Land‐use transformation is one of the most important and pervasive ecological changes occurring across the Earth, but its long‐term effects are poorly understood. Here, we analyze the effects of urban and agriculture development on bird biodiversity and community structure over a 16‐yr study period. We found that long‐term effects of land‐use change are dependent on spatial scale and land‐use type. At the regional scale, we found that gamma diversity (total number of species observed) declined by ~10% over time. At the landscape spatial scale, we found that beta diversity (uniqueness of bird communities) increased by ~16% over time. Additionally, the average contributions of urban riparian bird communities to beta diversity were generally the highest but declined by ~26% over the study period. Contributions of urban communities to beta diversity were generally the lowest but increased by ~10% over time. At the local scale, we observed different responses for different measures of alpha diversity. For bird species richness, temporal changes varied by land use. Species richness declined 16% at sites in desert riparian areas but increased by 21% and 12% at sites in urban and agricultural areas, respectively. Species evenness declined across all land uses, with some land uses experiencing more rapid declines than others. Our analysis of species groups that shared certain traits suggests that these community‐level changes were driven by species that are small, breed onsite, and feed on insects, grains, and nectar. Collectively, our results suggest that biodiversity declines associated with land‐use change predominate at the regional and local spatial scale, and that these effects can strengthen or weaken over time. However, these changes counterintuitively led to increases in biodiversity at the landscape scale, as bird communities became more unique. This has implications for conservation and management as it shows that the effects of land‐use modification on biodiversity may be positive or negative depending on the spatial scale considered.

Over the last decades several studies have identified that the directional changes in climate induced by anthropogenic emissions of greenhouse gases are affecting the ecology of desert ecosystems. In the Southwest United States, the impacts of climate change to plant abundance and distribution have already been reported, including in the Sonoran Desert ecosystem, home of the iconic Saguaro (Carnegiea gigantea). Hence, there is an urgent need to assess the potential impacts of climate change on the saguaro. The goals of this study are to provide a map of actual habitat suitability (1), describe the relationships between abiotic predictors and the saguaro distribution at regional extents (2), and describe the potential effect of climate change on the spatial distribution of the saguaro (3). Species Distribution Modeling (SDM) was used to investigate the relationships between abiotic variables and the Saguaro distribution. SDMs were calibrated using presence records, 2,000 randomly-generated pseudo absences, and ten abiotic variables. Of these, annual precipitation and max temperature of the warmest month was found to have the greatest relative influence on saguaro distribution. SDMs indicated that 6.9% and 8.1% of the current suitable habitat is predicted to be lost by 2050 and 2070, respectively. Therefore, predicted changes in climate may result in a substantial contraction of the suitable habitat for saguaro over the next century. By identifying the drivers of saguaro distribution and assessing potential changes in habitat suitability due to climate change, this study will help practitioners to design more comprehensive strategies to conserve the saguaro in the face of climate change.

The third largest Biodiversity Hotspot of the world, Mesoamerican forests are declining due to human pressures. Based on species distribution models calibrated for 1224 native tree species in Mesoamerica, we identified high-value forest conservation areas at the resolution of a 10km×10km cells using the Zonation Reserve Selection software, and investigated whether these high-value forest conservation areas are well represented by the World Database on Protected Areas network. We had three key findings. First, dry forest is the least protected biome in Mesoamerica (4.5% protected), indicating that further action to safeguard this biome is warranted. Secondly, the poor overlap between protected areas and high-value forest conservation areas found herein may provide evidence that the establishment of protected areas may not be fully accounting for tree priority rank map. Third, high percentages of forest cover and high-value forest conservation areas still need to be represented by the protected areas network. Because deforestation rates are still increasing in this region, Mesoamerica needs funding and coordinated action by policy makers, national and local governmental and non-governmental organizations, conservationists and other stakeholders

The continuous p‐median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach toEDis computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p‐median for datasets of South African animals. We tested whether maxdisp performs as well as continuous p‐median for 12 datasets that included plants and other continents, and whether particular types of environmental variables produced consistently better models ofED. We selected 12 species inventories and atlases to span a broad range of taxa (plants, birds, mammals, reptiles, and amphibians), spatial extents, and resolutions. For each dataset, we used continuous p‐medianEDand maxdispEDin combination with five sets of environmental variables (five combinations of temperature, precipitation, insolation,NDVI, and topographic variables) to select environmentally diverse sites. We used the species accumulation index (SAI) to evaluate the efficiency ofEDin representing species for each approach and set of environmental variables. MaxdispEDrepresented species better than continuous p‐medianEDin five of 12 biodiversity datasets, and about the same for the other seven biodiversity datasets. Efficiency ofEDalso varied with type of variables used to define environmental space, but no particular combination of variables consistently performed best. We conclude that maxdispEDperforms at least as well as continuous p‐medianED, and has the advantage of faster and simpler computation. Surprisingly, using all 38 environmental variables was not consistently better than using subsets of variables, nor did any subset emerge as consistently best or worst; further work is needed to identify the best variables to define environmental space. Results can help ecologists and conservationists select sites for species representation and assist in conservation planning.

Lack of biodiversity data is a major impediment to prioritizing sites for species representation. Because comprehensive species data are not available in any planning area, planners often use surrogates (such as vegetation communities, or mapped occurrences of a well‐inventoried taxon) to prioritize sites. We propose and demonstrate the effectiveness of predicted rarity‐weighted richness (PRWR) as a surrogate in situations where species inventories may be available for a portion of the planning area. Use ofPRWRas a surrogate involves several steps. First, rarity‐weighted richness (RWR) is calculated from species inventories for aq% subset of sites. Then random forest models are used to modelRWRas a function of freely available environmental variables for thatq% subset. This function is then used to calculatePRWRfor all sites (including those for which no species inventories are available), andPRWRis used to prioritize all sites. We testedPRWRon plant and bird datasets, using the species accumulation index to measure efficiency ofPRWR. Sites with the highestPRWRrepresented species with median efficiency of 56% (range 32%–77% across six datasets) whenq = 20%, and with median efficiency of 39% (range 20%–63%) whenq = 10%. An efficiency of 56% means that selecting sites in order ofPRWRrank was 56% as effective as having full knowledge of species distributions inPRWR's ability to improve on the number of species represented in the same number of randomly selected sites. Our results suggest thatPRWRmay be able to help prioritize sites to represent species if a planner has species inventories for 10%–20% of the sites in the planning area.