How much does an individual habitat fragment contribute to metapopulation dynamics and persistence?

Ovaskainen, Otso; Hanski, Ilkka

doi:10.1016/s0040-5809(03)00102-3

Cited by 93 publications

(102 citation statements)

References 35 publications

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“…For all species, sequentially removing subpopulations following the network metric, eigenvector centrality, produced the most rapid declines in metapopulation biomass (35)(36)(37) (Fig. 4A and Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The most important characteristic is the existence of hub subpopulations (21,29,35,36). These are the most Kelp bass potential and realized connectivity matrices, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…A network perspective is extremely useful for understanding metapopulation structure and dynamics (14,21,29,33,35,(41)(42)(43)(44). However, there are several caveats to its application.…”

Section: Discussionmentioning

confidence: 99%

“…In age-structured models, the "reproductive value" quantifies the contribution of individual age classes to future generation (53). This metric has been generalized to metapopulations by Ovaskainen and Hanski (35) and Jacobi and Jonsson (36), and it identifies the contribution of an individual subpopulation to the growth rate of the entire metapopulation. This metric is otherwise known as "eigenvector centrality" in network theory and has been used to identify keystone species in food webs (36) and important Web pages by the Google search algorithm (54).…”

Section: Methodsmentioning

confidence: 99%

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Identifying critical regions in small-world marine metapopulations

Watson

Siegel

Kendall³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

111

118

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The precarious state of many nearshore marine ecosystems has prompted the use of marine protected areas as a tool for management and conservation. However, there remains substantial debate over their design and, in particular, how to best account for the spatial dynamics of nearshore marine species. Many commercially important nearshore marine species are sedentary as adults, with limited home ranges. It is as larvae that they disperse greater distances, traveling with ocean currents sometimes hundreds of kilometers. As a result, these species exist in spatially complex systems of connected subpopulations. Here, we explicitly account for the mutual dependence of subpopulations and approach protected area design in terms of network robustness. Our goal is to characterize the topology of nearshore metapopulation networks and their response to perturbation, and to identify critical subpopulations whose protection would reduce the risk for stock collapse. We define metapopulation networks using realistic estimates of larval dispersal generated from ocean circulation simulations and spatially explicit metapopulation models, and we then explore their robustness using node-removal simulation experiments. Nearshore metapopulations show small-world network properties, and we identify a set of highly connected hub subpopulations whose removal maximally disrupts the metapopulation network. Protecting these subpopulations reduces the risk for systemic failure and stock collapse. Our focus on catastrophe avoidance provides a unique perspective for spatial marine planning and the design of marine protected areas.connectivity | larval dispersal | protected area design | network theory | Lagrangian simulations N earshore marine ecosystems are some of the most productive and diverse environments on earth, maintaining a wide variety of organisms and providing essential food and services to the global population. Unfortunately, they are also under increasing stress from perturbations, such as oil spills, climate change, and overfishing, and are at risk for losing their productive output (1, 2). Mitigating the impacts of these perturbations is difficult because most nearshore marine species exist in a spatially complex system of connected subpopulations; stress placed at one location may detrimentally reduce stock levels at many others (3, 4). Quantifying patterns of connectivity is crucial to our ability to manage these systems effectively. For example, marine protected areas (MPAs) have emerged as an important tool for conservation and fisheries managers tasked with maintaining nearshore systems and accounting for the mutual demographic dependence of populations (3-9). Current approaches to their design center on either protecting essential habitats (5, 6, 9) or maximizing economic goals (10, 11). We provide an alternative and consider the design of MPAs as a problem of metapopulation network robustness, here defined as the ability of a system to withstand perturbation (12). Our goal is to account for patterns of connectivity and...

