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1.
Proc Biol Sci ; 282(1803): 20142879, 2015 Mar 22.
Article in English | MEDLINE | ID: mdl-25673685

ABSTRACT

Dispersal has long been recognized as a mechanism that shapes many observed ecological and evolutionary processes. Thus, understanding the factors that promote its evolution remains a major goal in evolutionary ecology. Landscape connectivity may mediate the trade-off between the forces in favour of dispersal propensity (e.g. kin-competition, local extinction probability) and those against it (e.g. energetic or survival costs of dispersal). It remains, however, an open question how differing degrees of landscape connectivity may select for different dispersal strategies. We implemented an individual-based model to study the evolution of dispersal on landscapes that differed in the variance of connectivity across patches ranging from networks with all patches equally connected to highly heterogeneous networks. The parthenogenetic individuals dispersed based on a flexible logistic function of local abundance. Our results suggest, all else being equal, that landscapes differing in their connectivity patterns will select for different dispersal strategies and that these strategies confer a long-term fitness advantage to individuals at the regional scale. The strength of the selection will, however, vary across network types, being stronger on heterogeneous landscapes compared with the ones where all patches have equal connectivity. Our findings highlight how landscape connectivity can determine the evolution of dispersal strategies, which in turn affects how we think about important ecological dynamics such as metapopulation persistence and range expansion.


Subject(s)
Animal Distribution , Biological Evolution , Ecosystem , Animals , Genetic Fitness , Longevity , Models, Biological , Parthenogenesis , Population Dynamics
2.
Ecology ; 94(3): 627-39, 2013 Mar.
Article in English | MEDLINE | ID: mdl-23687889

ABSTRACT

Ecological communities show extremely complex patterns of variation in space, and quantifying the relative importance of spatial and environmental factors underpinning patterns of species distributions is one of the main goals of community ecology. Although we have accumulated good knowledge about the processes driving species distributions within metacommunities, we have few insights about whether (and how) environmental and spatial features can actually generate consistent species distributional patterns across multiple metacommunities. In this paper we applied the elements of metacommunity structure (EMS) framework to identify and classify metacommunities according to multiple but discrete patterns of species distributions. Given that each pattern has unique underlying structuring mechanisms, exploring and comparing such patterns across multiple metacommunities spanning large geographical areas provides a way to test the existence of general principles underlying species distributions within metacommunities. In this study, we applied the EMS framework into a data set containing about 9000 lakes distributed across 85 fish metacommunities across Ontario, Canada, and estimated the relative importance of local and spatial factors in explaining their distributional patterns. Nested and Clementsian gradients were the patterns that fitted most metacommunities; nested metacommunities were distributed throughout the province, while Clementsian gradient metacommunities were concentrated in the southeastern region. Sixty-five percent of nested metacommunities were located in low-energy watersheds (i.e., colder climate and shorter growing season), whereas metacommunities representing Clementsian gradients were present in high-energy watersheds (i.e., relatively warmer climate and longer growing season). Taken together, our results reveal that the environmental and spatial properties in which metacommunities are embedded are at least partially responsible for their species distributional patterns.


Subject(s)
Demography , Ecosystem , Fishes/physiology , Lakes , Animals , Climate , Fishes/classification , Models, Biological , Ontario
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