Geology topics

Wayne E. Thogmartin

Publications and source records attributed to Wayne E. Thogmartin.

At least 109 records · Page 6Linked to original sources

USGS2016

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.

PeerJ↗

USGS2016

A trans-national monarch butterfly population model and implications for regional conservation priorities

1. The monarch has undergone considerable population declines over the past decade, and the governments of Mexico, Canada, and the United States have agreed to work together to conserve the species. 2. Given limited resources, understanding where to focus conservation action is key for widespread species like monarchs. To support planning for continental-scale monarch habitat restoration, we address the question of where restoration efforts are likely to have the largest impacts on monarch butterfly ( Danaus plexippus Linn.) population growth rates. 3. We present a spatially explicit demographic model simulating the multi-generational annual cycle of the eastern monarch population, and use the model to examine management scenarios, some of which focus on particular regions of North America. 4. Improving the monarch habitat in the north central or southern parts of the monarch range yields a slightly greater increase in the population growth rate than restoration in other regions. However, combining restoration efforts across multiple regions yields population growth rates above 1 with smaller simulated improvements in habitat per region than single-region strategies. 5. S ynthesis and applications: These findings suggest that conservation investment in projects across the full monarch range will be more effective than focusing on one or a few regions, and will require international cooperation across many land use categories.

Ecological Entomology↗

USGS2016

Evaluation of downscaled, gridded climate data for the conterminous United States

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

Ecological Applications↗

USGS2016

Assessing the sensitivity of avian species abundance to land cover and climate

Climate projections for the Midwestern United States predict southerly climates to shift northward. These shifts in climate could alter distributions of species across North America through changes in climate (i.e., temperature and precipitation), or through climate-induced changes on land cover. Our objective was to determine the relative impacts of land cover and climate on the abundance of five bird species in the Central United States that have habitat requirements ranging from grassland and shrubland to forest. We substituted space for time to examine potential impacts of a changing climate by assessing climate and land cover relationships over a broad latitudinal gradient. We found positive and negative relationships of climate and land cover factors with avian abundances. Habitat variables drove patterns of abundance in migratory and resident species, although climate was also influential in predicting abundance for some species occupying more open habitat (i.e., prairie warbler, blue-winged warbler, and northern bobwhite). Abundance of northern bobwhite increased with winter temperature and was the species exhibiting the most significant effect of climate. Models for birds primarily occupying early successional habitats performed better with a combination of habitat and climate variables whereas models of species found in contiguous forest performed best with land cover alone. These varied species-specific responses present unique challenges to land managers trying to balance species conservation over a variety of land covers. Management activities focused on increasing forest cover may play a role in mitigating effects of future climate by providing habitat refugia to species vulnerable to projected changes. Conservation efforts would be best served focusing on areas with high species abundances and an array of habitats. Future work managing forests for resilience and resistance to climate change could benefit species already susceptible to climate impacts.

Ecosphere↗

USGS2016

Past and predicted future effects of housing growth on open space conservation opportunity areas and habitat connectivity around National Wildlife Refuges

Context Housing growth can alter suitability of matrix habitats around protected areas, strongly affecting movements of organisms and, consequently, threatening connectivity of protected area networks. Objectives Our goal was to quantify distribution and growth of housing around the U.S. Fish and Wildlife Service National Wildlife Refuge System. This is important information for conservation planning, particularly given promotion of habitat connectivity as a climate change adaptation measure. Methods We quantified housing growth from 1940 to 2000 and projected future growth to 2030 within three distances from refuges, identifying very low housing density open space, “opportunity areas” (contiguous areas with <6.17 houses/km 2 ), both nationally and by USFWS administrative region. Additionally, we quantified number and area of habitat corridors within these opportunity areas in 2000. Results Our results indicated that the number and area of open space opportunity areas generally decreased with increasing distance from refuges and with the passage of time. Furthermore, total area in habitat corridors was much lower than in opportunity areas. In addition, the number of corridors sometimes exceeded number of opportunity areas as a result of habitat fragmentation, indicating corridors are likely vulnerable to land use change. Finally, regional differences were strong and indicated some refuges may have experienced so much housing growth already that they are effectively too isolated to adapt to climate change, while others may require extensive habitat restoration work. Conclusions Wildlife refuges are increasingly isolated by residential housing development, potentially constraining the movement of wildlife and, therefore, their ability to adapt to a changing climate.

