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USGS2021

Demographic and potential biological removal models identify raptor species sensitive to current and future wind energy

A central challenge in applied ecology is understanding the effect of anthropogenic fatalities on wildlife populations and predicting which populations may be particularly vulnerable and in greatest need of management attention. We used 3 approaches to investigate potential effects of fatalities from collisions with wind turbines on 14 raptor species for both current (106 GW) and anticipated future (241 GW) levels of installed wind energy capacity in the United States. Our goals were to identify species at relatively high vs low risk of experiencing population declines from turbine collisions and to also compare results generated from these approaches. Two of the approaches used a calculated turbine-caused mortality rate to decrement population growth, where population trends were derived either from the North American Breeding Bird Survey or a matrix model parameterized from literature-derived demographic values. The third approach was potential biological removal, which estimates the number of fatalities that allow a population to reach and maintain its optimal sustainable population set by management concerns. Different results among the methods reveal substantial gaps in knowledge and uncertainty in both demographic parameters and species-specific estimates of fatalities from wind turbines. Our results suggest that, of the 14 species studied, those with relatively higher potential of population-level impacts from wind turbine collisions included barn owl, ferruginous hawk, golden eagle, American kestrel, and red-tailed hawk. Burrowing owl, Cooper’s hawk, great horned owl, northern harrier, turkey vulture, and osprey had a relatively lower potential for population impacts, and results were not easily interpretable for merlin, prairie falcon, and Swainson’s hawk. Projections of current levels of fatalities to future wind energy scenarios at 241 GW of installed capacity suggest some species could experience population declines because of turbine collisions. Populations of those species may benefit from research to identify tools to prevent or reduce raptor collisions with wind turbines.

Ecosphere↗

USGS2015

Spatially explicit modeling of blackbird abundance in the Prairie Pothole Region

Knowledge of factors influencing animal abundance is important to wildlife biologists developing management plans. This is especially true for economically important species such as blackbirds (Icteridae), which cause more than $100 million in crop damages annually in the United States. Using data from the North American Breeding Bird Survey, the National Land Cover Dataset, and the National Climatic Data Center, we modeled effects of regional environmental variables on relative abundance of 3 blackbird species (red-winged blackbird, Agelaius phoeniceus ; yellow-headed blackbird, Xanthocephalus xanthocephalus ; common grackle, Quiscalus quiscula ) in the Prairie Pothole Region of the central United States. We evaluated landscape covariates at 3 logarithmically related spatial scales (1,000 ha, 10,000 ha, and 100,000 ha) and modeled weather variables at the 100,000-ha scale. We constructed models a priori using information from published habitat associations. We fit models with WinBUGS using Markov chain Monte Carlo techniques. Both landscape and weather variables contributed strongly to predicting blackbird relative abundance (95% credibility interval did not overlap 0). Variables with the strongest associations with blackbird relative abundance were the percentage of wetland area and precipitation amount from the year before bird surveys were conducted. The influence of spatial scale appeared small—models with the same variables expressed at different scales were often in the best model subset. This large-scale study elucidated regional effects of weather and landscape variables, suggesting that management strategies aimed at reducing damages caused by these species should consider the broader landscape, including weather effects, because such factors may outweigh the influence of localized conditions or site-specific management actions. The regional species distributional models we developed for blackbirds provide a tool for understanding these broader landscape effects and guiding wildlife management practices to areas that are optimally beneficial. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Iowa, Minnesotta, Montana, Nebraska, North Dakota↗

USGS2004

The relationship between species detection probability and local extinction probability

In community-level ecological studies, generally not all species present in sampled areas are detected. Many authors have proposed the use of estimation methods that allow detection probabilities that are < 1 and that are heterogeneous among species. These methods can also be used to estimate community-dynamic parameters such as species local extinction probability and turnover rates (Nichols et al. Ecol Appl 8:1213-1225; Conserv Biol 12:1390-1398). Here, we present an ad hoc approach to estimating community-level vital rates in the presence of joint heterogeneity of detection probabilities and vital rates. The method consists of partitioning the number of species into two groups using the detection frequencies and then estimating vital rates (e.g., local extinction probabilities) for each group. Estimators from each group are combined in a weighted estimator of vital rates that accounts for the effect of heterogeneity. Using data from the North American Breeding Bird Survey, we computed such estimates and tested the hypothesis that detection probabilities and local extinction probabilities were negatively related. Our analyses support the hypothesis that species detection probability covaries negatively with local probability of extinction and turnover rates. A simulation study was conducted to assess the performance of vital parameter estimators as well as other estimators relevant to questions about heterogeneity, such as coefficient of variation of detection probabilities and proportion of species in each group. Both the weighted estimator suggested in this paper and the original unweighted estimator for local extinction probability performed fairly well and provided no basis for preferring one to the other.

