Combination of targeted monitoring and Breeding Bird Survey data improves population trend estimation and species distribution modeling for the Common Nighthawk

Standardized monitoring is critical for conservation because reliable survey data are used to inform the necessity, type, and effectiveness of conservation actions. Many of the avian monitoring data used for conservation are collected by “comprehensive” programs that survey for all species observed; however, there are some species that have low availability for detection during comprehensive surveys and are instead monitored with targeted programs. Unfortunately, those targeted programs are rarely evaluated relative to existing programs and management objectives to inform allocation of effort. We assessed the statistical performance of the comprehensive North American Breeding Bird Survey (BBS), the targeted Canadian Nightjar Survey (CNS), and the two programs combined for the Common Nighthawk (Chordeiles minor). First, we used parameters from the existing datasets to simulate population declines and determined the probability of detecting those declines. Analyses that combined both datasets resulted in higher probability of detecting a 30% population decline (BBS: 38%, CNS: 64%, combined: 69%). Next, we built probability of occurrence models and assessed the predictive performance of those models. Combined analyses had similar predictive performance to the CNS and moderated poor performance of the BBS in the north (mean Cohen’s kappa; BBS: 0.40, CNS: 0.46, combined: 0.50). Our results suggest that data from targeted monitoring is important for evaluation of Common Nighthawk population trend and habitat relationships but is best combined with BBS data. Comprehensive and targeted monitoring programs may be better when considered together, and exploration of data combination should become the rule, not the exception. We suggest that the framework we present can be used as a starting point for evaluating targeted monitoring programs relative to defined objectives and existing programs, with the potential to explore hypothetical management scenarios.

Estimation Of Annual Indexes of Abundance and Trend From The American Woodcock Singing-Ground Survey: Comparison Of 4 Models

Status and trends of American Woodcock Scolopax minor populations in the eastern and central US and Canada are monitored via a Singing-ground Survey , conducted just after sunset along roadsides in spring. Annual analyses of the survey produce estimates of trend and annual indexes of abundance for 25 states and provinces, eastern and central management regions, and survey-wide. In recent years, a log-linear hierarchical model that defines year effects as random effects in the context of a slope parameter (the S Model) has been used to model population change. Recently, alternative models have been proposed for analysis of Singing-ground Survey data. Analysis of a similar roadside survey, the North American Breeding Bird Survey , has indicated that alternative models are preferable for almost all species analyzed in the Breeding Bird Survey. Here, we use leave-one-out cross validation to compare model fit for the present Singing-ground Survey model to fits of three alternative models, including a model that describes population change as the difference in expected counts between successive years (the D model) and two models that include t -distributed extra-Poisson overdispersion effects (H models) as opposed to normally-distributed extra-Poisson overdispersion. Leave-one-out cross validation results indicate that the D model was favored by the Bayesian predictive information criterion but a pairwise t -test indicated that model D was not significantly better-fitting to Singing-ground Survey data than the S model. The H models are not preferable to the alternatives with normally-distributed overdispersion. All models provided generally similar estimates of trend and annual indexes suggesting that, within this model set, choice of model will not lead to alternative conclusions regarding population change. However, as in Breeding Bird Survey analyses, we note a tendency for S model results to provide slightly more extreme estimates of trend relative to D models. We recommend use of the D model for future Singing-ground Survey analyses.

North American Breeding Bird Survey status and trend estimates to inform a wide range of conservation needs, using a flexible Bayesian hierarchical generalized additive model

The status and trend estimates derived from the North American Breeding Bird Survey (BBS) are critical sources of information for bird conservation. However, the estimates are partly dependent on the statistical model used. Therefore, multiple models are useful because not all of the varied uses of these estimates (e.g., inferences about long-term change, annual fluctuations, population cycles, and recovery of once-declining populations) are supported equally well by a single statistical model. Here we describe Bayesian hierarchical generalized additive models (GAMs) for the BBS, which share information on the pattern of population change across a species’ range. We demonstrate the models and their benefits using data from a selection of species, and we run full cross-validation of the GAMs against 2 other models to compare the predictive fit. The GAMs have a better predictive fit than the standard model for all species studied here and comparable predictive fit to an alternative first difference model. In addition, one version of the GAM described here (GAMYE) estimates a population trajectory that can be decomposed into a smooth component and the annual fluctuations around that smooth component. This decomposition allows trend estimates based only on the smooth component, which are more stable between years and are therefore particularly useful for trend-based status assessments, such as those by the International Union for the Conservation of Nature. It also allows for the easy customization of the model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs. annual precipitation). For these reasons and more, this GAMYE model is a particularly useful model for the BBS-based status and trend estimates.

bbsBayes: An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data

The North American Breeding Bird Survey (BBS) is the primary ecological monitoring program used to assess the population, status, and trend of North American birds. As such, accessible analysis of BBS data is crucial to wildlife conservation/management and ecological science in North America. The R package bbsBayes was developed as a wrapper for the analysis of BBS data using hierarchical Bayesian models, including the models currently used by the Canadian Wildlife Service and the United States Geological Survey. The goal of bbsBayes is to provide an accessible package for anyone in the conservation community to estimate population trajectories (time-series) and trends (rates of change) for any of the 400+ bird species monitored by the BBS, and to allow more advance users to easily access the data and model-templates necessary to customize an analysis for their research.

North American Breeding Bird Survey status and trend estimates to inform a wide-range of conservation needs, using a flexible Bayesian hierarchical generalized additive model

ABSTRACTThe status and trend estimates derived from the North American Breeding Bird Survey (BBS), are critical sources of information for bird conservation. However, the estimates are partly dependent on the statistical model used. Therefore, multiple models are useful because not all of the varied uses of these estimates (e.g. inferences about long-term change, annual fluctuations, population cycles, recovery of once declining populations) are supported equally well by a single statistical model. Here we describe Bayesian hierarchical generalized additive models (GAM) for the BBS, which share information on the pattern of population change across a species’ range. We demonstrate the models and their benefits using data a selection of species; and we run a full cross-validation of the GAMs against two other models to compare predictive fit. The GAMs have better predictive fit than the standard model for all species studied here, and comparable predictive fit to an alternative first difference model. In addition, one version of the GAM described here (GAMYE) estimates a population trajectory that can be decomposed into a smooth component and the annual fluctuations around that smooth. This decomposition allows trend estimates based only on the smooth component, which are more stable between years and are therefore particularly useful for trend-based status assessments, such as those by the IUCN. It also allows for the easy customization of the model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs annual precipitation). For these reasons and more, this GAMYE model is a particularly useful model for the BBS-based status and trend estimates.LAY SUMMARYThe status and trend estimates derived from the North American Breeding Bird Survey are critical sources of information for bird conservation, but they are partly dependent on the statistical model used.We describe a model to estimate population status and trends from the North American Breeding Bird Survey data, using a Bayesian hierarchical generalized additive mixed-model that allows for flexible population trajectories and shares information on population change across a species’ range.The model generates estimates that are broadly useful for a wide range of common conservation applications, such as IUCN status assessments based on trends or changes in the rates of decline for species of concern; and the estimates have better or similar predictive accuracy to other models., and