Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

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Novel methods improve prediction of species' distributions from occurrence data

■ Abstract The literature on effects of habitat fragmentation on biodiversity is huge. It is also very diverse, with different authors measuring fragmentation in different ways and, as a consequence, drawing different conclusions regarding both the magnitude and direction of its effects. Habitat fragmentation is usually defined as a landscape-scale process involving both habitat loss and the breaking apart of habitat. Results of empirical studies of habitat fragmentation are often difficult to interpret because (a) many researchers measure fragmentation at the patch scale, not the landscape scale and (b) most researchers measure fragmentation in ways that do not distinguish between habitat loss and habitat fragmentation per se, i.e., the breaking apart of habitat after controlling for habitat loss. Empirical studies to date suggest that habitat loss has large, consistently negative effects on biodiversity. Habitat fragmentation per se has much weaker effects on biodiversity that are at least as likely to be positive as negative. Therefore, to correctly interpret the influence of habitat fragmentation on biodiversity, the effects of these two components of fragmentation must be measured independently. More studies of the independent effects of habitat loss and fragmentation per se are needed to determine the factors that lead to positive versus negative effects of fragmentation per se. I suggest that the term “fragmentation” should be reserved for the breaking apart of habitat, independent of habitat loss.

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Effects of Habitat Fragmentation on Biodiversity

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Convex Analysisの二,三の進展について

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

Predicting species distribution: offering more than simple habitat models.

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.

https://www.jstor.org/stable/info/3071919

Simple connectivity measures in spatial ecology

Habitat loss and deterioration, climate change, and economic pressures for resource extraction have all led to a global loss of biodiversity. The limited resources available for conservation need to be used both effectively and efficiently in order to minimise further losses. Spatial conservation prioritization addresses the question of how we should allocate conservation effort and funds in space and time. While the benefits of quantitative conservation prioritization methods have been widely promoted, adoption of these methods in "real-world" planning and implementation is still in its infancy, partly due to the difficulty of identifying which methods and tools (if any) are suited to specific planning problems. Spatial Conservation Prioritization brings together a team of leading scientists to introduce the conceptual and methodological aspects of how to undertake spatial conservation planning in a quantitative manner. It provides the reader with information on when, why, and how to use which statistical and computational methods for conservation prioritization. Important topics underlying spatial prioritization including metapopulation modelling, population viability analysis modelling, species distribution modelling, and uncertainty analysis are discussed, as well as operational definitions and methods. The book includes chapters on the most widely used and latest software, and concludes with an insight into the future of the field.

Spatial conservation prioritization: Quantitative methods and computational tools

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

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.

Metapopulation dynamics: effects of habitat quality and landscape structure

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.

Atte Moilanen

Papers

Simple connectivity measures in spatial ecology

Spatial conservation prioritization: Quantitative methods and computational tools

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

Metapopulation dynamics: effects of habitat quality and landscape structure

Design of reserve networks and the persistence of biodiversity.