Oecologia↗

USGS2005

Efficient statistical mapping of avian count data

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Environmental and Ecological Statistics↗

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↗

USGS2013

Gonadal abnormalities in frogs (Lithobates spp.) collected from managed wetlands in an agricultural region of Nebraska, USA

Nebraska's Rainwater Basin (RWB) provides important wetland habitat for North American migratory birds. Concern exists that pesticide and nutrient runoff from surrounding row-crops enters wetlands degrading water quality and adversely affecting birds and wildlife. Frogs may be especially vulnerable. Plains leopard (Lithobates blairi) metamorphs from RWB wetlands with varying concentrations of pesticides were evaluated for a suite of biomarkers of exposure to endocrine active chemicals. Froglets had ovarian dysgenesis, high rates of testicular oocytes, and female-biased sex ratios however, there was no clear statistical association between pesticide concentrations and biomarkers. Data interpretation was hindered because timing and duration of exposures were unknown and due to an incomplete understanding of L. blairi sexual development. Emphasis is on describing the complex developmental biology of closely-related leopard frogs, how this understanding can explain RWB L. blairi anomalies, and the need for sampling at the appropriate life stage.

Nebraska↗

USGS2002

On the estimation of species richness based on the accumulation of previously unrecorded species

Estimation of species richness of local communities has become an important topic in community ecology and monitoring. Investigators can seldom enumerate all the species present in the area of interest during sampling sessions. If the location of interest is sampled repeatedly within a short time period, the number of new species recorded is typically largest in the initial sample and decreases as sampling proceeds, but new species may be detected if sampling sessions are added. The question is how to estimate the total number of species. The data collected by sampling the area of interest repeatedly can be used to build species-accumulation curves: the cumulative number of species recorded as a function of the number of sampling sessions (which we refer to as ?species-accumulation data?). A classic approach used to compute total species richness is to fit curves to the data on species accumulation with sampling effort. This approach does not rest on direct estimation of the probability of detecting species during sampling sessions and has no underlying basis regarding the sampling process that gave rise to the data. Here we recommend a probabilistic, nonparametric estimator for species richness for use with species-accumulation data. We use estimators of population size that were developed for capture-recapture data, but that can be used to estimate the size of species assemblages using species-accumulation data. Models of detection probability account for the underlying sampling process. They permit variation in detection probability among species. We illustrate this approach using data from the North American Breeding Bird Survey (BBS). We describe other situations where species accumulation data are collected under different designs (e.g., over longer periods of time, or over spatial replicates) and that lend themselves to use of capture-recapture models for estimating the size of the community of interest. We discuss the assumptions and interpretations corresponding to each situation.

Alabama, Louisiana, Maine, Maryland, New Hampshire↗

USGS2003

Inferences about nested subsets structure when not all species are detected

Comparisons of species composition among ecological communities of different size have often provided evidence that the species in communities with lower species richness form nested subsets of the species in larger communities. In the vast majority of studies, the question of nested subsets has been addressed using information on presence-absence, where a '0' is interpreted as the absence of a given species from a given location. Most of the methodological discussion in earlier studies investigating nestedness concerns the approach to generation of model-based matrices. However, it is most likely that in many situations investigators cannot detect all the species present in the location sampled. The possibility that zeros in incidence matrices reflect nondetection rather than absence of species has not been considered in studies addressing nested subsets, even though the position of zeros in these matrices forms the basis of earlier inference methods. These sampling artifacts are likely to lead to erroneous conclusions about both variation over space in species richness and the degree of similarity of the various locations. Here we propose an approach to investigation of nestedness, based on statistical inference methods explicitly incorporating species detection probability, that take into account the probabilistic nature of the sampling process. We use presence-absence data collected under Pollock?s robust capture-recapture design, and resort to an estimator of species richness originally developed for closed populations to assess the proportion of species shared by different locations. We develop testable predictions corresponding to the null hypothesis of a nonnested pattern, and an alternative hypothesis of perfect nestedness. We also present an index for assessing the degree of nestedness of a system of ecological communities. We illustrate our approach using avian data from the North American Breeding Bird Survey collected in Florida Keys.