Landscape Ecology↗

USGS2016

A generalizable energetics-based model of avian migration to facilitate continental-scale waterbird conservation

Conserving migratory birds is made especially difficult because of movement among spatially disparate locations across the annual cycle. In light of challenges presented by the scale and ecology of migratory birds, successful conservation requires integrating objectives, management, and monitoring across scales, from local management units to ecoregional and flyway administrative boundaries. We present an integrated approach using a spatially explicit energetic-based mechanistic bird migration model useful to conservation decision-making across disparate scales and locations. This model moves a mallard-like bird (Anas platyrhynchos), through spring and fall migration as a function of caloric gains and losses across a continental scale energy landscape. We predicted with this model that fall migration, where birds moved from breeding to wintering habitat, took a mean of 27.5 days of flight with a mean seasonal survivorship of 90.5% (95% CI = 89.2%, 91.9%) whereas spring migration took a mean of 23.5 days of flight with mean seasonal survivorship of 93.6% (95% CI = 92.5%, 94.7%). Sensitivity analyses suggested that survival during migration was sensitive to flight speed, flight cost, the amount of energy the animal could carry and the spatial pattern of energy availability, but generally insensitive to total energy availability per se. Nevertheless, continental patterns in the bird-use days occurred principally in relation to wetland cover and agricultural habitat in the fall. Bird-use days were highest in both spring and fall in the Mississippi Alluvial Valley and along the coast and near-shore environments of South Carolina. Spatial sensitivity analyses suggested that locations nearer to migratory endpoints were less important to survivorship; for instance, removing energy from a 1,036 km2 stopover site at a time from the Atlantic Flyway suggested coastal areas between New Jersey and North Carolina, including Chesapeake Bay and the North Carolina piedmont, are essential locations for efficient migration and increasing survivorship during spring migration but not locations in Ontario and Massachusetts. This sort of spatially explicit information may allow decision-makers to prioritize their conservation actions toward locations most influential to migratory success. Thus, this mechanistic model of avian migration provides a decision-analytic medium integrating the potential consequences of local actions to flyway-scale phenomena.

Ecological Applications↗

USGS2016

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.

Scientific Reports↗

USGS2016

Developing population models with data from marked individuals

Population viability analysis (PVA) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density-dependence, environmental stochasticity, and specification of uncertainties. Developing a fully specified population model from commonly available data sources – notably, mark–recapture studies – remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. We present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark–recapture dataset. Unlike standard mark–recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. Furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. We apply this approach to 9 bird species and demonstrate the feasibility of using data from the Monitoring Avian Productivity and Survivorship (MAPS) program. Bias-correction factors for raw estimates of survival and fecundity derived from mark–recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. Our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of PVA. This method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern.

Biological Conservation↗

USGS2016

Future frequencies of extreme weather events in the National Wildlife Refuges of the conterminous U.S.

Climate change is a major challenge for managers of protected areas world-wide, and managers need information about future climate conditions within protected areas. Prior studies of climate change effects in protected areas have largely focused on average climatic conditions. However, extreme weather may have stronger effects on wildlife populations and habitats than changes in averages. Our goal was to quantify future changes in the frequency of extreme heat, drought, and false springs, during the avian breeding season, in 415 National Wildlife Refuges in the conterminous United States. We analyzed spatially detailed data on extreme weather frequencies during the historical period (1950–2005) and under different scenarios of future climate change by mid- and late-21st century. We found that all wildlife refuges will likely experience substantial changes in the frequencies of extreme weather, but the types of projected changes differed among refuges. Extreme heat is projected to increase dramatically in all wildlife refuges, whereas changes in droughts and false springs are projected to increase or decrease on a regional basis. Half of all wildlife refuges are projected to see increases in frequency (> 20% higher than the current rate) in at least two types of weather extremes by mid-century. Wildlife refuges in the Southwest and Pacific Southwest are projected to exhibit the fastest rates of change, and may deserve extra attention. Climate change adaptation strategies in protected areas, such as the U.S. wildlife refuges, may need to seriously consider future changes in extreme weather, including the considerable spatial variation of these changes.

