Connectivity is a fundamental concept that is widely utilized in spatial ecology. The majority of connectivity measures used in the recent ecological literature only consider the nearest neighbor patch/population, or patches within a limited neighborhood of the focal patch (a buffer). Meta-analysis suggests that studies using nearest neighbor connectivity measures are much less likely to find statistically significant effects of connectivity than studies that use more complex measures. Here we compare simple connectivity measures in their ability to predict colonization events in two large and good-quality empirical data sets. The nearest neighbor distance to an occupied patch is found to be an inferior measure. Buffer measures do much better, but their performance is found to be sensitive to the estimate of the buffer radius. For highly fragmented habitats, the best and most consistent performance is found for a measure that takes into account the size of the focal patch and the sizes of and distances to all potential source populations. When experimenting with reduced data sets, it was discovered that nearest neighbor measures fail to find a statistically significant effect of connectivity for a large range of data set sizes for which the more complex measures still detect a highly significant effect. We conclude that the simplicity of a nearest neighbor measure is not an adequate compensation for poor performance.

Across large parts of the world, wildlife has to coexist with human activity in highly modified and fragmented landscapes. Combining concepts from population viability analysis and spatial reserve design, this study develops efficient quantitative methods for identifying conservation core areas at large, even national or continental scales. The proposed methods emphasize long-term population persistence, are applicable to both fragmented and natural landscape structures, and produce a hierarchical zonation of regional conservation priority. The methods are applied to both observational data for threatened butterflies at the scale of Britain and modelled probability of occurrence surfaces for indicator species in part of Australia. In both cases, priority landscapes important for conservation management are identified.

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Globally, priority areas for biodiversity are relatively well known, yet few detailed plans exist to direct conservation action within them, despite urgent need. Madagascar, like other globally recognized biodiversity hot spots, has complex spatial patterns of endemism that differ among taxonomic groups, creating challenges for the selection of within-country priorities. We show, in an analysis of wide taxonomic and geographic breadth and high spatial resolution, that multitaxonomic rather than single-taxon approaches are critical for identifying areas likely to promote the persistence of most species. Our conservation prioritization, facilitated by newly available techniques, identifies optimal expansion sites for the Madagascar government's current goal of tripling the land area under protection. Our findings further suggest that high-resolution multitaxonomic approaches to prioritization may be necessary to ensure protection for biodiversity in other global hot spots.

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Summary1. The challenge of climate change forces us to re-examine the assumptions underlying conservation planning. 2. Increasing 'connectivity' has emerged as the most favoured option for conservation in the face of climate change. 3. We argue that the importance of connectivity is being overemphasized: quantifying the benefits of connectivity per se is plagued with uncertainty, and connectivity can be co-incidentally improved by targeting more concrete metrics: habitat area and habitat quality. 4. Synthesis and applications. Before investing in connectivity projects, conservation practitioners should analyse the benefits expected to arise from increasing connectivity and compare them with alternative investments, to ensure as much biodiversity conservation and resilience to climate change as possible within their budget. Strategies that we expect to remain robust in the face of climate change include maintaining and increasing the area of high quality habitats, prioritizing areas that have high environmental heterogeneity and controlling other anthropogenic threatening processes.

Ecologists working with metapopulations are interested in the rate of migration among several local populations, mortality during migration, and the scaling of migration rate with habitat patch area and isolation. We describe a model of individual capture histories obtained from multisite mark–release–recapture studies, which allows one to measure these parameters using maximum likelihood estimation. The model yields separate estimates of mortality within habitat patches and mortality during migration, on the assumption that only the latter is affected by the isolation of the source population. The model is suitable for studies involving 10 or more populations, with differences in habitat patch areas and isolation, and in which several hundred individuals have been marked and recaptured. We apply the model to a metapopulation of the butterfly Melitaea diamina with 14 local populations, 557 marked individuals, and 1301 recaptures. Immigration and emigration scaled as patch area to power 0.2. Roughly half of the daily losses of individuals from habitat patches of 1 ha in area were due to emigration, <1% of daily migration distances were >1 km, and 16% of all deaths were estimated to have occurred during migration. Programs are available to calculate the parameter estimates, their confidence intervals, and goodness‐of‐fit tests.

Connectivity is a fundamental concept that is widely utilized in spatial ecology. The majority of connectivity measures used in the recent ecological literature only consider the nearest neighbor patch/population, or patches within a limited neighborhood of the focal patch (a buffer). Meta-analysis suggests that studies using nearest neighbor connectivity measures are much less likely to find statistically significant effects of connectivity than studies that use more complex measures. Here we compare simple connectivity measures in their ability to predict colonization events in two large and good-quality empirical data sets. The nearest neighbor distance to an occupied patch is found to be an inferior measure. Buffer measures do much better, but their performance is found to be sensitive to the estimate of the buffer radius. For highly fragmented habitats, the best and most consistent performance is found for a measure that takes into account the size of the focal patch and the sizes of and distances to all potential source populations. When experimenting with reduced data sets, it was discovered that nearest neighbor measures fail to find a statistically significant effect of connectivity for a large range of data set sizes for which the more complex measures still detect a highly significant effect. We conclude that the simplicity of a nearest neighbor measure is not an adequate compensation for poor performance.