Changes in landscape and climate in Mexico and Texas reveal small effects on migratory habitat of monarch butterflies (Danaus plexippus).

The decline of the iconic monarch butterfly (Danaus plexippus) in North America has motivated research on the impacts of land use and land cover (LULC) change and climate variability on monarch habitat and population dynamics. We investigated spring and fall trends in LULC, milkweed and nectar resources over a 20-year period, and ~ 30 years of climate variables in Mexico and Texas, U.S. This region supports spring breeding, and spring and fall migration during the annual life cycle of the monarch. We estimated a - 2.9% decline in milkweed in Texas, but little to no change in Mexico. Fall and spring nectar resources declined < 1% in both study extents. Vegetation greenness increased in the fall and spring in Mexico while the other climate variables did not change in both Mexico and Texas. Monarch habitat in Mexico and Texas appears relatively more intact than in the midwestern, agricultural landscapes of the U.S. Given the relatively modest observed changes in nectar and milkweed, the relatively stable climate conditions, and increased vegetation greenness in Mexico, it seems unlikely that habitat loss (quantity or quality) in Mexico and Texas has caused large declines in population size or survival during migration.

The geographic extent of bird populations affected by renewable-energy development.

Bird populations are declining globally. Wind and solar energy can reduce emissions of fossil fuels that drive anthropogenic climate change, yet renewable-energy production represents a potential threat to bird species. Surveys to assess potential effects at renewable-energy facilities are exclusively local, and the geographic extent encompassed by birds killed at these facilities is largely unknown, which creates challenges for minimizing and mitigating the population-level and cumulative effects of these fatalities. We performed geospatial analyses of stable hydrogen isotope data obtained from feathers of 871 individuals of 24 bird species found dead at solar- and wind-energy facilities in California (USA). Most species had individuals with a mix of origins, ranging from 23% to 98% nonlocal. Mean minimum distances to areas of likely origin for nonlocal individuals were as close as 97 to >1250 km, and these minimum distances were larger for species found at solar-energy facilities in deserts than at wind-energy facilities in grasslands (Cohen's d = 6.5). Fatalities were drawn from an estimated 30-100% of species' desingated ranges, and this percentage was significantly smaller for species with large ranges found at wind facilities (Pearson's r = -0.67). Temporal patterns in the geographic origin of fatalities suggested that migratory movements and nonmigratory movements, such as dispersal and nomadism, influence exposure to fatality risk for these birds. Our results illustrate the power of using stable isotope data to assess the geographic extent of renewable-energy fatalities on birds. As the buildout of renewable-energy facilities continues, accurate assessment of the geographic footprint of wildlife fatalities can be used to inform compensatory mitigation for their population-level and cumulative effects.

Extensión geográfica de las poblaciones de aves afectadas por desarrollos de energía renovable Resumen Las poblaciones mundiales de aves están en declive. Las energías solar y eólica pueden reducir las emisiones de combustibles fósiles que causan el cambio climático, aunque la producción de energías renovables representa una amenaza potencial para las aves. Los censos para evaluar los efectos potenciales en los centros de energía renovable son exclusivamente locales y se sabe poco sobre la extensión geográfica representada por las aves que mueren en estas instalaciones, lo que plantea obstáculos para mitigar los efectos acumulativos y de nivel poblacional de estas muertes. Realizamos análisis geoespaciales con datos del isótopo de hidrógeno estable obtenido de las plumas de 871 ejemplares de 24 especies de aves que fueron hallados muertos en los centros de energía solar y eólica en California, EE.UU. La mayoría de las especies contó con ejemplares de orígenes mixtos, con un rango del 23% al 98% no local. La media de la distancia mínima a las áreas de probable origen de los ejemplares no locales varía entre los 97 hasta > 1,250 km. Estas distancias mínimas fueron mayores para las especies encontradas en los centros de energía solar situadas en desiertos que para las especies encontradas en los centros de energía eólica localizadas en pastizales (d de Cohen = 6.5). Las muertes representan un 30­100% de la extensión de las especies. Este porcentaje fue significativamente menor para las especies con extensiones amplias encontradas en instalaciones eólicas (r de Pearson = ­0.67). Los patrones temporales en el origen geográfico de las muertes sugieren que los movimientos migratorios y no migratorios, como la dispersión y el nomadismo, influyen en la exposición de estas aves al riesgo de muerte. Nuestros resultados demuestran la utilidad de los isótopos estables para evaluar el alcance geográfico de las muertes de aves asociadas a energías renovables. Con el progresivo aumento de instalaciones de energía renovable, una evaluación precisa de la huella geográfica de la mortandad de fauna salvaje podrá guiar la mitigación compensatoria de sus efectos acumulativos y de nivel poblacional.

