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Species distribution and ecological niche models (hereafter SDMs) are popular tools with broad applications in ecology, biodiversity conservation, and environmental science. Many SDM applications require projecting models in environmental conditions non‐analog to those used for model training (extrapolation), giving predictions that may be statistically unsupported and biologically meaningless. We introduce a novel method, Shape, a model‐agnostic approach that calculates the extrapolation degree for a given projection data point by its multivariate distance to the nearest training data point. Such distances are relativized by a factor that reflects the dispersion of the training data in environmental space. Distinct from other approaches, Shape incorporates an adjustable threshold to control the binary discrimination between acceptable and unacceptable extrapolation degrees. We compared Shape's performance to five extrapolation metrics based on their ability to detect analog environmental conditions in environmental space and improve SDMs suitability predictions. To do so, we used 760 virtual species to define different modeling conditions determined by species niche tolerance, distribution equilibrium condition, sample size, and algorithm. All algorithms had trouble predicting species niches. However, we found a substantial improvement in model predictions when model projections were truncated independently of extrapolation metrics. Shape's performance was dependent on extrapolation threshold used to truncate models. Because of this versatility, our approach showed similar or better performance than the previous approaches and could better deal with all modeling conditions and algorithms. Our extrapolation metric is simple to interpret, captures the complex shapes of the data in environmental space, and can use any extrapolation threshold to define whether model predictions are retained based on the extrapolation degrees. These properties make this approach more broadly applicable than existing methods for creating and applying SDMs. We hope this method and accompanying tools support modelers to explore, detect, and reduce extrapolation errors to achieve more reliable models. Keywords: environmental novelty, extrapolation, Mahalanobis distance, model prediction, non‐analog environmental data, transferability [ABSTRACT FROM AUTHOR]

Summary: Spix's Macaw Cyanopsitta spixii is one of the most endangered Neotropical Psittacidae species. Extinct in the wild in the year 2000, in June 2022 the first cohort of C. spixii was reintroduced to its original habitat. For a successful reintroduction of the species, it is necessary to examine the viability of the population against natural and external threats and the environmental requirements for success. Thus, this paper presents a "Population Viability Analysis" (PVA) for Spix's Macaw. It used the Vortex and RangeShiftR software, biological and environmental data from a bibliographic survey, and information provided by the field team responsible for the reintroduction of the species, and who work directly with the species in captivity. We found that the minimum viable population (MVP) for reintroduction of the species is 20 individuals. However, considering the impact of disease, drought, hunting, and illegal trafficking, this population can only persist if the release of individuals from captivity occurs annually over the next 20 years combined with the reforestation of natural habitat to support population growth. [ABSTRACT FROM AUTHOR]