Patchy Reaction-Diffusion and Population Abundance: The Relative Importance of Habitat Amount and Arrangement

Flather,; Bevers,

doi:10.2307/3079313

Cited by 89 publications

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“…Previous work indicates that the total amount of habitat in a landscape may be the main determinant of population size and viability (26), and the spatial configuration of habitat does not influence occupancy until the proportion of habitat falls below a threshold (27)(28)(29). We were unable to test this hypothesis quantitatively because the total amount of habitat and nonhabitat in each landscape was unknown for most studies.…”

Section: Table 1 Numbers Of Species Included In the Metaanalysismentioning

confidence: 95%

Effect of habitat area and isolation on fragmented animal populations

Prugh

Hodges

Sinclair

et al. 2008

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

635

485

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Habitat destruction has driven many once-contiguous animal populations into remnant patches of varying size and isolation. The underlying framework for the conservation of fragmented populations is founded on the principles of island biogeography, wherein the probability of species occurrence in habitat patches varies as a function of patch size and isolation. Despite decades of research, the general importance of patch area and isolation as predictors of species occupancy in fragmented terrestrial systems remains unknown because of a lack of quantitative synthesis. Here, we compile occupancy data from 1,015 bird, mammal, reptile, amphibian, and invertebrate population networks on 6 continents and show that patch area and isolation are surprisingly poor predictors of occupancy for most species. We examine factors such as improper scaling and biases in species representation as explanations and find that the type of land cover separating patches most strongly affects the sensitivity of species to patch area and isolation. Our results indicate that patch area and isolation are indeed important factors affecting the occupancy of many species, but properties of the intervening matrix should not be ignored. Improving matrix quality may lead to higher conservation returns than manipulating the size and configuration of remnant patches for many of the species that persist in the aftermath of habitat destruction.incidence function ͉ island biogeography ͉ logistic regression ͉ metaanalysis ͉ occupancy H abitat loss and fragmentation are major threats to terrestrial biodiversity (1). Globally, Ϸ40% of land has been converted for agricultural use (2), and regions as diverse as the eastern United States, the Philippines, and Ghana have lost Ͼ90% of their natural habitat (3, 4). Conservation theory and practice are founded on the principle that large habitat patches have more species than small ones and connected patches have more species than isolated ones (5). Although few would dispute this basic premise, we still do not know the general value of patch area and isolation as predictors of species occupancy in fragmented terrestrial systems. Despite hundreds of patch occupancy studies over Ͼ4 decades, there has been no quantitative synthesis of these findings. Several syntheses have examined species-area and diversity relationships (6, 7), but the species occupancy patterns that underlie diversity patterns in fragmented landscapes have been overlooked (8). How important is patch isolation relative to patch size in determining where species occur, and how consistent are these effects across diverse taxonomic groups? These are foundational, yet unanswered, questions for ecology and conservation biology.We synthesized patch occupancy data from 89 studies of terrestrial fauna on 6 continents (Table S1) to determine how patch area and isolation affect species' occurrence patterns. Collectively, these studies recorded the occurrence of 785 animal species (Table 1) in 1,015 population networks surveyed in 12,370 discrete habitat p...

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Section: Table 1 Numbers Of Species Included In the Metaanalysismentioning

confidence: 95%

Effect of habitat area and isolation on fragmented animal populations

Prugh

Hodges

Sinclair

et al. 2008

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

635

485

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“…The interesting question therefore is whether or not a reduction in production is proportional to basal cover reduction (that is, the pure effect of basal cover loss), or if additional "fragmentation" effects give rise to an overproportional loss in production. The analogous question of the relative effects of habitat loss and fragmentation for animal populations has also been a controversial topic of discussion (for example, see Kareiva and Wennergren 1995;Fahrig 1997Fahrig , 2002Flather and Bevers 2002).…”

Section: Introductionmentioning

confidence: 99%

Do Grasslands Have a Memory: Modeling Phytomass Production of a Semiarid South African Grassland

et al. 2004

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We analyzed data sets on phytomass production, basal cover, and monthly precipitation of a semiarid grassland in South Africa for good, medium, and poor rangeland condition (a) to investigate whether phytomass production per unit of basal cover differed among rangeland conditions, (b) to quantify the time scales of a carryover effect from production in previous months, and (c) to construct predictive models for monthly phytomass. Finally, we applied the best models to a 73-year data set of monthly precipitation data to study the long-term variability of grassland production. Our results showed that mean phytomass production per unit of basal cover did not vary significantly among the rangeland conditions-that is, vegetated patches in degraded grassland have approximately the same production as vegetated patches in grassland in good condition. Consequently, the stark decline in production with increasing degradation is a first-order effect of reduced basal area. Current-year precipitation accounted for 64%, 62%, and 36% of the interannual variation in phytomass production for good, medium, and poor condition, respectively. We found that 61%, 68%, and 33%, respectively, of the unexplained variation is related to a memory index that combines mean monthly temperature and a memory of past precipitations. We found a carryover effect in production from the previous 4 years for grassland in good condition and from the previous 1 or 3S month for grassland in medium and poor condition. The memory effect amplified the response of production to changes in precipitation due to alternation of prolonged periods of dry or wet years/months at the time scale of the memory. The interannual variability in phytomass production per unit basal cover (coefficient of variation [CV] ϭ 0.42-0.50 for our 73-year prediction, CV ϭ 0.57-0.71 for the 19-year data) was greater than the corresponding temporal variability in seasonal rainfall (CV ϭ 0.29).