Georectified polygon database of ground-mounted large-scale solar photovoltaic sites in the United States.

Over 4,400 large-scale solar photovoltaic (LSPV) facilities operate in the United States as of December 2021, representing more than 60 gigawatts of electric energy capacity. Of these, over 3,900 are ground-mounted LSPV facilities with capacities of 1 megawatt direct current (MWdc) or more. Ground-mounted LSPV installations continue increasing, with more than 400 projects appearing online in 2021 alone; however, a comprehensive, publicly available georectified dataset including spatial footprints of these facilities is lacking. The United States Large-Scale Solar Photovoltaic Database (USPVDB) was developed to fill this gap. Using US Energy Information Administration (EIA) data, locations of 3,699 LSPV facilities were verified using high-resolution aerial imagery, polygons were digitized around panel arrays, and attributes were appended. Quality assurance and control were achieved via team peer review and comparison to other US PV datasets. Data are publicly available via an interactive web application and multiple downloadable formats, including: comma-separated value (CSV), application programming interface (API), and GIS shapefile and GeoJSON.

Vulnerability of avian populations to renewable energy production.

Renewable energy production can kill individual birds, but little is known about how it affects avian populations. We assessed the vulnerability of populations for 23 priority bird species killed at wind and solar facilities in California, USA. Bayesian hierarchical models suggested that 48% of these species were vulnerable to population-level effects from added fatalities caused by renewables and other sources. Effects of renewables extended far beyond the location of energy production to impact bird populations in distant regions across continental migration networks. Populations of species associated with grasslands where turbines were located were most vulnerable to wind. Populations of nocturnal migrant species were most vulnerable to solar, despite not typically being associated with deserts where the solar facilities we evaluated were located. Our findings indicate that addressing declines of North American bird populations requires consideration of the effects of renewables and other anthropogenic threats on both nearby and distant populations of vulnerable species.

Urban landcover differentially drives day and nighttime air temperature across a semi-arid city.

Semi-arid urban environments are undergoing an increase in both average air temperatures and in the frequency and intensity of extreme heat events. Within cities, different composition and densities of urban landcovers (ULC) influence local air temperatures, either mitigating or increasing heat. Currently, understanding how combinations of ULC influence air temperature at the block to neighborhood scale is necessary for heat mitigation plans, and yet limited due to the complexities integrating high-resolution ULC with spatial and temporally high-resolution microclimate data. We quantify how ULC influences air temperature at 60 m resolution for day and nighttime climate normals and extreme heat conditions by integrating microclimate sensor data sensor and high-resolution (1 m2) ULC for Denver, Colorado's urban core. We derive ULC drivers of air temperature using a structural equation model, then use a random forest algorithm to predict air temperatures for 30-year climate normals and an extreme heat condition. We find that, in conjunction with other ULC, urban tree canopy reduces daytime air temperatures (-0.026 °C per % cover), and the combination of impervious surfaces and buildings increases daytime air temperature (0.021 °C per % cover). Compared to daytime hours, nighttime irrigated turf temperature cooling effects are increased from being non-significant to -0.022 °C per % cover, while tree canopy effects are reduced from -0.026 °C during the day to -0.016 °C at night. Overall, ULC drives ~17% and 25% of local air temperature during the day and night, respectively. ULC influence on daytime air temperatures is altered in extreme heat events, both depending on the ULC type and time of day. Our findings inform urban planners seeking to identify potential hot and cool spots within a semi-arid city and mitigate high urban air temperatures through using ULC within larger urban climate mitigation strategies.