Florida↗

USGS2009

COMDYN: Software to study the dynamics of animal communities using a capture-recapture approach

COMDYN is a set of programs developed for estimation of parameters associated with community dynamics using count data from two locations or time periods. It is Internet-based, allowing remote users either to input their own data, or to use data from the North American Breeding Bird Survey for analysis. COMDYN allows probability of detection to vary among species and among locations and time periods. The basic estimator for species richness underlying all estimators is the jackknife estimator proposed by Burnham and Overton. Estimators are presented for quantities associated with temporal change in species richness, including rate of change in species richness over time, local extinction probability, local species turnover and number of local colonizing species. Estimators are also presented for quantities associated with spatial variation in species richness, including relative richness at two locations and proportion of species present in one location that are also present at a second location. Application of the estimators to species richness estimation has been previously described and justified. The potential applications of these programs are discussed.

Bird Study↗

USGS2014

Multiseason occupancy models for correlated replicate surveys

Occupancy surveys collecting data from adjacent (sometimes correlated) spatial replicates have become relatively popular for logistical reasons. Hines et al . ( 2010 ) presented one approach to modelling such data for single-season occupancy surveys. Here, we present a multiseason analogue of this model (with corresponding software) for inferences about occupancy dynamics. We include a new parameter to deal with the uncertainty associated with the first spatial replicate for both single-season and multiseason models. We use a case study, based on the brown-headed nuthatch, to assess the need for these models when analysing data from the North American Breeding Bird Survey (BBS), and we test various hypotheses about occupancy dynamics for this species in the south-eastern United States. The new model permits inference about local probabilities of extinction, colonization and occupancy for sampling conducted over multiple seasons. The model performs adequately, based on a small simulation study and on results of the case study analysis. The new model incorporating correlated replicates was strongly favoured by model selection for the BBS data for brown-headed nuthatch ( Sitta pusilla ). Latitude was found to be an important source of variation in local colonization and occupancy probabilities for brown-headed nuthatch, with both probabilities being higher near the centre of the species range, as opposed to more northern and southern areas. We recommend this new occupancy model for detection–nondetection studies that use potentially correlated replicates.

North Carolina, South Carolina, Florida, Georgia↗

USGS2015

A centroid model of species distribution with applications to the Carolina wren Thryothorus ludovicianus and house finch Haemorhous mexicanus in the United States

Drastic shifts in species distributions are a cause of concern for ecologists. Such shifts pose great threat to biodiversity especially under unprecedented anthropogenic and natural disturbances. Many studies have documented recent shifts in species distributions. However, most of these studies are limited to regional scales, and do not consider the abundance structure within species ranges. Developing methods to detect systematic changes in species distributions over their full ranges is critical for understanding the impact of changing environments and for successful conservation planning. Here, we demonstrate a centroid model for range-wide analysis of distribution shifts using the North American Breeding Bird Survey. The centroid model is based on a hierarchical Bayesian framework which models population change within physiographic strata while accounting for several factors affecting species detectability. Yearly abundance-weighted range centroids are estimated. As case studies, we derive annual centroids for the Carolina wren and house finch in their ranges in the U.S. We further evaluate the first-difference correlation between species’ centroid movement and changes in winter severity, total population abundance. We also examined associations of change in centroids from sub-ranges. Change in full-range centroid movements of Carolina wren significantly correlate with snow cover days (r = −0.58). For both species, the full-range centroid shifts also have strong correlation with total abundance (r = 0.65, and 0.51 respectively). The movements of the full-range centroids of the two species are correlated strongly (up to r = 0.76) with that of the sub-ranges with more drastic population changes. Our study demonstrates the usefulness of centroids for analyzing distribution changes in a two-dimensional spatial context. Particularly it highlights applications that associate the centroid with factors such as environmental stressors, population characteristics, and progression of invasive species. Routine monitoring of changes in centroid will provide useful insights into long-term avian responses to environmental changes.