Biological Conservation↗

USGS2016

Flexible risk metrics for identifying and monitoring conservation-priority species

Region-specific conservation programs should have objective, reliable metrics for species prioritization and progress evaluation that are customizable to the goals of a program, easy to comprehend and communicate, and standardized across time. Regional programs may have vastly different goals, spatial coverage, or management agendas, and one-size-fits-all schemes may not always be the best approach. We propose a quantitative and objective framework for generating metrics for prioritizing species that is straightforward to implement and update, customizable to different spatial resolutions, and based on readily available time-series data. This framework is also well-suited to handling missing-data and observer error. We demonstrate this approach using North American Breeding Bird Survey (NABBS) data to identify conservation priority species from a list of over 300 landbirds across 33 bird conservation regions (BCRs). To highlight the flexibility of the framework for different management goals and timeframes we calculate two different metrics. The first identifies species that may be inadequately monitored by NABBS protocols in the near future (TMT, time to monitoring threshold), and the other identifies species likely to decline significantly in the near future based on recent trends (TPD, time to percent decline). Within the individual BCRs we found up to 45% (mean 28%) of the species analyzed had overall declining population trajectories, which could result in up to 37 species declining below a minimum NABBS monitoring threshold in at least one currently occupied BCR within the next 50 years. Additionally, up to 26% (mean 8%) of the species analyzed within the individual BCRs may decline by 30% within the next decade. Conservation workers interested in conserving avian diversity and abundance within these BCRs can use these metrics to plan alternative monitoring schemes or highlight the urgency of those populations experiencing the fastest declines. However, this framework is adaptable to many taxa besides birds where abundance time-series data are available.

Ecological Indicators↗

USGS2016

Using the North American Breeding Bird Survey to assess broad-scale response of the continent's most imperiled avian community, grassland birds, to weather variability

Avian populations can respond dramatically to extreme weather such as droughts and heat waves, yet patterns of response to weather at broad scales remain largely unknown. Our goal was to evaluate annual variation in abundance of 14 grassland bird species breeding in the northern mixed-grass prairie in relation to annual variation in precipitation and temperature. We modeled avian abundance during the breeding season using North American Breeding Bird Survey (BBS) data for the U.S. Badlands and Prairies Bird Conservation Region (BCR 17) from 1980 to 2012. We used hierarchical Bayesian methods to fit models and estimate the candidate weather parameters standardized precipitation index (SPI) and standardized temperature index (STI) for the same year and the previous year. Upland Sandpiper ( Bartramia longicauda ) responded positively to within-year STI (β = 0.101), and Baird's Sparrow ( Ammodramus bairdii ) responded negatively to within-year STI (β = −0.161) and positively to within-year SPI (β = 0.195). The parameter estimates were superficially similar (STI β = −0.075, SPI β = 0.11) for Grasshopper Sparrow ( Ammodramus savannarum ), but the best-selected model included an interaction between SPI and STI. The best model for both Eastern Kingbird ( Tyrannus tyrannus ) and Vesper Sparrow ( Pooecetes gramineus ) included the additive effects of within-year SPI (β = −0.032 and β = −0.054, respectively) and the previous-year's SPI (β = −0.057 and −0.02, respectively), although for Vesper Sparrow the lag effect was insignificant. With projected warmer, drier weather during summer in the Badlands and Prairies BCR, Baird's and Grasshopper sparrows may be especially threatened by future climate change.