Limitations, lack of standardization, and recommended best practices in studies of renewable energy effects on birds and bats.

Increasing global energy demand is fostering the development of renewable energy as an alternative to fossil fuels. However, renewable energy facilities may adversely affect wildlife. Facility siting guidelines recommend or require project developers complete pre- and postconstruction wildlife surveys to predict risk and estimate effects of proposed projects. Despite this, there are no published studies that have quantified the types of surveys used or how survey types are standardized within and across facilities. We evaluated 628 peer-reviewed publications, unpublished reports, and citations, and we analyzed data from 525 of these sources (203 facilities: 193 wind and 10 solar) in the United States and Canada to determine the frequency of pre- and postconstruction surveys and whether that frequency changed over time; frequency of studies explicitly designed to allow before-after or impact-control analyses; and what types of survey data were collected during pre- and postconstruction periods and how those data types were standardized across periods and among facilities. Within our data set, postconstruction monitoring for wildlife fatalities and habitat use was a standard practice (n = 446 reports), but preconstruction estimation of baseline wildlife habitat use and mortality was less frequently reported (n = 84). Only 22% (n = 45) of the 203 facilities provided data from both pre- and postconstruction, and 29% (n = 59) had experimental study designs. Of 108 facilities at which habitat-use surveys were conducted, only 3% estimated of detection probability. Thus, the available data generally preclude comparison of biological data across construction periods and among facilities. Use of experimental study designs and following similar field protocols would improve the knowledge of how renewable energy affects wildlife. Article Impact Statement Many surveys at wind and solar facilities provide limited information on wildlife use and fatality rates.

Limitaciones, Falta de Estandarización y las Mejores Prácticas Recomendadas en Estudios de los Efectos de las Energías Renovables sobre las Aves y los Murciélagos Resumen La creciente demanda global por energía está fomentando el desarrollo de energías renovables como una alternativa a los combustibles fósiles. Sin embargo, las instalaciones de energías renovables pueden afectar de manera adversa a la fauna. Las pautas para la ubicación de dichas instalaciones recomiendan o requieren que los desarrolladores de los proyectos realicen censos previa y posteriormente a la construcción de las instalaciones para pronosticar el riesgo y estimar los efectos de los proyectos propuestos. A pesar de esto, no existen estudios publicados que hayan cuantificado los tipos de censo usados o cómo los tipos de censo están estandarizados para las instalaciones en específico y en general. Evaluamos 628 publicaciones revisadas por pares, reportes sin publicar y referencias y analizamos los datos de 525 de estas fuentes (203 instalaciones: 193 de energía eólica y 10 de energía solar) en los Estados Unidos y Canadá para determinar la frecuencia de los censos previos y posteriores a la construcción y si dicha frecuencia cambió con el tiempo; para determinar la frecuencia de los estudios diseñados explícitamente para permitir los análisis antes-y-después o de control-impacto; y para determinar cuáles tipos de datos fueron recolectados previa y posteriormente a la construcción y cómo aquellos tipos de datos estuvieron estandarizados a través de los periodos y entre las instalaciones. Dentro de nuestro conjunto de datos, el monitoreo posterior a la construcción de las fatalidades faunísticas y el uso de hábitat fue una práctica común (n = 446 reportes), pero la estimación previa a la construcción de la línea base del uso de hábitat por la fauna y la mortalidad estuvo reportada con menor frecuencia (n = 84). Sólo el 22% (n = 45) de las 203 instalaciones proporcionaron datos de los censos previos y posteriores a la construcción y el 29% (n = 59) contó con diseño de estudios experimentales. De las 108 instalaciones en las que se realizaron censos de uso de hábitat, sólo el 3% incluyó la estimación de la probabilidad de detección. Por lo tanto, los datos disponibles generalmente impiden la comparación de los datos biológicos durante los periodos de construcción y entre las instalaciones. El uso del diseño de estudios experimentales y el seguimiento de protocolos de campo similares mejoraría el conocimiento sobre cómo las energías renovables afectan a la fauna.