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Section: Resultsmentioning

confidence: 99%

“…The most important characteristic is the existence of hub subpopulations (21,29,35,36). These are the most Kelp bass potential and realized connectivity matrices, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…A network perspective is extremely useful for understanding metapopulation structure and dynamics (14,21,29,33,35,(41)(42)(43)(44). However, there are several caveats to its application.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Identifying critical regions in small-world marine metapopulations

Watson

Siegel

Kendall³

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

111

118

View full text Add to dashboard Cite

show abstract

“…We can even calculate λ M at the patch level, allowing us to target specifi c areas within a species distribution for conservation prioritization (Ovaskainen and Hanski 2003 ). Since the spatial aspects would infl uence upon the evolutionary history of animals, we study this by quantifying isolation and size of patches (or islands).…”

Section: Metapopulation Capacitymentioning

confidence: 99%

Metapopulation Capacity Meets Evolutionary Distinctness: Spatial Fragmentation Complements Phylogenetic Rarity in Prioritization

Schnell

Safi

2016

Biodiversity Conservation and Phylogenetic Systematics

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Many species have declined or already gone extinct due to the human activities across the world causing what is termed the current sixth mass extinction event. The biggest determinant of species survival is the availability of a network of suitable habitat, affecting population size and eventual extinction risk. Considering that modern technology allows us to effi ciently quantify habitat loss, species distribution data can inform us of the required minimum connectivity of habitats. Evolutionary distinctiveness (ED) is already part of conservation schemes to prioritize rare traits and unique phylogenetic history. However, so far none of these prioritisations quantifi es the spatial constraints of a species to estimate long-term persistence based on the fragmentation of the landscape. Metapopulation capacity (λ M ) is one such measurement for quantifying fragmentation. Here we propose a combination of metapopulation capacity and phylogenetic distinctiveness to prioritize important specifi c habitat patches for evolutionary distinct species. We applied the new framework to prioritize island mammals and found Data Defi cient and Least Concern species with a high combined value in ED and λ M . Balancing between the extinction risks of solitary islands and the potential loss of unique evolutionary history of rare species on these islands can be a worthwhile exercise in prioritization schemes.

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Ecological coherence of Marine Protected Areas: New tools applied to the Baltic Sea network

Jonsson

Moksnes

Corell³

et al. 2020

Aquatic Conservation

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1. Spatial connectivity is an essential process to consider in the design and assessment of Marine Protected Areas (MPAs). To help maintain and restore marine populations and communities MPAs should form ecologically coherent networks.How to estimate and implement connectivity in MPA design remains a challenge.2. Here a new theoretical framework is presented based on biophysical modelling of organism dispersal, combined with a suite of tools to assess different aspects of connectivity that can be integrated in MPA design. As a demonstration, these tools are applied to an MPA network in the Baltic Sea (HELCOM MPA).3. The tools are based on the connectivity matrix, which summarizes dispersal probabilities, averaged over many years, between all considered areas in the geographic target area. The biophysical model used to estimate connectivity included important biological traits that affect dispersal patterns where different trait combinations and habitat preferences will produce specific connectivity matrices representing different species. 4. Modelled connectivity matrices were used to assess local retention within individual MPAs, which offers indications about the adequacy of size when MPAs are considered in isolation. The connectivity matrix also provides information about source areas to individual MPAs, e.g. sources of larvae or pressures such as contaminants. How well several MPAs act as a network was assessed within a framework of eigenvalue perturbation theory (EPT). With EPT, the optimal MPA network with respect to connectivity can be identified. In addition, EPT can suggest optimal extensions of existing MPA networks to enhance connectivity. Finally, dispersal barriers can be identified based on the connectivity matrix, which may suggest boundaries for management units.5. The assessment of connectivity for the HELCOM MPA are discussed in terms of possible improvements, but the tools presented here could be applied to any region.

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How much does an individual habitat fragment contribute to metapopulation dynamics and persistence?

Cited by 93 publications

References 35 publications

Identifying critical regions in small-world marine metapopulations

Identifying critical regions in small-world marine metapopulations

Metapopulation Capacity Meets Evolutionary Distinctness: Spatial Fragmentation Complements Phylogenetic Rarity in Prioritization

Ecological coherence of Marine Protected Areas: New tools applied to the Baltic Sea network

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