Phenology largely explains taller grass at successful nests in greater sage-grouse

Much interest lies in the identification of manageable habitat variables that affect key vital rates for species of concern. For ground-nesting birds, vegetation surrounding the nest may play an important role in mediating nest success by providing concealment from predators. Height of grasses surrounding the nest is thought to be a driver of nest survival in greater sage-grouse (Centrocercus urophasianus; sage-grouse), a species that has experienced widespread population declines throughout their range. However, a growing body of the literature has found that widely used field methods can produce misleading inference on the relationship between grass height and nest success. Specifically, it has been demonstrated that measuring concealment following nest fate (failure or hatch) introduces a temporal bias whereby successful nests are measured later in the season, on average, than failed nests. This sampling bias can produce inference suggesting a positive effect of grass height on nest survival, though the relationship arises due to the confounding effect of plant phenology, not an effect on predation risk. To test the generality of this finding for sage-grouse, we reanalyzed existing datasets comprising \u3e800 sage-grouse nests from three independent studies across the range where there was a positive relationship found between grass height and nest survival, including two using methods now known to be biased. Correcting for phenology produced equivocal relationships between grass height and sage-grouse nest survival. Viewed in total, evidence for a ubiquitous biological effect of grass height on sage-grouse nest success across time and space is lacking. In light of these findings, a reevaluation of land management guidelines emphasizing specific grass height targets to promote nest success may be merited

Does urbanisation lead to parallel demographic shifts across the world in a cosmopolitan plant?

Urbanisation is occurring globally, leading to dramatic environmental changes that are altering the ecology and evolution of species. In particular, the expansion of human infrastructure and the loss and fragmentation of natural habitats in cities is predicted to increase genetic drift and reduce gene flow by reducing the size and connectivity of populations. Alternatively, the ‘urban facilitation model’ suggests that some species will have greater gene flow into and within cities leading to higher diversity and lower differentiation in urban populations. These alternative hypotheses have not been contrasted across multiple cities. Here, we used the genomic data from the GLobal Urban Evolution project (GLUE), to study the effects of urbanisation on non‐adaptive evolutionary processes of white clover (Trifolium repens) at a global scale. We found that white clover populations presented high genetic diversity and no evidence of reduced Ne linked to urbanisation. On the contrary, we found that urban populations were less likely to experience a recent decrease in effective population size than rural ones. In addition, we found little genetic structure among populations both globally and between urban and rural populations, which showed extensive gene flow between habitats. Interestingly, white clover displayed overall higher gene flow within urban areas than within rural habitats. Our study provides the largest comprehensive test of the demographic effects of urbanisation. Our results contrast with the common perception that heavily altered and fragmented urban environments will reduce the effective population size and genetic diversity of populations and contribute to their isolation

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales.

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development

Spatial delineation of overlay between seven NREL wind power classes (WPC; 1-low wind value, 7-high wind value) and regional resource selection function maps grouped into seven geometric bins (see Fig 4 for color legend).

<p>Hatched areas are predicted low value for golden eagle nesting and wind development.</p

Resource selection function (RSF) probability grids across the Northwest Great Plains (NWGP) and Wyoming Basin (WYB) regions in Wyoming, USA.

<p>RSF values represent the probability proportion to use of golden eagle nest site. Predictions are based on a global model for each region.</p

Coefficient estimates and standard errors for global RSF models in the Northwest Great Plains (NWGP) and the Wyoming Basin (WYB).

<p>Coefficient estimates and standard errors for global RSF models in the Northwest Great Plains (NWGP) and the Wyoming Basin (WYB).</p

Area (km²) and the known number of nests (in parentheses) found overlapping cells between golden eagle RSF and NREL wind power class (WPC) map in the Northwest Great Plains (NWGP) and the Wyoming Basin (WYB).

<p>Values on outside of tables represent the number of wind turbines in each category as of 2009. Cell colors correspond to map in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134781#pone.0134781.g003" target="_blank">Fig 3</a>.</p

Best fit univariate term among competing variables in the Northwest Great Plains (NWGP) and Wyoming Basin (WYB), and coefficient estimate.

<p>m—mean;</p><p>sd-standard deviation;</p><p>²-quadratic term;</p><p>cur—current year; lag– 1 year lagged</p><p>* Correlated variable removed for inclusion in multivariate model</p><p>Asterisks denote correlated variables removed from multivariate RSF models.</p

Pairwise correlation values between variables used in global RSF models and best fit term associated with oil and gas development (producing wells within 5km).

<p>Pairwise correlation values between variables used in global RSF models and best fit term associated with oil and gas development (producing wells within 5km).</p