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“…Such critical values can control whether a spreading disturbance affects an entire population (Guichard et al, 2003). Thresholds arising from similar patterns are a pervasive and important phenomenon in the way populations interact with heterogeneous landscapes (Flather and Bevers, 2002;With et al, 1997). Indeed, from the perspective of an organism trying to traverse a landscape composed of patches of suitable habitat or host, especially a poorly dispersing one (King and With, 2002), whether the landscape is past the threshold of total clustering controls whether that organism experiences the entire landscape as connected.…”

Section: Percolation Theoriesmentioning

confidence: 99%

“…If, conversely, most patches are impermeable, then from the organism's perspective the landscape is a collection of isolated clusters of habitat. It turns out that there is a sharp threshold of habitat density distinguishing essentially connected from essentially isolated landscapes (Flather and Bevers, 2002;With et al, 1997).Population clustering and landscape connectivity are two biological interpretations of the same mathematical abstraction: percolation theory (Grimmett, 1999;Stauffer and Aharony, 1991). This formalism studies the random structures created by deleting vertices or edges from networks with fixed, independent probability.…”

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confidence: 99%

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Pair statistics clarify percolation properties of spatially explicit simulations

Achter

Webb

2006

Theoretical Population Biology

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Dispersal is a fundamental control on the spatial structure of a population. We investigate the precise mechanism by which a mixed strategy of short-and long-distance dispersal affects spatial patterning. Using techniques from pair approximation and percolation theory, we demonstrate that dispersal controls the extent to which a population is completely connected by modulating the proportion of neighboring sites which are simultaneously occupied. We show that near the percolation threshold this pair statistic, rather than other metrics proposed earlier, best explains clustering, and we suggest more general circumstances under which this may hold.Key words: dispersal, percolation, pair approximation, aggregation, spatial models, connectivity 2 Understanding the sources and consequences of spatial heterogeneity is a fundamental task in ecology (Levin, 1992). Both in terrestrial (Cain et al., 2000) and marine (Carlon and Olson, 1993) systems, dispersal is one of the basic forces which shapes the spatial structure of a population (Levin and Muller-Landau, 2000; Tilman and Kareiva, 1997). A poorly-dispersing species shows spatial autocorrelation, whereas long-distance dispersal contributes to the spatial mixing of a population (Reed et al., 2000). This spatial patterning has important consequences for phenomena such as species coexistence (Bolker and Pacala, 1999), vulnerability to and recovery from disturbance (Etter and Caswell, 1994;Hiebeler, 2004), and other aspects of ecosystem function (Pacala and Deutschman, 1995). Through such mechanisms, whether dispersal is highly localized, long-distance, or a mixture of the two can affect the spatiotemporal dynamics of a population.In this paper, we examine the effects of mixed dispersal strategies on the spatial structure of a population. To this end we consider a spatially explicit birth-death model, in which organisms have a mixed strategy of local and global dispersal. We will see that the exact strategy -that is, the relative frequency of local and global dispersal -has important consequences for the spatial structure of a population. Specifically, we show that whether a population is completely connected depends on the proportion of pairs of neighboring sites which are simultaneously occupied, and that dispersal influences patterns of aggregation by changing this pair statistic. (Earlier statements in the literature (Iwasa, 2000;Kubo et al., 1996) imply that clustering is determined by a conditional site occupation probability which is related to, but distinct from, the pair statistic.) Briefly, local density controls global connectedness.Much prior work on spatially-explicit, single-species systems has focused on the special case of either purely local (de Aguiar et al., 2004;Haraguchi and Sasaki, 2000) or purely global (Deredec and Courchamp, 2003;Tilman, 1994) dispersal. Other researchers have examined both cases simultaneously (Boots and Sasaki, 2000;Socolar et al., 2001), without considering intermediate or mixed strategies.A class of models which expli...

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Patchy Reaction-Diffusion and Population Abundance: The Relative Importance of Habitat Amount and Arrangement

Cited by 89 publications

References 0 publications

Effect of habitat area and isolation on fragmented animal populations

Effect of habitat area and isolation on fragmented animal populations

Do Grasslands Have a Memory: Modeling Phytomass Production of a Semiarid South African Grassland

Pair statistics clarify percolation properties of spatially explicit simulations

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