The Condor↗

USGS2016

Factors affecting nest survival of Henslow's Sparrows ( Ammodramus henslowii ) in southern Indiana

Populations of Henslow’s Sparrows have declined dramatically in recent decades, coinciding with widespread loss of native grassland habitat. Prescribed burning is a primary tool for maintaining grassland patches, but its effects on nest survival of Henslow’s Sparrows remains largely unknown, especially in conjunction with other factors. We monitored 135 nests of Henslow’s Sparrows at Big Oaks National Wildlife Refuge in southern Indiana from 1998–2001 in an effort to understand factors influencing nest survival, including prescribed burning of habitat. We used a mixed-effects implementation of the logistic exposure model to predict daily nest survival in an information theoretic framework. We found that daily survival declined near the onset of hatching and increased with the height of standing dead vegetation, although this relationship was weak. We found only nominal support to suggest that time since burn influenced nest survival. Overall, nest age was the most important factor in estimating daily nest survival rates. Our daily survival estimate from our marginal model (0.937) was similar to that derived from the Mayfield method (0.944) suggesting that our results are comparable to previous studies using the Mayfield approach. Our results indicate that frequent burning to limit woody encroachment into grassland habitats might benefit Henslow’s Sparrow, but that a variety of factors ultimately influence daily nest survival. However, we note that burning too frequently can also limit occupancy by Henslow’s Sparrows. We suggest that additional research is needed to determine the population-level consequences of habitat alteration and if other extrinsic factors influence demographics of Henslow’s Sparrows.

Indiana↗

USGS2016

A management-oriented framework for selecting metrics used to assess habitat- and path-specific quality in spatially structured populations

Mobile species with complex spatial dynamics can be difficult to manage because their population distributions vary across space and time, and because the consequences of managing particular habitats are uncertain when evaluated at the level of the entire population. Metrics to assess the importance of habitats and pathways connecting habitats in a network are necessary to guide a variety of management decisions. Given the many metrics developed for spatially structured models, it can be challenging to select the most appropriate one for a particular decision. To guide the management of spatially structured populations, we define three classes of metrics describing habitat and pathway quality based on their data requirements (graph-based, occupancy-based, and demographic-based metrics) and synopsize the ecological literature relating to these classes. Applying the first steps of a formal decision-making approach (problem framing, objectives, and management actions), we assess the utility of metrics for particular types of management decisions. Our framework can help managers with problem framing, choosing metrics of habitat and pathway quality, and to elucidate the data needs for a particular metric. Our goal is to help managers to narrow the range of suitable metrics for a management project, and aid in decision-making to make the best use of limited resources.