Quantifying the Contribution of Habitats and Pathways to a Spatially Structured Population Facing Environmental Change.

The consequences of environmental disturbance and management are difficult to quantify for spatially structured populations because changes in one location carry through to other areas as a result of species movement. We develop a metric, G, for measuring the contribution of a habitat or pathway to network-wide population growth rate in the face of environmental change. This metric is different from other contribution metrics, as it quantifies effects of modifying vital rates for habitats and pathways in perturbation experiments. Perturbation treatments may range from small degradation or enhancement to complete habitat or pathway removal. We demonstrate the metric using a simple metapopulation example and a case study of eastern monarch butterflies. For the monarch case study, the magnitude of environmental change influences the ordering of node contribution. We find that habitats within which all individuals reside during one season are the most important to short-term network growth under complete removal scenarios, whereas the central breeding region contributes most to population growth over all but the strongest disturbances. The metric G provides for more efficient management interventions that proactively mitigate impacts of expected disturbances to spatially structured populations.

A continuously updated, geospatially rectified database of utility-scale wind turbines in the United States.

Over 60,000 utility-scale wind turbines are installed in the United States as of October, 2019, representing over 97 gigawatts of electric power capacity; US wind turbine installations continue to grow at a rapid pace. Yet, until April 2018, no publicly-available, regularly updated data source existed to describe those turbines and their locations. Under a cooperative research and development agreement, analysts from three organizations collaborated to develop and release the United States Wind Turbine Database (USWTDB) - a publicly available, continuously updated, spatially rectified data source of locations and attributes of utility-scale wind turbines in the United States. Technical specifications and wind facility data, incorporated from five sources, undergo rigorous quality control. The location of each turbine is visually verified using high-resolution aerial imagery. The quarterly-updated data are available in a variety of formats, including an interactive web application, comma-separated values (CSV), shapefile, and application programming interface (API). The data are used widely by academic researchers, engineers and developers from wind energy companies, government agencies, planners, educators, and the general public.

Geographic context affects the landscape change and fragmentation caused by wind energy facilities.

Wind energy generation affects landscapes as new roads, pads, and transmission lines are constructed. Limiting the landscape change from these facilities likely minimizes impacts to biodiversity and sensitive wildlife species. We examined the effects of wind energy facilities' geographic context on changes in landscape patterns using three metrics: portion of undeveloped land, core area index, and connectance index. We digitized 39 wind facilities and the surrounding land cover and measured landscape pattern before and after facility construction using the amount, core area, and connectivity of undeveloped land within one km around newly constructed turbines and roads. New facilities decreased the amount of undeveloped land by 1.8% while changes in metrics of landscape pattern ranged from 50 to 140%. Statistical models indicated pre-construction development was a key factor explaining the impact of new wind facilities on landscape metrics, with pre-construction road networks, turbine spacing, and topography having smaller influences. As the proportion of developed land around facilities increased, a higher proportion of the facility utilized pre-construction developed land and a lower density of new roads were built, resulting in smaller impacts to undeveloped landscapes. Building of new road networks was also a predictor of landscape fragmentation. Utilizing existing development and carefully placing turbines may provide opportunities to minimize the impacts of new wind energy facilities.

Prioritizing conserved areas threatened by wildfire and fragmentation for monitoring and management.

