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

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.

/pdf/species-distribution-models-ecological-explanation-and-1szux2yl31.pdf

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

Aim Beta diversity (variation of the species composition of assemblages) may reflect two different phenomena, spatial species turnover and nestedness of assemblages, which result from two antithetic processes, namely species replacement and species loss, respectively. The aim of this paper is to provide a unified framework for the assessment of beta diversity, disentangling the contribution of spatial turnover and nestedness to beta-diversity patterns.

Innovation  I derive an additive partitioning of beta diversity that provides the two separate components of spatial turnover and nestedness underlying the total amount of beta diversity. I propose two families of measures of beta diversity for pairwise and multiple-site situations. Each family comprises one measure accounting for all aspects of beta diversity, which is additively decomposed into two measures accounting for the pure spatial turnover and nestedness components, respectively. Finally, I provide a case study using European longhorn beetles to exemplify the relevance of disentangling spatial turnover and nestedness patterns.

Main conclusion  Assigning the different beta-diversity patterns to their respective biological phenomena is essential for analysing the causality of the processes underlying biodiversity. Thus, the differentiation of the spatial turnover and nestedness components of beta diversity is crucial for our understanding of central biogeographic, ecological and conservation issues.

/pdf/partitioning-the-turnover-and-nestedness-components-of-beta-2tj8148btj.pdf

Partitioning the turnover and nestedness components of beta diversity

A recent increase in studies of β diversity has yielded a confusing array of concepts, measures and methods. Here, we provide a roadmap of the most widely used and ecologically relevant approaches for analysis through a series of mission statements. We distinguish two types of β diversity: directional turnover along a gradient vs. non-directional variation. Different measures emphasize different properties of ecological data. Such properties include the degree of emphasis on presence/absence vs. relative abundance information and the inclusion vs. exclusion of joint absences. Judicious use of multiple measures in concert can uncover the underlying nature of patterns in β diversity for a given dataset. A case study of Indonesian coral assemblages shows the utility of a multi-faceted approach. We advocate careful consideration of relevant questions, matched by appropriate analyses. The rigorous application of null models will also help to reveal potential processes driving observed patterns in β diversity.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1461-0248.2010.01552.x

Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist

Summary

1. Beta diversity, that is, the variation in species composition among sites, can be the result of species replacement between sites (turnover) and species loss from site to site (nestedness).



2. We present betapart, an R package for computing total dissimilarity as Sorensen or Jaccard indices, as well as their respective turnover and nestedness components.



3. betapart allows the assessment of spatial patterns of beta diversity using multiple-site dissimilarity measures accounting for compositional heterogeneity across several sites or pairwise measures providing distance matrices accounting for the multivariate structure of dissimilarity.



4. betapart also allows computing patterns of temporal difference in assemblage composition, and its turnover and nestedness components.



5. Several example analyses are shown, using the data included in the package, to illustrate the relevance of separating the turnover and nestedness components of beta diversity to infer different mechanisms behind biodiversity patterns.