Ecography↗

USGS2012

Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions

1. Understanding the factors affecting species occurrence is a pre-eminent focus of applied ecological research. However, direct information about species occurrence is lacking for many species. Instead, researchers sometimes have to rely on so-called presence-only data (i.e. when no direct information about absences is available), which often results from opportunistic, unstructured sampling. MAXENT is a widely used software program designed to model and map species distribution using presence-only data. 2. We provide a critical review of MAXENT as applied to species distribution modelling and discuss how it can lead to inferential errors. A chief concern is that MAXENT produces a number of poorly defined indices that are not directly related to the actual parameter of interest – the probability of occurrence ( ψ ). This focus on an index was motivated by the belief that it is not possible to estimate ψ from presence-only data; however, we demonstrate that ψ is identifiable using conventional likelihood methods under the assumptions of random sampling and constant probability of species detection. 3. The model is implemented in a convenient r package which we use to apply the model to simulated data and data from the North American Breeding Bird Survey. We demonstrate that MAXENT produces extreme under-predictions when compared to estimates produced by logistic regression which uses the full (presence/absence) data set. We note that MAXENT predictions are extremely sensitive to specification of the background prevalence, which is not objectively estimated using the MAXENT method. 4. As with MAXENT, formal model-based inference requires a random sample of presence locations. Many presence-only data sets, such as those based on museum records and herbarium collections, may not satisfy this assumption. However, when sampling is random, we believe that inference should be based on formal methods that facilitate inference about interpretable ecological quantities instead of vaguely defined indices.

Methods in Ecology and Evolution↗

USGS2005

Estimating size and composition of biological communities by modeling the occurrence of species

We develop a model that uses repeated observations of a biological community to estimate the number and composition of species in the community. Estimators of community-level attributes are constructed from model-based estimators of occurrence of individual species that incorporate imperfect detection of individuals. Data from the North American Breeding Bird Survey are analyzed to illustrate the variety of ecologically important quantities that are easily constructed and estimated using our model-based estimators of species occurrence. In particular, we compute site-specific estimates of species richness that honor classical notions of species-area relationships. We suggest extensions of our model to estimate maps of occurrence of individual species and to compute inferences related to the temporal and spatial dynamics of biological communities.

Journal of the American Statistical Association↗

USGS2013

Validating predictions from climate envelope models

Climate envelope models are a potentially important conservation tool, but their ability to accurately forecast species’ distributional shifts using independent survey data has not been fully evaluated. We created climate envelope models for 12 species of North American breeding birds previously shown to have experienced poleward range shifts. For each species, we evaluated three different approaches to climate envelope modeling that differed in the way they treated climate-induced range expansion and contraction, using random forests and maximum entropy modeling algorithms. All models were calibrated using occurrence data from 1967–1971 (t1) and evaluated using occurrence data from 1998–2002 (t2). Model sensitivity (the ability to correctly classify species presences) was greater using the maximum entropy algorithm than the random forest algorithm. Although sensitivity did not differ significantly among approaches, for many species, sensitivity was maximized using a hybrid approach that assumed range expansion, but not contraction, in t2. Species for which the hybrid approach resulted in the greatest improvement in sensitivity have been reported from more land cover types than species for which there was little difference in sensitivity between hybrid and dynamic approaches, suggesting that habitat generalists may be buffered somewhat against climate-induced range contractions. Specificity (the ability to correctly classify species absences) was maximized using the random forest algorithm and was lowest using the hybrid approach. Overall, our results suggest cautious optimism for the use of climate envelope models to forecast range shifts, but also underscore the importance of considering non-climate drivers of species range limits. The use of alternative climate envelope models that make different assumptions about range expansion and contraction is a new and potentially useful way to help inform our understanding of climate change effects on species.