In many parts of the world, the combined effects of habitat fragmentation and altered disturbance regimes pose a significant threat to biodiversity. This is particularly true in Mediterranean-type ecosystems (MTEs), which tend to be fire-prone, species rich, and heavily impacted by human land use. Given the spatial complexity of overlapping threats and species' vulnerability along with limited conservation budgets, methods are needed for prioritizing areas for monitoring and management in these regions. We developed a multi-criteria Pareto ranking methodology for prioritizing spatial units for conservation and applied it to fire threat, habitat fragmentation threat, species richness, and genetic biodiversity criteria in San Diego County, California, USA. We summarized the criteria and Pareto ranking results (from west to east) within the maritime, coastal, transitional, inland climate zones within San Diego County. Fire threat increased from the maritime zone eastward to the transitional zone, then decreased in the mountainous inland climate zone. Number of fires and fire return interval departure were strongly negatively correlated. Fragmentation threats, particularly road density and development density, were highest in the maritime climate zone, declined towards the east, and were positively correlated. Species richness criteria showed distributions among climate zones similar to those of the fire threat variables. When using species richness and fire threat criteria, most lower-ranked (higher conservation priority) units occurred in the coastal and transitional zones. When considering genetic biodiversity, lower-ranked units occurred more often in the mountainous inland zone. With Pareto ranking, there is no need to select criteria weights as part of the decision-making process. However, negative correlations and larger numbers of criteria can result in more units assigned to the same rank. Pareto ranking is broadly applicable and can be used as a standalone decision analysis method or in conjunction with other methods.

A general modeling framework for describing spatially structured population dynamics.

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network-based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life-history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network-based population is modeled with discrete time steps. Using both theoretical and real-world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network-based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles.

Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act.

In 2014, the Fish and Wildlife Service (FWS) and National Marine Fisheries Service announced a new policy interpretation for the U.S. Endangered Species Act (ESA). According to the act, a species must be listed as threatened or endangered if it is determined to be threatened or endangered in a significant portion of its range (SPR). The 2014 policy seeks to provide consistency by establishing that a portion of the range should be considered significant if the associated individuals' "removal would cause the entire species to become endangered or threatened." We reviewed 20 quantitative techniques used to assess whether a portion of a species' range is significant according to the new guidance. Our assessments are based on the 3R criteria-redundancy (i.e., buffering from catastrophe), resiliency (i.e., ability to withstand stochasticity), and representation (i.e., ability to evolve)-that the FWS uses to determine if a species merits listing. We identified data needs for each quantitative technique and considered which methods could be implemented given the data limitations typical of rare species. We also identified proxies for the 3Rs that may be used with limited data. To assess potential data availability, we evaluated 7 example species by accessing data in their species status assessments, which document all the information used during a listing decision. In all species, an SPR could be evaluated with at least one metric for each of the 3Rs robustly or with substantial assumptions. Resiliency assessments appeared most constrained by limited data, and many species lacked information on connectivity between subpopulations, genetic variation, and spatial variability in vital rates. These data gaps will likely make SPR assessments for species with complex life histories or that cross national boundaries difficult. Although we reviewed techniques for the ESA, other countries require identification of significant areas and could benefit from this research.

Monarch butterfly population decline in North America: identifying the threatening processes.

The monarch butterfly (Danaus plexippus) population in North America has sharply declined over the last two decades. Despite rising concern over the monarch butterfly's status, no comprehensive study of the factors driving this decline has been conducted. Using partial least-squares regressions and time-series analysis, we investigated climatic and habitat-related factors influencing monarch population size from 1993 to 2014. Potential threats included climatic factors, habitat loss (milkweed and overwinter forest), disease and agricultural insecticide use (neonicotinoids). While climatic factors, principally breeding season temperature, were important determinants of annual variation in abundance, our results indicated strong negative relationships between population size and habitat loss variables, principally glyphosate use, but also weaker negative effects from the loss of overwinter forest and breeding season use of neonicotinoids. Further declines in population size because of glyphosate application are not expected. Thus, if remaining threats to habitat are mitigated we expect climate-induced stochastic variation of the eastern migratory population of monarch butterfly around a relatively stationary population size.