/pdf/betapart-an-r-package-for-the-study-of-beta-diversity-534elqpnf6.pdf

Betapart an R package for the study of beta diversity

Aim Beta diversity can be partitioned into two components: dissimilarity due to species replacement and dissimilarity due to nestedness (Baselga, 2010, Global Ecology and Biogeography, 19, 134–143). Several contributions have challenged this approach or proposed alternative frameworks. Here, I review the concepts and methods used in these recent contributions, with the aim of clarifying: (1) the rationale behind the partitioning of beta diversity into species replacement and nestedness-resultant dissimilarity, (2) how, based on this rationale, numerators and denominators of indices have to match, and (3) how nestedness and nestednessresultant dissimilarity are related but different concepts. Innovation The rationale behind measures of species replacement (turnover) dictates that the number of species that are replaced between sites (numerator of the index) has to be relativized with respect to the total number of species that could potentially be replaced (denominator). However, a recently proposed partition of Jaccard dissimilarity fails to do this. In consequence, this partition underestimates the contribution of species replacement and overestimates the contribution of richness differences to total dissimilarity. I show how Jaccard dissimilarity can be partitioned into meaningful turnover and nestedness components, and extend these new indices to multiple-site situations. Finally the concepts of nestedness and nestedness-resultant dissimilarity are discussed. Main conclusions Nestedness should be assessed using consistent measures that depend both on paired overlap and matrix filling, e.g. NODF, whereas beta-diversity patterns should be examined using measures that allow the total dissimilarity to be separated into the components of dissimilarity due to species replacement and dissimilarity due to nestedness. In the case of multiple-site dissimilarity patterns, averaged pairwise indices should never be used because the mean of the pairwise values is unable to accurately reflect the multiple-site attributes of dissimilarity.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1466-8238.2011.00756.x

The relationship between species replacement, dissimilarity derived from nestedness, and nestedness

It is well known that biodiversity data from historical inventories presents important geographic and taxonomic biases. Due to this, current knowledge on the distribution of most species could be incomplete and biased. We assess how the biases in historical biodiversity data might affect the description of the environmental niche of the species, using exhaustive data on the distribution of dung beetles in Madrid as a case study. We describe the historical process of survey and compare such historical data with the results of an exhaustive survey, identifying the environmental biases in the historical surveys during different periods, and assessing the completeness of the environmental niche of the species provided by historical data through time. Events like the Spanish Civil War affect the tempo and spread of surveys, but the exhaustive work since 1970 provides a good, though incomplete, coverage of the region by 1998. In spite of this, the biases in historical data result in a limited knowledge about the niche of an important number of species. Although nearly a half of the species had the 100% of their niche covered by data in 1998, roughly a third had less than 75%, nearly a fourth less than 50%, and 18 species had to be excluded from the analyses due to the lack of data. Our results point out that data from non-standardized inventories often provide an incomplete description of the environmental responses of most species. Due to this, we highlight that currently predictive models of species distributions present some limitations, since the results of models based in partial information about the environmental niche of the species will be compromised. Therefore, the biases in the available data must be evaluated before constructing predictive maps of species distributions, and taken into account when drawing conclusions or conservation strategies from these maps.

/pdf/historical-bias-in-biodiversity-inventories-affects-the-4g9drwn317.pdf

Historical bias in biodiversity inventories affects the observed environmental niche of the species

Summary


Dissimilarity measures can be formulated using matching components that can be defined as the intersection in terms of species composition of both sets (a) and the relative complements of each set (b and c respectively). Previous work has extended these matching components to abundance-based measures of dissimilarity.
Using these matching components in terms of species abundances I provide a novel partition separating two components of abundance-based dissimilarity: (i) balanced variation in abundance, whereby the individuals of some species in one site are substituted by the same number of individuals of different species in another site; and (ii) abundance gradients, whereby some individuals are lost from one site to the other.
New indices deriving from the additive partition of Bray-Curtis dissimilarity are presented, each one accounting separately for these two antithetic components of assemblage variation.
An example comparing the patterns of increase of assemblage dissimilarity with spatial distance in two tropical forests is provided to illustrate the usefulness of the novel partition to discern the different sources of assemblage variation.
The widely used Bray-Curtis index of dissimilarity is the result of summing these two sources of dissimilarity, and therefore might consider equivalent patterns that are markedly different. Therefore, the novel partition may be useful to assess biodiversity patterns and to explore their causes, as substitution and loss of individuals are patterns that can derive from completely different processes.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12029

Andrés Baselga

Papers

Partitioning the turnover and nestedness components of beta diversity

Betapart an R package for the study of beta diversity

The relationship between species replacement, dissimilarity derived from nestedness, and nestedness

Historical bias in biodiversity inventories affects the observed environmental niche of the species

Separating the two components of abundance-based dissimilarity: balanced changes in abundance vs. abundance gradients