PLoS ONE↗

USGS2007

Population-specific demographic estimates provide insights into declines of Lark Buntings ( Calamospiza melanocorys )

Many North American prairie bird populations have recently declined, and the causes of these declines remain largely unknown. To determine whether population limitation occurs during breeding, we evaluated the stability of a population of prairie birds using population-specific values for fecundity and postfledging survival. During 2001-2003, we radiomarked 67 female Lark Buntings ( Calamospiza melanocorys ) to determine annual fecundity and evaluate contributing factors such as nest survival and breeding response (number of breeding attempts and dispersal). Collectively, 67 females built 112 nests (1.67 ± 0.07 nests female −1 season −1 ; range: 1–3); 34 were second nests and 11 were third nests. Daily nest survival estimates were similar for initial and later nests with overall nest survival (DSR 19 ) of 30.7% and 31.7%, respectively. Nest predation was the most common cause of failure (92%). Capture and radiomarking of females did not affect nest survival. Lark Bunting dispersal probabilities increased among females that fledged young from initial nests and females that lost their original nests late in the season. Conservative and liberal estimates of mean annual fecundity were 0.96 ±0.11 and 1.24 ± 0.09 female offspring per female, respectively. Given the fecundity and juvenile-survival estimates for this population, annual adult survival values of 71–77% are necessary to achieve a stable population. Because adult survival of prairie passerines ranges between 55% and 65%, this study area may not be capable of sustaining a stable population in the absence of immigration. We contrast our population assessment with one that assumes indirect values of fecundity and juvenile survival. To elucidate limiting factors, estimation of population-specific demographic parameters is desirable. We present an approach for selecting species and areas for evaluation of population stability.

The Auk↗

USGS2002

Assessing avian richness in remnant wetlands: Towards an improved methodology

Because the North American Breeding Bird Survey provides inadequate coverage of wetland habitat, the Wetland Breeding Bird Survey was recently established in Ohio, USA. This program relies on volunteers to conduct 3 counts at each monitored wetland. Currently, all counts are conducted during the morning. Under the premise that volunteer participation could be increased by allowing evening counts, we evaluated the potential for modifying the methodology. We evaluated the sampling efficiency of all 3-count combinations of morning and evening counts using data collected at 14 wetlands. Estimates of overall species richness decreased with increasing numbers of evening counts. However, this pattern did not hold when analyses were restricted to wetland-dependent species or those of conservation concern. Our findings suggest that it would be reasonable to permit evening counts, particularly if the data are to be used to monitor wetland dependent species and those of concern.

Wetlands↗

USGS1998

Estimating relative abundance from count data

Much of the available information on large-scale patterns of animal abundance is based on count surveys. The data provided by such surveys are often influenced by nuisance factors affecting the numbers of animals counted, but unrelated to population size. Temporal and spatial patterns in nuisance factors may exist, causing simple summaries of counts to give a misleading view of patterns in population size. We develop models for count data that allows the incorporation of such factors, and describe methods for estimating spatial patterns of relative abundance from counts. We carry out spatial analyses of North American Breeding Bird Survey data, in which observer ability is a nuisance parameter nested within sites. In light of evidence that new observers tend to count more birds than the observers they replace, we model observer ability as a random effect with mean depending on observer initiation year.

Austrian Journal of Statistics↗

USGS2002

Hierarchical modeling of population stability and species group attributes from survey data

Many ecological studies require analysis of collections of estimates. For example, population change is routinely estimated for many species from surveys such as the North American Breeding Bird Survey (BBS), and the species are grouped and used in comparative analyses. We developed a hierarchical model for estimation of group attributes from a collection of estimates of population trend. The model uses information from predefined groups of species to provide a context and to supplement data for individual species; summaries of group attributes are improved by statistical methods that simultaneously analyze collections of trend estimates. The model is Bayesian; trends are treated as random variables rather than fixed parameters. We use Markov Chain Monte Carlo (MCMC) methods to fit the model. Standard assessments of population stability cannot distinguish magnitude of trend and statistical significance of trend estimates, but the hierarchical model allows us to legitimately describe the probability that a trend is within given bounds. Thus we define population stability in terms of the probability that the magnitude of population change for a species is less than or equal to a predefined threshold. We applied the model to estimates of trend for 399 species from the BBS to estimate the proportion of species with increasing populations and to identify species with unstable populations. Analyses are presented for the collection of all species and for 12 species groups commonly used in BBS summaries. Overall, we estimated that 49% of species in the BBS have positive trends and 33 species have unstable populations. However, the proportion of species with increasing trends differs among habitat groups, with grassland birds having only 19% of species with positive trend estimates and wetland birds having 68% of species with positive trend estimates.

Ecology↗