Density estimates of monarch butterflies overwintering in central Mexico.

Given the rapid population decline and recent petition for listing of the monarch butterfly (Danaus plexippus L.) under the Endangered Species Act, an accurate estimate of the Eastern, migratory population size is needed. Because of difficulty in counting individual monarchs, the number of hectares occupied by monarchs in the overwintering area is commonly used as a proxy for population size, which is then multiplied by the density of individuals per hectare to estimate population size. There is, however, considerable variation in published estimates of overwintering density, ranging from 6.9-60.9 million ha-1. We develop a probability distribution for overwinter density of monarch butterflies from six published density estimates. The mean density among the mixture of the six published estimates was ∼27.9 million butterflies ha-1 (95% CI [2.4-80.7] million ha-1); the mixture distribution is approximately log-normal, and as such is better represented by the median (21.1 million butterflies ha-1). Based upon assumptions regarding the number of milkweed needed to support monarchs, the amount of milkweed (Asclepias spp.) lost (0.86 billion stems) in the northern US plus the amount of milkweed remaining (1.34 billion stems), we estimate >1.8 billion stems is needed to return monarchs to an average population size of 6 ha. Considerable uncertainty exists in this required amount of milkweed because of the considerable uncertainty occurring in overwinter density estimates. Nevertheless, the estimate is on the same order as other published estimates. The studies included in our synthesis differ substantially by year, location, method, and measures of precision. A better understanding of the factors influencing overwintering density across space and time would be valuable for increasing the precision of conservation recommendations.

Factors associated with bat mortality at wind energy facilities in the United States.

Hundreds of thousands of bats are killed annually by colliding with wind turbines in the U.S., yet little is known about factors causing variation in mortality across wind energy facilities. We conducted a quantitative synthesis of bat collision mortality with wind turbines by reviewing 218 North American studies representing 100 wind energy facilities. This data set, the largest compiled for bats to date, provides further support that collision mortality is greatest for migratory tree-roosting species (Hoary Bat [Lasiurus cinereus], Eastern Red Bat [Lasiurus borealis], Silver-haired Bat [Lasionycteris noctivagans]) and from July to October. Based on 40 U.S. studies meeting inclusion criteria and analyzed under a common statistical framework to account for methodological variation, we found support for an inverse relationship between bat mortality and percent grassland cover surrounding wind energy facilities. At a national scale, grassland cover may best reflect openness of the landscape, a factor generally associated with reduced bat activity and abundance that may also reduce turbine collisions. Further representative sampling of wind energy facilities is required to validate this broad pattern. Ecologically informed decisions regarding placement of wind energy facilities involves multiple considerations, including not only factors associated with bat mortality, but also factors associated with bird collision mortality, indirect habitat-related impacts to all species, and overall ecosystem impacts.

Effects of wind energy generation and white-nose syndrome on the viability of the Indiana bat.

Wind energy generation holds the potential to adversely affect wildlife populations. Species-wide effects are difficult to study and few, if any, studies examine effects of wind energy generation on any species across its entire range. One species that may be affected by wind energy generation is the endangered Indiana bat (Myotis sodalis), which is found in the eastern and midwestern United States. In addition to mortality from wind energy generation, the species also faces range-wide threats from the emerging infectious fungal disease, white-nose syndrome (WNS). White-nose syndrome, caused by Pseudogymnoascus destructans, disturbs hibernating bats leading to high levels of mortality. We used a spatially explicit full-annual-cycle model to investigate how wind turbine mortality and WNS may singly and then together affect population dynamics of this species. In the simulation, wind turbine mortality impacted the metapopulation dynamics of the species by causing extirpation of some of the smaller winter colonies. In general, effects of wind turbines were localized and focused on specific spatial subpopulations. Conversely, WNS had a depressive effect on the species across its range. Wind turbine mortality interacted with WNS and together these stressors had a larger impact than would be expected from either alone, principally because these stressors together act to reduce species abundance across the spectrum of population sizes. Our findings illustrate the importance of not only prioritizing the protection of large winter colonies as is currently done, but also of protecting metapopulation dynamics and migratory connectivity.