Ecological Indicators↗

USGS2016

Dynamic distributions and population declines of Golden-winged Warblers

With an estimated breeding population in 2010 of 383,000 pairs, the Golden-winged Warbler ( Vermivora chrysoptera ) is among the most vulnerable and steeply declining of North American passerines. This species also has exhibited among the most dynamic breeding distributions, with populations expanding and then contracting over the past 150 years in response to regional habitat changes, interactions with closely related Blue-winged Warblers ( V. cyanoptera ), and possibly climate change. Since 1966, the rangewide population has declined by >70% (-2.3% per year; latest North American Breeding Bird Survey data), with much steeper declines in the Appalachian Mountains bird conservation region (-8.3% per year, 98% overall decline). Despite apparently stable or increasing populations in the northwestern part of the range (Minnesota, Manitoba), population estimates for Golden-winged Warbler have continued to decline by 18% from the decade of the 1990s to the 2000s. Population modeling predicts a further decline to roughly 37,000 individuals by 2100, with the species likely to persist only in Manitoba, Minnesota, and possibly Ontario. To delineate the present-day distribution and to identify population concentrations that could serve as conservation focus areas, we compiled rangewide survey data collected in 2000-2006 in 21 states and 3 Canadian provinces, as part of the Golden-winged Warbler Atlas Project (GOWAP), supplemented by state and provincial Breeding Bird Atlas data and more recent observations in eBird. Based on >8,000 GOWAP surveys for Golden-winged and Blue-winged warblers and their hybrids, we mapped occurrence of phenotypically pure and mixed populations in a roughly 0.5-degree grid across the species’ ranges. Hybrids and mixed Golden-winged-Blue-winged populations occurred in a relatively narrow zone across Minnesota, Wisconsin, Michigan, southern Ontario, and northern New York. Phenotypically pure Golden-winged Warbler populations occurred north of this hybrid zone, but the future of northern populations in the Great Lakes states and Canada (where >80% of the species occurs at present) is highly uncertain because of continued northward expansion of Blue-winged Warblers and hybridization. A second, now-disjunct band of Golden-winged Warbler populations exists in the Appalachian Mountains from southeastern New York to northern Georgia, surrounded at lower elevations by Blue-winged Warblers. Important concentrations of Golden-winged Warblers persist in the Allegheny Mountains region of West Virginia, the Cumberland Mountains in Tennessee, Blue Ridge Mountains of western North Carolina, Allegheny Plateau and Pocono Mountains in Pennsylvania, and in the Hudson Highlands of southern New York. These high-elevation Appalachian populations have escaped contact with Blue-winged Warblers until very recently and represent important refugia for conservation and management; other Appalachian populations are rapidly declining. In addition, based on historical records and standardized surveys across the wintering grounds, we identified three regions of concentration: highlands and Caribbean slopes from Guatemala and Belize to northwestern Nicaragua; middle elevations (both slopes) in Costa Rica and western Panama; and in an arc of the northern Andes from central Colombia to northern Venezuela. It is possible that the winter range has been shifting towards the northwest in recent decades, paralleling shifts in the breeding distribution. Future conservation efforts for Golden-winged Warbler need to include close monitoring of the dynamic phenotypic and genetic distributional shifts, and may need to consider the “winged warbler” complex together as a highly adaptable evolutionary unit.

Studies in Avian Biology↗

USGS2015

The pace of past climate change vs. potential bird distributions and land use in the United States

Climate change may drastically alter patterns of species distributions and richness, but predicting future species patterns in occurrence is challenging. Significant shifts in distributions have already been observed, and understanding these recent changes can improve our understanding of potential future changes. We assessed how past climate change affected potential breeding distributions for landbird species in the conterminous United States. We quantified the bioclimatic velocity of potential breeding distributions, that is, the pace and direction of change for each species’ suitable climate space over the past 60 years. We found that potential breeding distributions for landbirds have shifted substantially with an average velocity of 1.27 km yr −1 , about double the pace of prior distribution shift estimates across terrestrial systems globally (0.61 km yr −1 ). The direction of shifts was not uniform. The majority of species’ distributions shifted west, northwest, and north. Multidirectional shifts suggest that changes in climate conditions beyond mean temperature were influencing distributional changes. Indeed, precipitation variables that were proxies for extreme conditions were important variables across all models. There were winners and losers in terms of the area of distributions; many species experienced contractions along west and east distribution edges, and expansions along northern distribution edges. Changes were also reflected in the potential species richness, with some regions potentially gaining species (Midwest, East) and other areas potentially losing species (Southwest). However, the degree to which changes in potential breeding distributions are manifested in actual species richness depends on landcover. Areas that have become increasingly suitable for breeding birds due to changing climate are often those attractive to humans for agriculture and development. This suggests that many areas might have supported more breeding bird species had the landscape not been altered. Our study illustrates that climate change is not only a future threat, but something birds are already experiencing.

Global Change Biology↗

USGS2015

Assessing local population vulnerability to wind energy development with branching process models: an application to wind energy development

