Atte Moilanen

SIMPLE CONNECTIVITY MEASURES IN SPATIAL ECOLOGY

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.

Design of reserve networks and the persistence of biodiversity

Sophisticated computational methods have been developed to help us to identify sets of nature reserves that maximize the representation of regional diversity, but, until recently, the methods have not dealt explicitly and directly with the main goal of reserve networks, that of the long-term maintenance of biodiversity. Furthermore, the successful application of current methods requires reliable information about species distributions, which is not always available. Recent results show that data quality, as well as the choice of surrogates for biodiversity, could be critical for successful reserve design. Because of these problems and a lack of communication between scientists and managers, the impact of computational site-selection tools in applied conservation planning has been minimal.

METAPOPULATION DYNAMICS: EFFECTS OF HABITAT QUALITY AND LANDSCAPE STRUCTURE

Metapopulation dynamics have received much attention in conservation and population biology, but the standard approach has also been criticized for being too restrictive, as it is based on the effects of habitat patch area and isolation only. Here we demonstrate how the effects of habitat quality (extra environmental factors) and detailed landscape structure (described with GIS [Geographical Information System]) can be included in a spatially realistic metapopulation model, the incidence function model. Expanded models are tested with a large data set on the Glanville fritillary butterfly (Melitaea cinxia). The incidence function model supplemented with additional environmental factors revealed some new and confirmed some previously known interactions between M. cinxia and its environment. However, the ability of the additional environmental factors to explain the error in the fit of the basic model was generally low (≤15%). In the second variant of the basic model, landscape structure was used to modify effective patch isolations. This approach, though biologically appealing, failed to improve significantly the fit of the incidence function model. There are several possible reasons for this failure, including inaccurate satellite data, problems with habitat classification, and most importantly, generic problems in the modeling of migration. Our results demonstrate that additional complexity beyond the effects of habitat patch area and isolation does not necessarily improve the predictive power of a metapopulation model.

Aligning Conservation Priorities Across Taxa in Madagascar with High-Resolution Planning Tools

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 governments 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.

Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems.

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.

ESTIMATING THE PARAMETERS OF SURVIVAL AND MIGRATION OF INDIVIDUALS IN METAPOPULATIONS

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 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.

Predicting the Occurrence of Endangered Species in Fragmented Landscapes

Reliable prediction of metapopulation persistence in fragmented landscapes has become a priority in conservation biology, with ongoing destruction of habitat confining increasing numbers of species into networks of small patches. A spatially realistic metapopulation model, which includes the first-order effects of patch area and isolation on extinction and colonization, has been tested. The distribution of an endangered butterfly was successfully predicted on the basis of parameter values estimated for a well-studied congeneric species. This modeling approach can be a practical tool in the study and conservation of species in highly fragmented landscapes.

Patch occupancy models of metapopulation dynamics : Efficient parameter estimation using implicit statistical inference

The practical value of a predictive metapopulation model is much affected by the amount of data required for parameter estimation. Some metapopulation models require information on population turnover events for parameterization, whereas other models, such as the incidence function model that is used in this study, can be parameterized with spatial data on patch occupancy. The latter data are more readily available. The original method of using spatial pattern data to parameterize the incidence function and other patch models has been criticized for involving potentially troublesome assumptions, such as the independence of habitat patches and constant colonization probabilities. This study describes an improved parameter estimation method that is not affected by these problems. The proposed method is based on Monte Carlo inference for implicit statistical models, and it can be adapted to any stochastic patch occupancy model of metapopulation dynamics. As an additional advantage, the new method allows the estimation of the amplitude of regional stochasticity. Tested with simulated data, the new method was found to produce substantially more accurate parameter estimates than the original method. The new approach is applied to two empirical metapopulations, the false heath fritillary butterfly in Finland and the American pika at Bodie, California.

Delaying conservation actions for improved knowledge: how long should we wait?

Decisions about where conservation actions are implemented are based on incomplete knowledge about biodiversity. The Protea Atlas is a comprehensive database, containing information collated over a decade. Using this data set in a series of retrospective simulations, we compared the outcome from different scenarios of information gain, and habitat protection and loss, over a 20-year period. We assumed that there was no information on proteas at the beginning of the simulation but knowledge improved each year. Our aim was to find out how much time we should spend collecting data before protecting habitat when there is ongoing loss of habitat. We found that, in this case, surveying for more than 2 years rarely increased the effectiveness of conservation decisions in terms of representation of proteas in protected areas and retention within the landscape. If the delay is too long, it can sometimes be more effective just using a readily available habitat map. These results reveal the opportunity costs of delaying conservation action to improve knowledge.

Population connectivity: recent advances and new perspectives

Connectivity is a vital component of metapopulation and landscape ecology, influencing fundamental processes such as population dynamics, evolution, and community responses to climate change. Here, we review ongoing developments in connectivity science, providing perspectives on recent advances in identifying, quantifying, modelling and analysing connectivity, and highlight new applications for conservation. We also address ongoing challenges for connectivity research, explore opportunities for addressing them and highlight potential linkages with other fields of research. Continued development of connectivity science will provide insights into key aspects of ecology and the evolution of species, and will also contribute significantly towards achieving more effective conservation outcomes.