Predictable shorebird departure patterns from a staging site can inform collision risks and mitigation of wind energy developments

High-quality staging sites are critical for long-distance migratory shorebirds to rest and refuel but are under threat from human development, including expansion of wind energy projects. However, predicting migration timing and movements in relation to weather conditions at staging sites can increase our understanding and mitigate effects of wind turbine collisions. Here we assessed northward migration timing and orientation in relation to environmental conditions at an inland staging area in Saskatchewan, Canada, with active and proposed wind energy developments. The area is known to host ~25% of North America's Sanderling Calidris alba population and 16 other Arctic-breeding migrant shorebird species. We quantified arrival and departure time of day in relation to weather using data from 140 of 237 Sanderlings radiotagged locally and at a southern staging site in the Gulf of Mexico with the Motus Wildlife Tracking System (April–June, 2015–2017). Although Sanderling arrival times were not related to time of day or weather, departures were more likely at sunset in winds blowing towards the northwest at intermediate speeds (<22 km/h). Departure flights were also primarily oriented north-northwest in the direction of a proposed wind energy development site at a mean ground speed of 21.4 m/s. Based on published climb rates and flight speed data, we estimated that shorebirds needed between 2 and 14 km setback distance to clear maximum turbine heights of 165 m. Given that departure events were predictable in time and space, adaptive mitigation may be useful for planning wind energy developments while reducing risk for staging Arctic-breeding shorebirds.     

Large scale maximum average power multiple inference on time-course count data with application to RNA-seq analysis

Experiments that longitudinally collect RNA sequencing (RNA-seq) data can provide transformative insights in biology research by revealing the dynamic patterns of genes. Such experiments create a great demand for new analytic approaches to identify differentially expressed (DE) genes based on large-scale time-course count data. Existing methods, however, are suboptimal with respect to power and may lack theoretical justification. Furthermore, most existing tests are designed to distinguish among conditions based on overall differential patterns across time, though in practice, a variety of composite hypotheses are of more scientific interest. Finally, some current methods may fail to control the false discovery rate. In this paper, we propose a new model and testing procedure to address the above issues simultaneously. Specifically, conditional on a latent Gaussian mixture with evolving means, we model the data by negative binomial distributions. Motivated by Storey (2007) and Hwang and Liu (2010), we introduce a general testing framework based on the proposed model and show that the proposed test enjoys the optimality property of maximum average power. The test allows not only identification of traditional DE genes but also testing of a variety of composite hypotheses of biological interest. We establish the identifiability of the proposed model, implement the proposed method via efficient algorithms, and demonstrate its good performance via simulation studies. The procedure reveals interesting biological insights, when applied to data from an experiment that examines the effect of varying light environments on the fundamental physiology of the marine diatom Phaeodactylum tricornutum.     

One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Contrasting sensitivity of nestling and fledgling Barn Swallow Hirundo rustica body mass to local weather conditions

Local weather can influence the growth and development of young birds either indirectly, by modifying prey availability, or directly, by affecting energetic trade-offs. Such effects can have lasting implications for life history traits, but the nature of these effets may vary with the developmental stage of the birds, and over timescales from days to weeks. We examined the interactive effects of temperature, rainfall and wind speed on the mass of nestling and fledgling Barn Swallows Hirundo rustica both on the day of capture and averaging weather across the time since hatching. At the daily timescale, nestling mass was negatively correlated with temperature, but the strength of this association depended on the level of rainfall and wind speed; nestlings were typically heavier on dry or windy days, and the negative effect of temperature was strongest under calm or wet conditions. At the early lifetime timescale (i.e. from hatching to pre-fledging), nestling mass was negatively correlated with temperature at low wind speed. Fledgling body mass was less sensitive to weather; the only weather effect evident was a negative correlation with temperature at the daily scale under high rainfall that became slightly positive under low rainfall. These changes are consistent with weather effects on the availability and distribution of insects within the landscape (e.g. causing high concentrations of flying insects) and with the effects of weather variation on nest microclimate. These results together demonstrate the impacts of weather on chick growth, over immediate (daily) and longer term (nestling/fledgling lifetime) timescales. This shows that sensitivity to local weather conditions varies across the early lifetime of young birds (nestling–fledgling stages) and illustrates the mechanisms by which larger scale (climate) variations influence the body condition of individuals.     

Use of Upland and Riparian Areas by Wintering Bald Eagles and Implications for Wind Energy

Weather can shape movements of animals and alter their exposure to anthropogenic threats. Bald eagles (Haliaeetus leucocephalus) are increasingly at risk from collision with turbines used in onshore wind energy generation. In the midwestern United States, development of this energy source typically occurs in upland areas that bald eagles use only intermittently. Our objective was to determine the factors that cause wintering bald eagles to occupy riparian areas and riskier, upland areas. We tracked 20 bald eagles using telemetry in the Upper Midwest (MN, IA, MO, WI, IL, USA) during winter 2014–2015 and 2015–2016 and evaluated habitat use by eagles in response to variation in weather and time of year. Eagles used riparian areas more when wind speed and atmospheric pressure were low. Exclusive use of uplands was more frequent during weather systems with low pressure and high humidity and after long periods of cold weather. There was a non-linear response to time of year (measured by days before migration) in the frequency of exclusive use of uplands or riparian areas. Probability of exclusive use of either landscape was generally constant within 95 days prior to migration. The probability of use of riparian areas, however, was markedly less during dates >100 days before migration. Our results suggest that eagles are most likely to be exposed to wind energy developments located in upland areas during low pressure systems, after long periods of cold weather, and several months before the onset of spring migration. This information helps to better understand the factors influencing bald eagle habitat use in winter and will be useful to managers and developers wishing to establish effective strategies to avoid, minimize, and mitigate take, and to survey for mortalities at wind energy developments. © 2020 The Wildlife Society.     

