Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections?

Holloway, Paul A.; Miller, Jennifer A.; Gillings, Simon

doi:10.1080/13658816.2016.1158823

Cited by 33 publications

(44 citation statements)

References 51 publications

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“…All the dispersal models differed significantly from unlimited dispersal with the exception of the discrete dispersal kernels (Table 3.4). This result is congruent with other studies conducted (Holloway et al 2016), in part due to the fact that the discrete dispersal kernel identifies the maximum possible dispersal distance and treats all locations within this distance as equally accessible. While this may not be the most representative method of long-distance dispersal, it consistently results in higher accuracies compared to the other methods.…”

Section: Discussionsupporting

confidence: 82%

“…Surprisingly, unlimited dispersal (UD) and limited dispersal with an eight-neighbor connectivity (L2) had identical accuracies and areas (Figure 3.3, Figure 3.4). Limited dispersal methods are often considered as a pessimistic case of dispersal, but for species which have continuous distributions at a relatively coarse spatial resolution, they can report similar results to unlimited dispersal (Holloway et al 2016).…”

Section: Discussionmentioning

confidence: 68%

“…All of these methods require a rate of dispersal in order to be properly fit. Empirically derived dispersal rates would ideally be used due to the variability and uncertainty of fitting theoretically derived dispersal kernels (Holloway et al 2016), however, compiling dispersal information for every species in a region would be extremely costly and inefficient. In addressing this issue, Vittoz and Engler (2007) North America and Europe.…”

Section: Parameterized Constraintsmentioning

confidence: 99%

“…Various methodologies have been developed to derive dispersal rates of the population over time (Table 1.4), and these models are then subsequently used to constrain the projected abiotic and biotic suitability (Franklin 2010;Miller and Holloway 2015). (Engler and Guisan, 2009;Cunze et al 2013;Holloway et al 2016) more extensive testing across different taxa and spatial scales is needed.…”

Section: Chapter 3: Quaternary Range Shifts: a Quantitative Assessmenmentioning

confidence: 99%

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Incorporating movement in species distribution models

Miller

Holloway

2015

Progress in Physical Geography: Earth and Environment

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Movement in the context of species distribution models (SDMs) generally refers to a species’ ability to access suitable habitat. Movement ability can be determined by some combination of dispersal constraints or migration rates, landscape factors such as patch configuration, disturbance, and barriers, and demographic factors related to age at maturity, mortality, and fecundity. Including movement ability can result in more precise projections that help to distinguish suitable habitat that is or can be potentially occupied, from suitable habitat that is inaccessible. While most SDM studies have ignored movement or conceptualized it in overly simplistic ways (e.g. no dispersal versus unlimited dispersal), it is increasingly important to incorporate realistic information on movement ability, particularly for studies that aim to project future distributions such as climate change forecasting and invasive species applications. This progress report addresses the increasingly complex ways in which movement has been incorporated in SDM and outlines directions for further study.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 68%

Section: Parameterized Constraintsmentioning

confidence: 99%

Section: Chapter 3: Quaternary Range Shifts: a Quantitative Assessmenmentioning

confidence: 99%

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Incorporating movement in species distribution models

Miller

Holloway

2015

Progress in Physical Geography: Earth and Environment

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“…Most conservation-related publications have focused on predicting the future range expansion of already established invasive birds. Compared to native species, range shift predictions for invasive birds can show greater sensitivity to model assumptions, possibly because invasive birds are not (yet) in equilibrium with the environment and tend to have smaller initial distributions (Holloway et al 2016).…”

Section: Assessing Invasive Birdsmentioning

confidence: 99%

Avian SDMs: current state, challenges, and opportunities

Engler

Stiels

Schidelko

et al. 2017

Journal of Avian Biology

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Quantifying species distributions using species distribution models (SDMs) has emerged as a central method in modern biogeography. These empirical models link species occurrence data with spatial environmental information. Since their emergence in the 1990s, thousands of scientific papers have used SDMs to study organisms across the entire tree of life, with birds commanding considerable attention. Here, we review the current state of avian SDMs and point to challenges and future opportunities for specific applications, ranging from conservation biology, invasive species and predicting seabird distributions, to more general topics such as modeling avian diversity, niche evolution and seasonal distributions at a biogeographic scale. While SDMs have been criticized for being phenomenological in nature, and for their inability to explicitly account for a variety of processes affecting populations, we conclude that they remain a powerful tool to learn about past, current, and future species distributions -at least when their limitations and assumptions are recognized and addressed. We close our review by providing an outlook on prospects and synergies with other disciplines in which avian SDMs can play an important role.

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Habitat functionality: Integrating environmental and geographic space in niche modeling for conservation planning

Moorter

Kivimäki

Panzacchi

et al. 2023

Ecology

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Niche modeling is typically used to assess the effects of anthropogenic land use and climate change on species distributions and to inform spatial conservation planning. These models focus on the suitability of local biotic and abiotic conditions for a species in environmental space (E‐space). Although movements also affect species occurrence, efforts to formally integrate geographic space (G‐space) into niche modeling have been hindered by the lack of comprehensive theoretical frameworks. We propose the “functional habitat” framework to define areas that are simultaneously of high quality in E‐space, and functionally connected to other suitable habitats in G‐space. Originating in metapopulation ecology, approaches have been developed to assess the amount of suitable connected habitats, based on the proximity between pairs of locations. Using network theory, which operates in topological space (T‐space, defined by a network), we extended these metapopulation approaches to integrate movement constraints in G‐space with niche modeling in E‐space. We demonstrate the functional habitat framework using empirical data (GPS tracking and population monitoring) throughout the European wild mountain reindeer (Rangifer t. tarandus) distribution range. We show that functional habitat outperforms traditional suitability in explaining the species' distribution. This approach integrates effects from habitat loss and fragmentation for spatial conservation planning, and avoids overemphasizing small, inaccessible areas with locally suitable habitats. The functional habitat framework formally integrates biotic, abiotic, and movement constraints in niche modeling using network theory, thus opening a wide range of applications in spatial conservation planning.

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Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections?

Cited by 33 publications

References 51 publications

Incorporating movement in species distribution models

Incorporating movement in species distribution models

Avian SDMs: current state, challenges, and opportunities

Habitat functionality: Integrating environmental and geographic space in niche modeling for conservation planning

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