How to understand species’ niches and range dynamics: a demographic research agenda for biogeography

By Frank M. Schurr, J. Pagel, J. Cabral, J. Groeneveld, O. Bykova, R. O’Hara, Florian Hartig, Werner Kissling, H. P. Linder, Guy F. Midgley and 3 more. 2012

317 citations recorded by the source corpus.

Range dynamics causes mismatches between a species’ geographical distribution and the set of suitable environments in which population growth is positive (the Hutchinsonian niche). This is because source–sink population dynamics cause species to occupy unsuitable environments, and because environmental change creates non‐equilibrium situations in which species may be absent from suitable environments (due to migration limitation) or present in unsuitable environments that were previously suitable (due to time‐delayed extinction). Because correlative species distribution models do not account for these processes, they are likely to produce biased niche estimates and biased forecasts of future range dynamics. Recently developed dynamic range models (DRMs) overcome this problem: they statistically estimate both range dynamics and the underlying environmental response of demographic rates from species distribution data. This process‐based statistical approach qualitatively advances biogeographical analyses. Yet, the application of DRMs to a broad range of species and study systems requires substantial research efforts in statistical modelling, empirical data collection and ecological theory. Here we review current and potential contributions of these fields to a demographic understanding of niches and range dynamics. Our review serves to formulate a demographic research agenda that entails: (1) advances in incorporating process‐based models of demographic responses and range dynamics into a statistical framework, (2) systematic collection of data on temporal changes in distribution and abundance and on the response of demographic rates to environmental variation, and (3) improved theoretical understanding of the scaling of demographic rates and the dynamics of spatially coupled populations. This demographic research agenda is challenging but necessary for improved comprehension and quantification of niches and range dynamics. It also forms the basis for understanding how niches and range dynamics are shaped by evolutionary dynamics and biotic interactions. Ultimately, the demographic research agenda should lead to deeper integration of biogeography with empirical and theoretical ecology.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/964b7f711bf46c2a15cab653fb510f27768b9c29

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Citations in the corpus

Mechanistic and Correlative Models of Ecological Niches (2015, unknown): “…has had to find some means of approximating the fundamental niche, which has included simple assumption and postulation of niche size, position, and shape (Godsoe 2010); niches derived from mechanistic models (Kearney & Porter 2004); and niches estimated by correlational means (Schurr et al. 2012).”
Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species (2018, background): “This drawback arises because landscape heterogeneity and metapopulation dynamics limit the ability of species to disperse and track suitable climate changes (Opdam & Wascher, 2004; Schurr et al., 2012).”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, background): “…of field data or life-history information, Bayesian ‘range dynamics models’ with priors derived from related species are a promising development that could help improve our theoretical understanding of range dynamics for species’ with simple demographic characteristics (Schurr et al. 2012).”
Will remote sensing shape the next generation of species distribution models? (2015, background): “…such as testing ecological theories (e.g. Petitpierre et al. 2012); predicting species range dynamics in response to environmental change (e.g. Schurr et al. 2012; Fordham et al. 2013, 2014; Dolos et al. 2015); 4 ª 2015 The Authors Remote Sensing in Ecology and Conservation published by John…”
Improving species distribution models: the value of data on abundance (2014, methodology): “By integrating demographic data with range dynamics, these models aim to reduce bias in future range projections (Pagel & Schurr 2012; Schurr et al. 2012).”
Emerging issues and challenges in conservation of biodiversity in the rangelands of Tanzania (2013, unknown): “Furthermore, new approaches exist to incorporate species interactions into species distribution models (Kissling et al. 2012, Schurr et al. 2012, Wisz et al. 2013).”
RangeShifter: a platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes (2014, methodology): “In response to recent calls for moving towards integrated dynamic modelling approaches (Franklin 2010; Huntley et al. 2010; Schurr et al. 2012; Thuiller et al. 2013), we developed RangeShifter, a novel dynamic modelling platform for investigating species’ spatial dynamics, which provides…”
Temperature dependence of the reproduction niche and its relevance for plant species distributions (2012, background): “Hierarchical Bayesian approaches and inverse parameterization (see Hartig et al., 2012; Schurr et al., 2012) could be a solution to this problem.”
REVIEW: Predictive ecology in a changing world (2015, background): “dynamics of a wide range of phenomena (Schurr et al. 2012; Thuiller et al. 2013).”