The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models. It ...avoids the supposed subjectivity in the threshold selection process, when continuous probability derived scores are converted to a binary presence-absence variable, by summarizing overall model performance over all possible thresholds. In this manuscript we review some of the features of this measure and bring into question its reliability as a comparative measure of accuracy between model results. We do not recommend using AUC for five reasons: (1) it ignores the predicted probability values and the goodness-of-fit of the model; (2) it summarises the test performance over regions of the ROC space in which one would rarely operate; (3) it weights omission and commission errors equally; (4) it does not give information about the spatial distribution of model errors; and, most importantly, (5) the total extent to which models are carried out highly influences the rate of well-predicted absences and the AUC scores.
Historic processes are expected to influence present diversity patterns in combination with contemporary environmental factors. We hypothesise that the joint use of beta diversity partitioning ...methods and a threshold-based approach may help reveal the effect of large-scale historic processes on present biodiversity. We partitioned intra-regional beta diversity into its turnover (differences in composition caused by species replacements) and nestedness-resultant (differences in species composition caused by species losses) components. We used piecewise regressions to show that, for amphibian beta diversity, two different world regions can be distinguished. Below parallel 37, beta diversity is dominated by turnover, while above parallel 37, beta diversity is dominated by nestedness. Notably, these regions are revealed when the piecewise regression method is applied to the relationship between latitude and the difference between the Last Glacial Maximum (LGM) and the present temperature but not when present energy-water factors are analysed. When this threshold effect of historic climatic change is partialled out, current energy-water variables become more relevant to the nestedness-resultant dissimilarity patterns, while mountainous areas are associated with higher spatial turnover. This result suggests that nested patterns are caused by species losses that are determined by physiological constraints, whereas turnover is associated with speciation and/or Pleistocene refugia. Thus, the new threshold-based view may help reveal the role of historic factors in shaping present amphibian beta diversity patterns.
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•Mediterranean ectothermic insect species active under cold conditions are rare.•Chelotrupes momus is an endemic and apterous species active during winter nights.•Results indicate ...that this species is active without the need to warm its body.•The exoskeleton of this species may facilitate the absorbance of infrared radiation.•This would constitute an example of a “hotter-is-not-better” thermal strategy.
While there are numerous examples of thermogenesis processes in poikilothermic insects that maintain a stable temperature for a certain time and in certain parts of the body, there is a lack of information on ectothermic insect species capable of remaining active under “cold” conditions that would be challenging for other species. Such a thermal strategy would imply the existence of a metabolism that can operate at different temperatures without the need to increase body temperature when experiencing cold environmental conditions. This “hotter-is-not-better“ thermal strategy is considered ancestral and conjectured to be linked to the origin and evolution of endothermy. In this study, we examined the thermal performance of a large-bodied dung beetle species (Chelotrupes momus) capable of being active during the winter nights in the Iberian Mediterranean region. Field and laboratory results were obtained using thermocamera records, thermocouples, data loggers and spectrometers that measured ultraviolet, visible and near-infrared wavelengths. The thermal data clearly indicated that this species can remain active at a body temperature of approximately 6 °C without the need to warm its body above ambient temperature. Comparing the spectrophotometric data of the species under study with that from other previously examined dung beetle species indicated that the exoskeleton of this particular species likely enhances the absorption of infrared radiation, thereby implying a dual role of the exoskeleton in both heat acquisition and heat dissipation. Taken together, these results suggest that this species has morphological and metabolic adaptations that enable life processes at temperatures that are typically unsuitable for most insect species in the region.
Ecologists and evolutionary biologists are increasingly using big-data approaches to tackle questions at large spatial, taxonomic, and temporal scales. However, despite recent efforts to gather two ...centuries of biodiversity inventories into comprehensive databases, many crucial research questions remain unanswered. Here, we update the concept of knowledge shortfalls and review the tradeoffs between generality and uncertainty. We present seven key shortfalls of current biodiversity data. Four previously proposed shortfalls pinpoint knowledge gaps for species taxonomy (Linnean), distribution (Wallacean), abundance (Prestonian), and evolutionary patterns (Darwinian). We also redefine the Hutchinsonian shortfall to apply to the abiotic tolerances of species and propose new shortfalls relating to limited knowledge of species traits (Raunkiæran) and biotic interactions (Eltonian). We
conclude with a general framework for the combined impacts and consequences of shortfalls of large-scale biodiversity knowledge for evolutionary and ecological research and consider ways of overcoming the seven shortfalls and dealing with the uncertainty they generate.
Abstract
Aim
Different hypothesis have been proposed to explain differences in species richness among islands. However, few studies have attempted to compare the explanatory power of multiple ...hypotheses using a large data set. Here, we analyse how different types of predictors (energetic/climatic, environmental heterogeneity, island biogeography and anthropogenic) affect variation in dung beetle species richness on Mediterranean and Macaronesian islands.
