Animal Dispersal in Fragmented Habitat: Measuring Habitat Connectivity, Corridor Use, and Dispersal Mortality

Brooker, Lesley; Brooker, Michael; Cale, Peter

doi:10.5751/es-00109-030104

Cited by 109 publications

(62 citation statements)

References 19 publications

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“…The corridor concept (e.g., Forman 1983;Merriam 1991;Saunders and Hobbs 1991;Lindenmayer and Nix 1993;Merriam and Saunders 1993;Noss 1993;Bennett et al 1994;Bonner 1994;Dawson 1994;Rosenberg et al 1997;Tischendorf 1997;Haddad 1999;Brooker et al 1999;Graves et al 2007;Ockinger and Smith 2007) originated from the generalized assumption that organisms do not venture into non-habitat.…”

Section: Structural Definitionsmentioning

confidence: 99%

Connectivity measures: a review

2008

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One of the central problems in contemporary ecology and conservation biology is the drastic change of landscapes induced by anthropogenic activities, resulting in habitat loss and fragmentation. For many wild living species, local extinctions of fragmented populations are common and recolonization is critical for regional survival. Successful recolonization depends on the availability of dispersing individuals and the degree of landscape connectivity. The obvious implications of landscape connectivity for conservation biology have led to a proliferation of connectivity measures. However, general relationships between landscape connectivity and landscape structure are lacking, and so are the relationships between different connectivity metrics. Consequently, there is a need to develop landscape metrics that more accurately characterize the landscape with an emphasis on the underlying processes. Here we review various definitions of landscape connectivity, explain their mathematical connotations, and make some unifying conclusions and suggestions for future research.

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Section: Structural Definitionsmentioning

confidence: 99%

Connectivity measures: a review

2008

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“…Where multiple species or life stages are involved, the data requirements are large and future work should address the need to synthesize connectivity models parametrized for multiple organisms of interest. Strong habitat preferences do not guarantee use of that habitat for dispersal (Schippers et al 1996;Vignieri 2005), and an organism's decisions along the way may be more important in determining the overall path than the cumulative resistance over the entire trajectory (Brooker et al 1999). Here, we explicitly consider the uncertainty associated with typical, least cost path techniques by developing simulation approaches to examine the implications of alternative connectivity models in two infectious disease applications.…”

Section: Discussionmentioning

confidence: 99%

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

Remais

Akullian

Ding

et al. 2010

J. R. Soc. Interface.

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The sustained transmission and spread of environmentally mediated infectious diseases is governed in part by the dispersal of parasites, disease vectors and intermediate hosts between sites of transmission. Functional geospatial models can be used to quantify and predict the degree to which environmental features facilitate or limit connectivity between target populations, yet typical models are limited in their geographical and analytical approach, providing simplistic, global measures of connectivity and lacking methods to assess the epidemiological implications of fine-scale heterogeneous landscapes. Here, functional spatial models are applied to problems of surveillance and control of the parasitic blood fluke Schistosoma japonicum and its intermediate snail host Oncomelania haupensis in western China. We advance functional connectivity methods by providing an analytical framework to (i) identify nodes of transmission where the degree of connectedness to other villages, and thus the potential for disease spread, is higher than is estimated using Euclidean distance alone and (ii) (re)organize transmission sites into disease surveillance units based on second-order relationships among nodes using non-Euclidean distance measures, termed effective geographical distance (EGD). Functional environmental models are parametrized using ecological information on the target organisms, and pair-wise distributions of internode EGD are estimated. A Monte Carlo rank product analysis is presented to identify nearby nodes under alternative distance models. Nodes are then iteratively embedded into EGD space and clustered using a k-means algorithm to group villages into ecologically meaningful surveillance groups. A consensus clustering approach is taken to derive the most stable cluster structure. The results indicate that novel relationships between nodes are revealed when non-Euclidean, ecologically determined distance measures are used to quantify connectivity in heterogeneous landscapes. These connections are not evident when analysing nodes in Euclidean space, and thus surveillance and control activities planned using Euclidean distance measures may be suboptimal. The methods developed here provide a quantitative framework for assessing the effectiveness of ecologically grounded surveillance systems and of control and prevention strategies for environmentally mediated diseases.

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“…Dispersal models responsive to landscape heterogeneity have been used for brown bears (Wiegand et al 1998) and birds (Brooker et al 1999).…”

Section: Dispersalmentioning

confidence: 99%

Inferring Process From Pattern: Can Territory Occupancy Provide Information About Life History Parameters?

Tyre

Possingham

Lindenmayer

2001

Ecological Applications

111

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Abstract. A significant problem in wildlife management is identifying ''good'' habitat for species within the short time frames demanded by policy makers. Statistical models of the response of species presence/absence to predictor variables are one solution, widely known as habitat modeling. We use a ''virtual ecologist'' to test logistic regression as a means of developing habitat models within a spatially explicit, individual-based simulation that allows habitat quality to influence either fecundity or survival with a continuous scale. The basic question is how good are logistic regression models of habitat quality at identifying habitat where birth rates are high and death rates low (i.e., ''source'' habitat)? We find that, even when all the important variables are perfectly measured, and there is no error in surveying the species of interest, demographic stochasticity and the limiting effect of localized dispersal generally prevent an explanation of much more than half of the variation in territory occupancy as a function of habitat quality. This is true regardless of whether fecundity or survival is influenced by habitat quality. In addition, habitat models only detect a significant effect of habitat on territory occupancy when habitat quality is spatially autocorrelated. We find that habitat models based on logistic regression really measure the ability of the species to reach and colonize areas, not birth or death rates.

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Animal Dispersal in Fragmented Habitat: Measuring Habitat Connectivity, Corridor Use, and Dispersal Mortality

Abstract: Abstract•

Cited by 109 publications

References 19 publications

Connectivity measures: a review

Connectivity measures: a review

Analytical methods for quantifying environmental connectivity for the control and surveillance of infectious disease spread

Inferring Process From Pattern: Can Territory Occupancy Provide Information About Life History Parameters?

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