Testing the performance of beta diversity measures based on incidence data: the robustness to undersampling

Copyright © 2009 Blackwell Publishing Ltd.AIM  Researchers measuring beta diversity have rarely concerned themselves with the problems of how complete the species lists of studied communities are, and of how the varying degrees of completeness can actually change estimates of beta diversity. No comprehensive assessment has been made regarding the behaviour of most beta diversity indices when applied to incomplete samples, a situation which is more common than usually recognized. Our objective was to assess the behaviour and robustness of a number of beta diversity measures for incidence data from under sampled communities. LOCATION  Mainland Portugal and the Azorean archipelago (North Atlantic)

cooccur: Probabilistic Species Co-Occurrence Analysis in R

The observation that species may be positively or negatively associated with each other is at least as old as the debate surrounding the nature of community structure which began in the early 1900's with Gleason and Clements. Since then investigating species co-occurrence patterns has taken a central role in understanding the causes and consequences of evolution, history, coexistence mechanisms, competition, and environment for community structure and assembly. This is because co-occurrence among species is a measurable metric in community datasets that, in the context of phylogeny, geography, traits, and environment, can sometimes indicate the degree of competition, displacement, and phylogenetic repulsion as weighed against biotic and environmental effects promoting correlated species distributions. Historically, a multitude of different co-occurrence metrics have been developed and most have depended on data randomization procedures to produce null distributions for significance testing. Here we improve upon and present an R implementation of a recently published model that is metric-free, distribution-free, and randomization-free. The R package, cooccur, is highly accessible, easily integrates into common analyses, and handles large datasets with high performance. In the article we develop the package's functionality and demonstrate aspects of co-occurrence analysis using three sample datasets

Ecological dependencies make remote reef fish communities most vulnerable to coral loss

yEcosystems face both local hazards, such as over-exploitation, and global hazards, such as climate change. Since the impact of local hazards attenuates with distance from humans, local extinction risk should decrease with remoteness, making faraway areas safe havens for biodiversity. However, isolation and reduced anthropogenic disturbance may increase ecological specialization in remote communities, and hence their vulnerability to secondary effects of diversity loss propagating through networks of interacting species. We show this to be true for reef fish communities across the globe. An increase in fish-coral dependency with the distance of coral reefs from human settlements, paired with the far-reaching impacts of global hazards, increases the risk of fish species loss, counteracting the benefits of remoteness. Hotspots of fish risk from fish-coral dependency are distinct from those caused by direct human impacts, increasing the number of risk hotspots by similar to 30% globally. These findings might apply to other ecosystems on Earth and depict a world where no place, no matter how remote, is safe for biodiversity, calling for a reconsideration of global conservation priorities.Peer reviewe

A probabilistic model for analysing species co-occurrence. Global Ecology and Biogeography 22

ABSTRACT Aim To develop a new probabilistic model that can be used to test for statistically significant pair-wise patterns of species co-occurrence. The model gives the probability that two species would co-occur at a frequency less than (or greater than) the observed frequency if the two species were distributed independently of one another among a set of sites. The model can be used to classify species associations as negative, positive or random. Innovation Historically, the analysis of species co-occurrence has involved the use of data randomization. An observed species presence-absence matrix is compared with randomized matrices to determine if the observed matrix has structure, either an excess or deficit of species positively or negatively associated with each other. The computer algorithms used to randomize matrices can sometimes produce Type I and Type II errors (when the randomization algorithm produces a biased set of all possible matrices) due to the randomization process itself. The probabilistic model does not rely on any data randomization, hence it has a very low Type I error rate and is powerful having a low Type II error rate. Main conclusions When applied to 10 different data sets the probabilistic model revealed significant positive and negative species associations in most of the data sets. Compared with previous analyses the model tended to find fewer significant associations; this may indicate a generally low rate of Type I error in the model. The model is easy to implement and requires no special software. The model could potentially transform the way that ecologists test for species co-occurrence in a wide range of ecological studies