Quasi-extinction risk and population targets for the Eastern, migratory population of monarch butterflies (Danaus plexippus).

The Eastern, migratory population of monarch butterflies (Danaus plexippus), an iconic North American insect, has declined by ~80% over the last decade. The monarch's multi-generational migration between overwintering grounds in central Mexico and the summer breeding grounds in the northern U.S. and southern Canada is celebrated in all three countries and creates shared management responsibilities across North America. Here we present a novel Bayesian multivariate auto-regressive state-space model to assess quasi-extinction risk and aid in the establishment of a target population size for monarch conservation planning. We find that, given a range of plausible quasi-extinction thresholds, the population has a substantial probability of quasi-extinction, from 11-57% over 20 years, although uncertainty in these estimates is large. Exceptionally high population stochasticity, declining numbers, and a small current population size act in concert to drive this risk. An approximately 5-fold increase of the monarch population size (relative to the winter of 2014-15) is necessary to halve the current risk of quasi-extinction across all thresholds considered. Conserving the monarch migration thus requires active management to reverse population declines, and the establishment of an ambitious target population size goal to buffer against future environmentally driven variability.

Prioritizing Avian Species for Their Risk of Population-Level Consequences from Wind Energy Development.

Recent growth in the wind energy industry has increased concerns about its impacts on wildlife populations. Direct impacts of wind energy include bird and bat collisions with turbines whereas indirect impacts include changes in wildlife habitat and behavior. Although many species may withstand these effects, species that are long-lived with low rates of reproduction, have specialized habitat preferences, or are attracted to turbines may be more prone to declines in population abundance. We developed a prioritization system to identify the avian species most likely to experience population declines from wind facilities based on their current conservation status and their expected risk from turbines. We developed 3 metrics of turbine risk that incorporate data on collision fatalities at wind facilities, population size, life history, species' distributions relative to turbine locations, number of suitable habitat types, and species' conservation status. We calculated at least 1 measure of turbine risk for 428 avian species that breed in the United States. We then simulated 100,000 random sets of cutoff criteria (i.e., the metric values used to assign species to different priority categories) for each turbine risk metric and for conservation status. For each set of criteria, we assigned each species a priority score and calculated the average priority score across all sets of criteria. Our prioritization system highlights both species that could potentially experience population decline caused by wind energy and species at low risk of population decline. For instance, several birds of prey, such as the long-eared owl, ferruginous hawk, Swainson's hawk, and golden eagle, were at relatively high risk of population decline across a wide variety of cutoff values, whereas many passerines were at relatively low risk of decline. This prioritization system is a first step that will help researchers, conservationists, managers, and industry target future study and management activity.

Onshore industrial wind turbine locations for the United States up to March 2014.

Wind energy is a rapidly growing form of renewable energy in the United States. While summary information on the total amounts of installed capacity are available by state, a free, centralized, national, turbine-level, geospatial dataset useful for scientific research, land and resource management, and other uses did not exist. Available in multiple formats and in a web application, these public domain data provide industrial-scale onshore wind turbine locations in the United States up to March 2014, corresponding facility information, and turbine technical specifications. Wind turbine records have been collected and compiled from various public sources, digitized or position verified from aerial imagery, and quality assured and quality controlled. Technical specifications for turbines were assigned based on the wind turbine make and model as described in public literature. In some cases, turbines were not seen in imagery or turbine information did not exist or was difficult to obtain. Uncertainty associated with these is recorded in a confidence rating.