Multi‐decadal land use impacts across the vast range of an iconic threatened species

Prioritizing Conservation Areas for the Hyacinth Macaw (Anodorhynchus hyacinthinus) in Brazil From Low-Coverage Genomic Data

Estimates of current genetic diversity and population connectivity are especially important for endangered species that are subject to illegal harvesting and trafficking. Genetic monitoring can also ensure that management units are sustaining viable populations, while estimating genetic structure and population dynamics can influence genetic rescue efforts and reintroduction from captive breeding and confiscated animals. The Hyacinth Macaw (Anodorhynchus hyacinthinus) is a charismatic endangered species with a fragmented (allopatric) distribution. Using low coverage genomes, we aimed to investigate the dynamics across the remaining three large disjunct populations of Hyacinth Macaws in Brazil to inform conservation strategies. We obtained low coverage DNA data for 54 individuals from seven sampling sites. Our results showed that Hyacinth Macaws have four genetically structured clusters with relatively high levels of diversity. The Pantanal biome had two genetically distinct populations, with no obvious physical barriers that might explain this differentiation. We detected signs of gene flow between populations, with some geographical regions being more connected than others. Estimates of effective population size in the past million years of the species' evolutionary history showed a decline trend with the lowest Ne in all populations reached within the last few thousand years. Our findings suggest that populations from the Pantanal biome are key to connecting sites across its distribution, and maintaining the integrity of this habitat is important for protecting the species. Given the genetic structure found, we also highlight the need of conserving all wild populations to ensure the protection of the species' evolutionary potential.

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.