Location
Mediterranean and Macaronesian islands.
Taxon
Dung beetles.
Methods
Using a large data set of islands (
n
= 147), we extracted the species richness of dung beetles on each island using 362 bibliographic reference sources. We performed GLMs to analyse the relationship between the species richness of dung beetles and 11 explanatory variables (temperature, evapotranspiration, aridity, area, maximum elevation, connection to continent during LGM, geological origin, distance from continent, nearest continent, years since first human colonization and human density) representing four types of causal hypotheses. We also included as a covariate the number of published papers studying dung beetles as a surrogate of the survey/study effort carried out in each island.
Results
GLMs suggest that the years since first human colonization, the number of published papers and island area were the predictors with a higher explanatory capacity. The volcanic character of the islands and the distance from the mainland had some relevance in the case of Scarabaeinae and Geotrupinae, and maximum elevation appeared relevant in the species richness of Scarabaeinae and Aphodiidae. The anthropogenic and island biogeography hypotheses on the variation in species richness were the ones that have the strongest explanatory capacity, regardless of the inclusion of the surrogate of survey effort as a covariate in the models.
Main conclusions
The long history of human movements and agricultural activities has facilitated the colonization of dung beetles and provided trophic resources for their persistence, leading to increased species richness. Thus, the importance of anthropogenic factors in shaping the biodiversity patterns of island biogeography cannot be ignored. These human‐induced influences may play a fundamental role in altering the biogeographic patterns of islands, even overriding the importance of other variables. Consequently, our findings underline the profound impact of historical human actions on islands biodiversity.
The increasing interest in the effects of climate changes on species distributions has been followed by the development of Species Distribution Models (SDMs). Although these techniques are starting ...to be used to study the location and dynamics of past species distributions, a sound theoretical framework for their use in paleoecology is still lacking. In this paper we are reviewing the main challenges for constructing Paleo-Species Distribution Models to describe and project the past distribution of species, namely data limitations, selection of predictors and choice of a biologically-relevant modeling procedure. We also review and discuss the current state-of-the-art in Paleo-SDMs, providing a series of recommendations for their use, and proposing future research lines to improve the use of these techniques in paleobiogeography.
► PSDM will provide accurate and fresh information for the past if: ► model calibration data sets include fossil and current species occurrences. ► absence data is avoided to calibrate the models. ► variables included in the models are biologically meaningful. ► Presence/absence models are avoided to predict the past distribution of species.
For many applications the continuous prediction afforded by species distribution modeling must be converted to a map of presence or absence, so a threshold probability indicative of species presence ...must be fixed. Because of the bias in probability outputs due to frequency of presences (prevalence), a fixed threshold value, such as 0.5, does not usually correspond to the threshold above which the species is more likely to be present. In this paper four threshold criteria are compared for a wide range of sample sizes and prevalences, modeling a virtual species in order to avoid the omnipresent error sources that the use of real species data implies. In general, sensitivity–specificity difference minimizer and sensitivity–specificity sum maximizer criteria produced the most accurate predictions. The widely-used 0.5 fixed threshold and Kappa-maximizer criteria are the worst ones in almost all situations. Nevertheless, whatever the criteria used, the threshold value chosen and the research goals that determined its choice must be stated.
1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for ...predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66 113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km², which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.
Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in ...turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (
Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.
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•The available distributional information on insect species is scarce and biased.•Climate change predictions by bioclimatic models are compromised by these data ...limitations.•Bioclimatic models for insects must rely fundamentally on presence information.•The use of simple algorithms without background absences would be more convenient.•Procedures aimed at extracting data on environmental limits are also recommended.
Experimental information on the roles played by climatic factors in determining the ecology and distribution of insect species is scarce. This has stimulated the increasing use of the climatic characteristics of the localities in which the species are observed to derive predictions under different climatic scenarios (the so called species-distribution models or SDMs). This text reviews the main limitations of these correlative models when they are applied to organisms, such as insects, that are characterized by a high degree of collector bias and incompleteness. It is argued that SDMs must rely solely on presence information, rejecting the use of background or pseudoabsences, and that we are not predicting the future distribution of a species but exploring the future location of the climatic conditions in which a species was observed. The scarcity and bias of the available occurrence information in insects as well as our ignorance about the non-climatic factors delimiting species ranges forces us to be extremely careful. It is therefore desirable to avoid the use of central tendency measures reflecting supposed optimum niche conditions because they are particularly dependent on the quantity and biases of the occurrence information. The use of simple algorithms and procedures aimed at extracting information on environmental limits from the available occurrences would be more convenient in this case.
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