Quantifying the impact of anthropogenic development on local populations is important for conservation biology and wildlife management. However, these local populations are often subject to demographic stochasticity because of their small population size. Traditional modeling efforts such as population projection matrices do not consider this source of variation whereas individual-based models, which include demographic stochasticity, are computationally intense and lack analytical tractability. One compromise between approaches is branching process models because they accommodate demographic stochasticity and are easily calculated. These models are known within some sub-fields of probability and mathematical ecology but are not often applied in conservation biology and applied ecology. We applied branching process models to quantitatively compare and prioritize species locally vulnerable to the development of wind energy facilities. Specifically, we examined species vulnerability using branching process models for four representative species: A cave bat (a long-lived, low fecundity species), a tree bat (short-lived, moderate fecundity species), a grassland songbird (a short-lived, high fecundity species), and an eagle (a long-lived, slow maturation species). Wind turbine-induced mortality has been observed for all of these species types, raising conservation concerns. We simulated different mortality rates from wind farms while calculating local extinction probabilities. The longer-lived species types (e.g., cave bats and eagles) had much more pronounced transitions from low extinction risk to high extinction risk than short-lived species types (e.g., tree bats and grassland songbirds). High-offspring-producing species types had a much greater variability in baseline risk of extinction than the lower-offspring-producing species types. Long-lived species types may appear stable until a critical level of incidental mortality occurs. After this threshold, the risk of extirpation for a local population may rapidly increase with only minimal increases in wind mortality. Conservation biologists and wildlife managers may need to consider this mortality pattern when issuing take permits and developing monitoring protocols for wind facilities. We also describe how our branching process models may be generalized across a wider range of species for a larger assessment project and then describe how our methods may be applied to other stressors in addition to wind.

Ecosphere↗

USGS2015

Spring plant phenology and false springs in the conterminous US during the 21st century

The onset of spring plant growth has shifted earlier in the year over the past several decades due to rising global temperatures. Earlier spring onset may cause phenological mismatches between the availability of plant resources and dependent animals, and potentially lead to more false springs, when subsequent freezing temperatures damage new plant growth. We used the extended spring indices to project changes in spring onset, defined by leaf out and by first bloom, and predicted false springs until 2100 in the conterminous United States (US) using statistically-downscaled climate projections from the Coupled Model Intercomparison Project 5 ensemble. Averaged over our study region, the median shift in spring onset was 23 days earlier in the Representative Concentration Pathway 8.5 scenario with particularly large shifts in the Western US and the Great Plains. Spatial variation in phenology was due to the influence of short-term temperature changes around the time of spring onset versus season long accumulation of warm temperatures. False spring risk increased in the Great Plains and portions of the Midwest, but remained constant or decreased elsewhere. We conclude that global climate change may have complex and spatially variable effects on spring onset and false springs, making local predictions of change difficult.

Geophysical Research Letters↗

USGS2015

Relating mesocarnivore relative abundance to anthropogenic land-use with a hierarchical spatial count model

There is growing need to develop models of spatial patterns in animal abundance, yet comparatively few examples of such models exist. This is especially true in situations where the abundance of one species may inhibit that of another, such as the intensively-farmed landscape of the Prairie Pothole Region (PPR) of the central United States, where waterfowl production is largely constrained by mesocarnivore nest predation. We used a hierarchical Bayesian approach to relate the distribution of various land-cover types to the relative abundances of four mesocarnivores in the PPR: coyote Canis latrans, raccoon Procyon lotor, red fox Vulpes vulpes, and striped skunk Mephitis mephitis. We developed models for each species at multiple spatial resolutions (41.4 km2, 10.4 km2, and 2.6 km2) to address different ecological and management-related questions. Model results for each species were similar irrespective of resolution. We found that the amount of row-crop agriculture was nearly ubiquitous in our best models, exhibiting a positive relationship with relative abundance for each species. The amount of native grassland land-cover was positively associated with coyote and raccoon relative abundance, but generally absent from models for red fox and skunk. Red fox and skunk were positively associated with each other, suggesting potential niche overlap. We found no evidence that coyote abundance limited that of other mesocarnivore species, as might be expected under a hypothesis of mesopredator release. The relationships between relative abundance and land-cover types were similar across spatial resolutions. Our results indicated that mesocarnivores in the PPR are most likely to occur in portions of the landscape with large amounts of agricultural land-cover. Further, our results indicated that track-survey data can be used in a hierarchical framework to gain inferences regarding spatial patterns in animal relative abundance.

Iowa, Minnesota↗