Abstract. We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) ln-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near À0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression, one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.

Summary 1.Compilation of vegetation databases has contributed significantly to the advancement of vegetation science all over the world. Yet, methodological problems result from the use of plant names, particularly in data that originate from numerous and heterogeneous sources. One of the main problems is the inordinate number of synonyms that can be found in vegetation lists. 2. We present Taxonstand, an r package to automatically standardise plant names using The Plant List (http://www.theplantlist.org). The scripts included in this package allow connection to the online search engine of the Plant List and retrieve information from each species about its current taxonomic status. In those cases where the species name is a synonym, it is replaced by the current accepted name. In addition, this package can help correcting orthographic errors in specific epithets. 3. This tool greatly facilitates the preparation of large vegetation databases prior to their analyses, particularly when they cover broad geographical areas (supranational or even continental scale) or contain data from regions with rich floras where taxonomic problems have not been resolved for many of their taxa. Automated workflows such as the one provided by the taxonstand package can ease considerably this task using a widely accessible working nomenclatural authority list for plant species names such as The Plant List.

Here we report that prioritizing sites in order of rarity-weighted richness (RWR) is a simple, reliable way to identify sites that represent all species in the fewest number of sites (minimum set problem) or to identify sites that represent the largest number of species within a given number of sites (maximum coverage problem). We compared the number of species represented in sites prioritized by RWR to numbers of species represented in sites prioritized by the Zonation software package for 11 datasets in which the size of individual planning units (sites) ranged from <1 ha to 2,500 km2. On average, RWR solutions were more efficient than Zonation solutions. Integer programming remains the only guaranteed way find an optimal solution, and heuristic algorithms remain superior for conservation prioritizations that consider compactness and multiple near-optimal solutions in addition to species representation. But because RWR can be implemented easily and quickly in R or a spreadsheet, it is an attractive alternative to integer programming or heuristic algorithms in some conservation prioritization contexts.

1. The stacking of species-distribution models (S-SDMs) is receiving attention by conservation researchers because this approach is capable of simultaneously predicting species richness and composition. However, the steps required to build S-SDMs implies at least two choices that influence its predictive performance which have not been extensively assessed: the selection of the modelling algorithm and the application of a threshold to transform the species-distribution models into binary maps to be added together to build the final S-SDM. Our goal was to provide guidelines concerning the best combinations of modelling algorithms and thresholds with which to build more accurate S-SDMs. 2. We generated 380 S-SDMs of 1224 tree species in Mesoamerica by combining 19 distribution modelling methods with 20 different thresholds using presence-only data from the Global Biodiversity Information Facility. We compared the predicted richness and composition with inventory data obtained from the BIOTREE-NET forest plot database. We designed two indicators of predictive performance that were based on the diversity factors used to measure species turnover: a (shared species between the observed and predicted compositions), b and c (the exclusive species of the predicted and observed compositions respectively) and compared them with the Sorensen and Beta-Simpson turnover measures. 3. Our proposed indexes and the Sorensen index proved suitable as indicators of predictive performance for S-SDMs, whereas the Beta-Simpson turnover measure presented issues that would prevent its application to evaluate S-SDMs. 4. Some modelling methods-especially machine learning and ensemble model forecasting methods performed significantly better than others in minimizing the error in predicted richness and composition. Our results also points out that restrictive thresholds (with high omission errors) lead to more accurate S-SDMs in terms of species richness and composition. Here, we demonstrate that particular combinations of modelling methods and thresholds provide results with higher predictive performance. 5. These results provide clear modelling guidelines that will help S-SDM modellers to select the appropriate combination of modelling methods and thresholds to build more accurate S-SDMs, and therefore will have a positive impact on the quality of the diversity models used to assist conservation planning.

The goal of this study was to document the distribution and establishment A. fulica such as their feeding preference and behavior in situ. The study was carried out at the city of Lauro de Freitas, Bahia state, Brazil, between November 2001 and November 2002. We used catch per unit effort methods to determine abundance, distribution, habitat choice and food preferences. The abundance and distribution of A. fulica was most representative in urban area, mainly near to the coastline. Lots and house gardens were the most preferred sites during active hours. The results indicated that A. fulica started their activity at the end of the evening and stopped in mid-morning.

