Scenarios of change in the realized climatic niche of mountain carnivores and ungulates

Mountains are among the natural systems most affected by climate change, and mountain mammals are considered particularly imperiled, given their high degree of specialization to narrow tolerance bands of environmental conditions. Climate change mitigation policies, such as the Paris Agreement, are essential to stem climate change impacts on natural systems. But how significant is the Paris Agreement to the survival of mountain mammals? We investigated how alternative emission scenarios may determine change in the realized climatic niche of mountain carnivores and ungulates in 2050. We based our predictions of future change in species niches based on how species have responded to past environmental changes, focusing on the probabilities of niche shrink and niche stability. We found that achieving the Paris Agreement's commitments would substantially reduce climate instability for mountain species. Specifically, limiting global warming to below 1.5°C would reduce the probability of niche shrinkage by 4% compared with a high-emission scenario. Globally, carnivores showed greater niche shrinkage than ungulates, whereas ungulates were more likely to shift their niches (i.e., face a level of climate change that allows adaptation). Twenty-three species threatened by climate change according to the IUCN Red List had greater niche contraction than other species we analyzed (3% higher on average). We therefore argue that climate mitigation policies must be coupled with rapid species-specific conservation intervention and sustainable land-use policies to avoid high risk of loss of already vulnerable species.

Global rarity of intact coastal regions

Management of the land-sea interface is essential for global conservation and sustainability objectives because coastal regions maintain natural processes that support biodiversity and the livelihood of billions of people. However, assessments of coastal regions have focused strictly on either the terrestrial or marine realm. Consequently, understanding of the overall state of Earth's coastal regions is poor. We integrated the terrestrial human footprint and marine cumulative human impact maps in a global assessment of the anthropogenic pressures affecting coastal regions. Of coastal regions globally, 15.5% had low anthropogenic pressure, mostly in Canada, Russia, and Greenland. Conversely, 47.9% of coastal regions were heavily affected by humanity, and in most countries (84.1%) >50% of their coastal regions were degraded. Nearly half (43.3%) of protected areas across coastal regions were exposed to high human pressures. To meet global sustainability objectives, all nations must undertake greater actions to preserve and restore the coastal regions within their borders.

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures-ways of generating matching pairs at different scales-in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.