Evaluating Tropical Biodiversity: Do We Need a More Refined Approach?

The exceptional biodiversity of tropical forests inspired the earliest ecologists such as H. W. Bates. Today we still strive to quantify and understand this diversity. Drawing on our own experience of Mamirauá reserve in Amazonas, which is located in an area that Bates explored, we argue that the emphasis of research in tropical ecosystems should shift away from species richness as an end in itself, and focus instead on other fundamental, but more tractable, questions that will increase our ecological understanding of these systems, support conservation management, and appeal to policy makers and society in general.     

Evaluating the performance of species richness estimators: sensitivity to sample grain size

1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations. 2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao 1, Jackknifel and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated. 4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).     

Overcoming the issue of small sample sizes in fragmentation genetics

Nazareno & Jump (2012) highlight potential issues with using small sample sizes in population genetic studies. By reanalysing allelic richness data from our recent publication on habitat fragmentation (Struebig et al. 2011), they assert that the observed relationship has been driven by three sites with the lowest number of individuals sampled. While sample size issues have been raised before in the genetic literature, Nazareno & Jump’s (2012) comment serves as a useful reminder to us all. Nevertheless, we disagree that our findings were significantly biased by sampling limitations. Here, we demonstrate by jackknifing that, contrary to the claims of Nazareno & Jump (2012), our correlations of allelic richness and fragment area are not driven solely by sites with low sample sizes. We maintain that small sample sizes can be accounted for in fragmentation studies and that sampling limitations should not detract from undertaking conservation genetic research.     

Counting Alleles with Rarefaction: Private Alleles and Hierarchical Sampling Designs

The number of alleles (allelic richness) in a population is a fundamental measure of genetic variation, and a useful statistic for identifying populations for conservation. Estimating allelic richness is complicated by the effects of sample size: large samples are expected to have more alleles. Rarefaction solves this problem. This communication extends the rarefaction procedure to count private alleles and to accommodate hierarchical sampling designs.     

Helminth parasite richness among vertebrates

The numbers of intestinal helminth species (parasite richnesS) recorded from each of 488 vertebrate host species are compared using data compiled from the published literature. Associations between parasite richness, sampling effort, host size and host habitat (aquatic versus terrestrial) are assessed using a method designed to control for phylogenetic association. Parasite richness increases with the number of surveys on which each estimate of parasite richness is based (sampling effort). When the effects of sampling effort are controlled for, there remains a strong positive relationship between parasite richness and host body size. There is no tendency for aquatic hosts to harbour more parasite species than terrestrial hosts independently of differences in sampling effort and body size. The results are interpreted in the context of hosts representing habitats for parasite colonization, resource allocation between parasite species, and the age of the major mammalian radiations.     

Are all Mexican odonate species documented? An assessment of species richness

With the aim of protecting Mexican diversity, one current governmental task is to complete national biological inventories. In the case of odonate insects, several researchers have hypothesized that species richness is complete (205 dragonflies and 151 damselflies), but there has not been any formal exercise to test this. Thus, we have investigated whether odonate species richness (for Mexican endemics, dragonflies (suborder Anisoptera), damselflies (suborder Zygoptera) and total species) is complete using sample-based and coverage-based rarefaction curves. Along with this, we also showed how good distribution data are in the country. The rarefaction curves have indicated 100% completeness for all groups suggesting that the inventory is complete. However, species' distribution data is highly patchy regarding areas either well (e.g. central Mexico) or badly (e.g. coast of Guerrero and Oaxaca) collected. We encourage researchers to continue odonate sampling in order to support at least three conservation actions: (i) conservation assessment of endangered species; (ii) knowledge of range shifts given rising global temperatures; and (iii) increase public interest and awareness in protected, touristic areas.     

ARES: software to compare allelic richness between uneven samples

Allelic richness is one of the most basic measures of genetic diversity. Its calculation is, however, still problematic because estimates depend on sample size. This paper describes an r library that calculates mean allelic richness with confidence bounds for a range of sample sizes. It takes a file in genepop format as input, or alternatively a binary data matrix with columns representing different individuals and rows representing different alleles. The output is tabular as well as graphical. Unlike existing tools, ares does extrapolate beyond the sample size and provides confidence bounds for these predictions.     

Fishing Behavior and Decision-Making in an Ecuadorian Community: A Scaled Approach

Debate over how people behave in an open access environment is ongoing in both the social sciences and natural resource management circles. Central to this debate is the question of driving forces behind decisions and behavior toward natural resources within a given area. Using data from an artisanal shrimping community in Ecuador, I demonstrate that behavior in an open access environment, measured in terms of fishing effort, is responsive to both economic and noneconomic factors, the nature of which depends on the temporal scale at which behavior is being measured—hourly, daily, or amount of gear over time. These findings suggest that much of the observed variation between previous studies and corresponding data sets may be a methodological artifact, and the ensuing debates ultimately a result of different methodologies and scales of analyses.