distribution modeling. Then, because population assessment figures so prominently in evaluating a species’ extinction risk to landscape change, the chapter considers the different classes of population models used to estimate population growth rates and population viability, including the use of metapopulation and spatially explicit simulation models.

Range expansions, biological invasions, and disease spread are all inherently spatial processes that involve the successful introduction or colonization, establishment, and dispersal of organisms (or their propagules) into new areas. Population spatial spread thus involves the interaction of both dispersal and demography with landscape structure. This chapter begins by exploring landscape effects on species’ range shifts and the extent to which species can shift their distributions in response to future land-use and climate-change scenarios. Next, the chapter evaluates the effect that landscape structure might have on invasive spread, including an overview of spatial models that are used to predict whether, when, and how fast an invasive species is likely to spread. The chapter concludes with a discussion of disease spread in a landscape context (landscape epidemiology), which involves the study of how pathogens, vectors, and hosts interact with environmental heterogeneity to influence the incidence and persistence of disease in an area.

This chapter introduces mathematical and statistical modelling approaches in community ecology. It starts with a conceptual section, continues with mathematical and statistical sections, and ends with a perspectives section. The conceptual section motivates the modelling approaches by providing the necessary background to community ecology. The mathematical sections start with models of two interacting species in homogeneous space, including a model with competitive interactions, a resource–consumer model, and a predator–prey model. The competition model is expanded to heterogeneous space and to the case of many competing species. This model is used to analyse the consequences of habitat loss and fragmentation at the community level. To illustrate the interplay between models and data, the statistical section analyses data generated by the mathematical models, with emphasis on time-series data of two interacting species, point pattern analyses, and joint species distribution models.

This book deals with ecological niche modeling and species distribution modeling, two emerging fields that address the ecological, geographic, and evolutionary dimensions of geographic distributions of species. It provides a conceptual overview of the complex relationships between ecological niches and geographic distributions of species, both across space and (perhaps to a lesser degree) through time. The emphasis is on how that conceptual framework relates to ecological niche modeling and species distribution modeling, which the book argues are complementary and are most broadly applicable to diverse questions regarding the ecology and geography of biodiversity phenomena. Part I of the book introduces the conceptual framework for thinking about and discussing the distributional ecology of species, Part II is concerned with the data and tools that have been used in the early development of the field, and Part III focuses on real-world situations to which these tools have been applied.

Impact assessments increasingly rely on models to project the potential impacts of climate change on species distributions. Ecological niche models have become established as an efficient and widely used method for interpolating (and sometimes extrapolating) species’ distributions. They use statistical and machine-learning approaches to relate species’ observations to environmental predictor variables and identify the main environmental determinants of species’ ranges. Based on this estimated species–environment relationship, the species’ potential distribution can be mapped in space (and time). In this chapter, we explain the concept and underlying assumptions of ecological niche models, describe the basic modelling steps using the silvereye (Zosterops lateralis) as a simple real-world example, identify potential sources of uncertainty in underlying data and in the model, and discuss potential limitations as well as latest developments and future perspectives of ecological niche models in a global change context.

This chapter provides an introduction to a set of theoretical and numerical models that have been developed for this purpose. Spatial dynamic models are presented in three integral parts: modelling core, context, and method. Modelling cores are dynamic models for describing population demography and spread. Lagrangian models of random walks and step-selection functions that aim to portray and explain individual movement, and Eulerian models of reaction-diffusion and integrodifference equations that aim to capture the spatial dynamics of populations are introduced. Modelling context defines the arena for implementing the core. The basics of species distribution models to provide the context of suitable habitat are presented, leaving aspects of hybrid models, biotic interactions, and non-equilibrium dynamics to other chapters. Modelling methods are techniques for implementing cores in context. Different agent-based models, including individual-based models, cellular automata, and gravity (network) models, are explained.

Understanding and predicting future ecological impacts of climate change, and then developing a conservation strategy to minimize the negative impacts on biodiversity, remains one of the greatest environmental challenges of the twenty-first century. We lack a robust understanding of how climate variability (e.g., temperature, precipitation) itself influences the biology of organisms and, when evidence points to a species being vulnerable to the effects of climate change, there is a lack of specific and timely recommendations for managers to reduce that vulnerability. This chapter reviews how we assess which species are most impacted by climate change and then provides a framework and examples of common strategies and tactics managers can use to incorporate climate change adaptation into bird conservation. In doing so, we present a suite of strategies designed to translate broad conservation concepts into targeted and prescriptive actions for birds.

A wide range of theoretical and applied analyses in animal ecology, biogeography, and conservation biology involve the production or use of maps of species' distributions. Species' distributions are dynamic over time, thus any map is a model, with its specific assumptions, caveats, approximations, and errors. Not all maps are equal or equally useful for all purposes, but distinguishing a useful map from a useless one (for any given purpose) by simply looking at it may be impossible. This chapter summarizes the different types of maps that can be produced, and highlights potential sources of errors and their effects.

Descriptions and consequences of the environmental dependence of long-term population growth rates, i.e., of fitness, are in the focus of this chapter. The ecological tolerances of species (or other reproductive units) are represented by multivariate response functions. These describe the dependence of the long-term growth rate of unregulated populations on the regulating and modifying environmental variables affecting them. Some particular environmental variables, like climatic ones, are routinely used in ecology and population genetics for predicting the geographic distribution of species or ecotypes, while crucial regulating variables are often overlooked. In a reversed logic, response functions can be deduced from the actual geographic distributions of the reproductive units, but this area of research is heavily burdened by several practical problems. Comparative studies of adaptations represent another approach to determining tolerance functions through studying the joint effects of environmental and genetic variation on fitness.

Ecological niches of three species of skunks (Mephitidae: Conepatus leuconotus, Mephitis mephitis, M. macroura) in and near their overlap zone in the American Southwest were studied to determine if competition may be limiting distribution of these species. A species distribution model developed in MaxEnt was used to identify suitable habitat for each species, from which contact zones for each species pair were identified. Principal components derived from habitat and climate variables inside and outside of contact zones for each species, and between species pairs within the contact zone were then compared. Species differed in environmental space inside and outside of contact zones, but species pairs did not differ within contact zones, indicating no evidence of competitive exclusion, and possible niche convergence at a broad spatial scale