Because many species have not been described and most species ranges have not been mapped, conservation planners often use surrogates for conservation planning, but evidence for surrogate effectiveness is weak. Surrogates are well-mapped features such as soil types, landforms, occurrences of an easily observed taxon (discrete surrogates), and well-mapped environmental conditions (continuous surrogate). In the context of reserve selection, the idea is that a set of sites selected to span diversity in the surrogate will efficiently represent most species. Environmental diversity (ED) is a rarely used surrogate that selects sites to efficiently span multivariate ordination space. Because it selects across continuous environmental space, ED should perform better than discrete surrogates (which necessarily ignore within-bin and between-bin heterogeneity). Despite this theoretical advantage, ED appears to have performed poorly in previous tests of its ability to identify 50 × 50 km cells that represented vertebrates in Western Europe. Using an improved implementation of ED, we retested ED on Western European birds, mammals, reptiles, amphibians, and combined terrestrial vertebrates. We also tested ED on data sets for plants of Zimbabwe, birds of Spain, and birds of Arizona (United States). Sites selected using ED represented European mammals no better than randomly selected cells, but they represented species in the other 7 data sets with 20% to 84% effectiveness. This far exceeds the performance in previous tests of ED, and exceeds the performance of most discrete surrogates. We believe ED performed poorly in previous tests because those tests considered only a few candidate explanatory variables and used suboptimal forms of ED's selection algorithm. We suggest future work on ED focus on analyses at finer grain sizes more relevant to conservation decisions, explore the effect of selecting the explanatory variables most associated with species turnover, and investigate whether nonclimate abiotic variables can provide useful surrogates in an ED framework.

Aim A major challenge for the emerging discipline of conservation biogeography is to identify conservation areas and understand the factors and processes that govern the spatial distribution of those areas. We aimed to identify highpriority conservation cells (HPCC) -1°cells that efficiently represent speciesfor amphibians, birds and mammals at the global extent, to identify the environmental variables associated with conservation priority, and to evaluate how well the areas of highest species richness correspond to these high-priority areas.Location A global analysis.Methods Distribution maps for 21,697 vertebrates and complementarity-based approaches were used to map HPCCs for vertebrates. We used 41 potential predictor variables and varimax-rotated factor analysis (VrFA) to identify sets of relatively uncorrelated environmental factors, and then used random forest models to investigate the relationships between VrFA factors and vertebrate conservation priorities. Finally, we evaluated whether species richness and threatened-species richness were efficient surrogates to identify HPCCs for each vertebrate taxon.Results For each of the three taxa, HPCCs were concentrated in the Neotropical, Afrotropical and Indo-Malay biogeographical realms. The spatial distribution of HPCCs was strongly correlated with environmental variables, especially energy-related variables. The cells with the highest species richness did not correspond to HPCCs for either birds or mammals.Discussion We suggest that elucidating the patterns and drivers of conservation priority could become a major focus of conservation biogeography. The ability to identify high-priority conservation sites from the environmental conditions in those sites may improve how sites are prioritized for conservation, so that all or most species can be conserved in affordable areas.

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.Keywords: Achatina fulica, alien species, gastropoda, condition factor, invasiveness species. Variáveis ambientais e humanas afetam o comprimento da concha, peso total e fator de condição de Achatina fulica (Gastropoda: Pulmonata)?Resumo A relação peso comprimento e o fator de condição têm sido bastante explorados em pesquisas envolvendo caracóis para obter o índice de condição física em populações e avaliar a condição do habitat. Neste trabalho, nosso objetivo foi descrever que variáveis influenciam os parâmteros biométricos e o bem estar de Achatina fulica em uma recente introdução. De novembro de 2001 a novembro de 2002, amostras mensais desses caracóis foram coletadas na cidade de Lauro de Freitas (Bahia), Brasil. Em seguida, os caracóis foram acondicionados em laboratório e foram obtidos o tamanho da concha e o seu peso. A partir desses dados a curva potencial e o fator de condição foram calculados. Cinco variáveis ambientais foram consideradas: amplitude de temperatura, temperatura média, humidade, precipitação e densidade humana. Regressões múltiplas foram usadas para gerar modelos preditivos através do critério de seleção e logo foram ordenados utilizando o critério de Akaíke. Regressões parciais foram usadas para obter os coeficientes de determinação do clima e fatores humanos. Um total de ...