The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning–based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles—the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.

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Environmental temperatures influence ectotherms’ physiology and capacity to perform activities necessary for survival and reproduction. Time available to perform those activities is determined by thermal tolerances and environmental temperatures. Estimates of activity time might enhance our ability to predict suitable areas for species’ persistence in face of climate warming, compared to the exclusive use of environmental temperatures, without considering thermal tolerances. We compare the ability of environmental temperatures and estimates of activity time to predict the geographic distribution of a tropical lizard, Tropidurus torquatus. We compared 105 estimates of activity time, resulting from the combination of four methodological decisions: 1) how to estimate daily environmental temperature variation (modeling a sinusoid wave ranging from monthly minimum to maximum temperature, extrapolating from operative temperatures measured in field or using biophysical projections of microclimate)? 2) In which temperature range are animals considered active? 3) Should these ranges be determined from body temperatures obtained in laboratory or in field? And 4) should thermoregulation simulations be included in estimations? We show that models using estimates of activity time made with the sinusoid and biophysical methods had higher predictive accuracy than those using environmental temperatures alone. Estimates made using the central 90% of temperatures measured in a thermal gradient as the temperature range for activity also ranked higher than environmental temperatures. Thermoregulation simulations did not improve model accuracy. Precipitation ranked higher than thermally related predictors. Activity time adds important information to distribution modeling and should be considered as a predictor in studies of the distribution of ectotherms. The distribution of T. torquatus is restricted by precipitation and by the effect of lower temperatures on their time of activity and climate warming could lead to range expansion. We provide an R package ‘Mapinguari’ with tools to generate spatial predictors based on the processes described herein.

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Disturbance caused by anthropogenic fires are increasingly affecting the biodiversity of fire-prone ecosystems worldwide. The Cerrado biodiversity hotspot suffers higher deforestation rates than Amazonia and concentrates most of the burned areas in South America. To support adequate fire-management decisions in Cerrado, knowledge on the effects of altered fire regimes upon its animal populations is necessary. Based on a long-term, large-scale fire experiment, we investigated the effects of different fire regimes on the demography of the gymnophthalmid Micrablepharus atticolus, an endemic lizard of the Cerrado. Because M. atticolus is more abundant in open habitats, we predicted that frequent burns should favor its populations. Over eight years, we conducted a mark-recapture study using pitfall trap arrays in five 10 ha plots of cerrado sensu stricto, subjected to prescribed burns. Using generalized linear mixed-models of time series data and an information theoretic approach to select demographic models, we describe the life history of M. atticolus and assess the response of apparent survival, detectability, and recruitment to burn regimes and climate variation. Micrablepharus atticolus has an annual life cycle, with complete annual population turnover; breeding takes place during the dry season, when activity is higher, and hatchlings appear in the wet season. Apparent survival, detectability, and recruitment increased in the short-run after the passage of fire in all experimental plots. In the long run, however, both fire-suppression and more severe fire regimes were seemingly detrimental, presumably by affecting microclimatic conditions and food availability. Short-term studies may not adequately describe the effects of fire on the demography of lizard populations. Adequate fire management is warranted for biodiversity conservation in Cerrado, both inside and outside protected areas, including a reduction in the frequency and severity of burns in open physiognomies and controlled, patch mosaic fires to prevent excessive fuel accumulation in denser physiognomies. Fire Regimes and the Demography of the Lizard Micrablepharus atticolus (Squamata, Gymnophthalmidae) in a . 2012. Adaptive responses and disruptive effects: how major wildfire influences kinship-based social interactions in a forest marsupial. Molecular Ecology 21:673-684. : Multi-model inference. Available from: http://CRAN.R-project.org/package=MuMIn. Bates D., Maechler M., Bolker B., Walker S. 2014. lme4: Linear mixed-effects models using Eigen and S4. Available from: http:// CRAN.R-project.org/package=lme4.