Dogs Detect Larger Wind Energy Effects on Bats and Birds

As wind turbine-caused mortality of birds and bats increases with increasing wind energy capacity, accurate fatality estimates are needed to assess effects, identify collision factors, and formulate mitigation. Finding a larger proportion of collision victims reduces the magnitude of adjustment for the proportion not found, thus reducing opportunities for bias. We tested detection dogs in trials of bat and small-bird carcasses placed randomly in routine fatality monitoring at the Buena Vista and Golden Hills Wind Energy projects, California, USA, 2017. Of trial carcasses placed and confirmed available before next-day fatality searches, dogs detected 96% of bats and 90% of small birds, whereas humans at a neighboring wind project detected 6% of bats and 30% of small birds. At Golden Hills dogs found 71 bat fatalities in 55 searches compared to 1 bat found by humans in 69 searches within the same search plots over the same season. Dog detection rates of trial carcasses remained unchanged with distance from turbine, and dogs found more fatalities than did humans at greater distances from turbines. Patterns of fatalities found by dogs within search plots indicated 20% of birds and 4–14% of bats remained undetected outside search plots at Buena Vista and Golden Hills. Dogs also increased estimates of carcass persistence by finding detection trial carcasses that the trial administrator had erroneously concluded were removed. Compared to human searches, dog searches resulted in fatality estimates up to 6.4 and 2.7 times higher for bats and small birds, respectively, along with higher relative precision and >90% lower cost per fatality detection. © 2020 The Authors. The Journal of Wildlife Management published by Wiley Periodicals, Inc. on behalf of The Wildlife Society.     

Winds at departure shape seasonal patterns of nocturnal bird migration over the North Sea

On their migratory journeys, terrestrial birds can come across large inhospitable areas with limited opportunities to rest and refuel. Flight over these areas poses a risk especially when wind conditions en route are adverse, in which case inhospitable areas can act as an ecological barrier for terrestrial migrants. Thus, within the east-Atlantic flyway, the North Sea can function as an ecological barrier. The main aim of this study was to shed light on seasonal patterns of bird migration in the southern North Sea and determine whether departure decisions on nights of intense migration were related to increased wind assistance. We measured migration characteristics with a radar that was located 18 km off the NW Dutch coast and used simulation models to infer potential departure locations of birds on nights with intense nocturnal bird migration. We calculated headings, track directions, airspeeds, groundspeeds on weak and intense migration nights in both seasons and compared speeds between seasons. Moreover, we tested if departure decisions on intense migration nights were associated with supportive winds. Our results reveal that on the intense migration nights in spring, the mean heading was towards E, and birds departed predominantly from the UK. On intense migration nights in autumn, the majority of birds departed from Denmark, Germany and north of the Netherlands with the mean heading towards SW. Prevailing winds from WSW at departure were supportive of a direct crossing of the North Sea in spring. However, in autumn winds were generally not supportive, which is why many birds exploited positive wind assistance which occurred on intense migration nights. This implies that the seasonal wind regimes over the North Sea alter its migratory dynamics which is reflected in headings, timing and intensity of migration.     

Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species’ range dynamics over geologic time

Understanding the factors that shape species’ distributions is a key topic in biogeography. As climates change, species can either cope with these changes through evolution, plasticity or by shifting their ranges to track the optimal climatic conditions. Ecological niche modeling (ENM) is a widespread technique in biogeography that estimates the niche of the organism by using occurrences and environmental data to estimate species’ potential distributions. ENMs are often criticized for failing to take species’ dispersal abilities into consideration. Here, we attempt to fill this gap by combining ENMs with dispersal and corridor modeling to study the range dynamics of North American spadefoot toads (Scaphiopodidae) over the Holocene. We first estimated the current and past distributions of spadefoot toads and then estimated their past distributions from the Last Glacial Maximum (LGM) to the present day. Then, we estimated how each taxon recolonized North American by using dispersal and corridor modeling. By combining these two modeling approaches we were able to 1) estimate the LGM refugia used by the North American spadefoot toads, 2) further refine these projections by estimating which of the putative LGM refugia contributed to the recolonization of North America via dispersal, and 3) estimate the relative influence of each LGM refugium to the current species’ distributions. The models were tested using previously published phylogeographic data, revealing a high degree of congruence between our models and the genetic data. These results suggest that combining ENMs and dispersal modeling over time is a promising approach to investigate both historical and future species’ range dynamics.