Establishing the link between habitat selection and animal population dynamics

Abstract

Abstract. Although classical ecological theory (e.g., on ideal free consumers) recognizes the potential effect of population density on the spatial distribution of animals, empirical species distribution models assume that species-habitat relationships remain unchanged across a range of population sizes. Conversely, even though ecological models and experiments have demonstrated the importance of spatial heterogeneity for the rate of population change, we still have no practical method for making the connection between the makeup of real environments, the expected distribution and fitness of their occupants, and the long-term implications of fitness for population growth. Here, we synthesize several conceptual advances into a mathematical framework using a measure of fitness to link habitat availability/selection to (density-dependent) population growth in mobile animal species. A key feature of this approach is that it distinguishes between apparent habitat suitability and the true, underlying contribution of a habitat to fitness, allowing the statistical coefficients of both to be estimated from multiple observation instances of the species in different environments and stages of numerical growth. Hence, it leverages data from both historical population time series and snapshots of species distribution to predict population performance under environmental change. We propose this framework as a foundation for building more realistic connections between a population's use of space and its subsequent dynamics (and hence a contribution to the ongoing efforts to estimate a species' critical habitat and fundamental niche). We therefore detail its associated definitions and simplifying assumptions, because they point to the framework's future extensions. We show how the model can be fit to data on species distributions and population dynamics, using standard statistical methods, and we illustrate its application with an individual-based simulation. When contrasted with nonspatial population models, our approach is better at fitting and predicting population growth rates and carrying capacities. Our approach can be generalized to include a diverse range of biological considerations. We discuss these possible extensions and applications to real data.

Outstanding Challenges in the Transferability of Ecological Models

“…Samples encompassing the full range of environmental conditions and their possible combinations should avoid incomplete niche characterization and improve transferability (Box 2). However, data are often collected opportunistically and pooled during analysis, such that model building ought to account for uneven sampling in environmental space (e.g., by including random effects, or through explicit balancing methods that capture the intensity and distribution of sampling effort [30]). Importantly, data resolution influences model fit, prediction, and by extension, transferability.…”

“…Nonstationarity can also undermine transferability, because species-habitat relationships vary in complexity, strength, and direction across different ecosystems. The effects of population density on apparent habitat preferences can compromise transferability if increases in population density force individuals into suboptimal areas [50], although modeling the dependencies of habitat coefficients on population density offers a potential solution [8,30]. Numerous datasets additionally suffer from sampling biases as well as spatial and temporal autocorrelation, leading to underestimations of heterogeneity among environmental gradients or populations, which cause problems for fitting and validating models [98].…”

Tarangire revisited: Consequences of declining connectivity in a tropical ungulate population

“…Matthiopoulos et al, 2015), which 506 helps set conservation and management priorities with greater certainty (Shuter et al, 2011). Our 507 PCMR method shows that in the Tarangire Manyara Ecosystem, wildebeest populations have 508 remained relatively stable and sustained intra-population connectivity, following increases in the 509 1980's and declines in the 1990's.…”

Disentangling Habitat Concepts for Demersal Marine Fish Management

“…For quantitative purposes, this interpretation of habitat (Interpretation I) has Aarts et al 2008, Matthiopoulos et al 2015. However, many studies of fish habitat have often only described one or two habitat components, which may concern either the seabed type ( Figure 1A), the physicochemical properties of the water column ( Figure 1B), or both, with no mention of biological characteristics ( Figure 1C) (Kaiser et al 1999).…”