The method of joinpoint regression has been used in numerous domains to assess changes in time series data, including such things as cancer mortality rates, motor vehicle collision mortalities, and disease risk. To help improve estimation of population parameters for use in ecological risk assessment and management, we present a simulation and analysis to describe the utility of this method for the ecological domain. We demonstrate how joinpoint regression can accurately identify if the population structure changes based on time series of abundance, as well as identify when this change occurs. In addition, we compare and contrast population parameter estimates derived through joinpoint and surplus production methods to those derived from standard surplus production methods alone. When considering a change point at 32 years (out of a 64 year simulation), the joinpoint regression model was able, on average, to estimate a joinpoint time of 32.31 years with a variance of 6.82 and 95% confidence interval for the mean relative bias of (0.0085, 0.0112). The model was able to consistently estimate population parameters, with variance of these estimations decreasing as the change in these population parameters increased. We conclude that joinpoint regression be added to the list of methods employed by those who assess ecological risk to allow for a more accurate and complete understanding of population dynamics.

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.

The status and trend estimates derived from the North American Breeding Bird Survey (BBS), are critical sources of information for bird conservation. However, many of the varied uses of these estimates are poorly supported by the current standard model. For example, inferences about population recovery require modeling approaches that are more sensitive to changes in the rates of population change through time and population cycles. In addition, regional status assessments would benefit from models that allow for the sharing of information across the species’ range. Here we describe Bayesian hierarchical generalized additive models (GAM) that fit these criteria, generating status and trend estimates optimized for many common uses related to conservation assessments. We demonstrate the models and their benefits using data for Barn Swallow (Hirundo rustica), Wood Thrush (Hylocichla mustelina), Carolina Wren (Thryothorus ludovicianus), and a selection of other species, and we run a full cross-validation of the GAMs against two other BBS models to compare predictive fit. The GAMs have better predictive fit than the standard model for all species studied here, and better or comparable predictive fit compared 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 also allows trend estimates based only on the smooth component, which are more stable between years and are therefore more useful for trend-based status assessments, such as those by the IUCN. It also allows for the easy customization of a BBS model to incorporate covariates that influence the smooth component separately from those that influence annual fluctuations (e.g., climate cycles vs annual precipitation). This GAMYE model is a broadly useful model for the BBS and other long-term surveys, because of its flexibility, its decomposition, and the hierarchical structure that shares information among regions.LAY SUMMARYWe describe an improved way to estimate population status and trends from the North American Breeding Bird Survey data, using a Bayesian hierarchical generalized additive mixed-model.The model generates estimates with better predictive accuracy than the previous model.Status and trend estimates from the model are more broadly useful for a wide range of common conservation applications

Finding ways of efficiently monitoring threatened species can be critical to effective conservation. The global proliferation of community science (also called citizen science) programs, like iNaturalist, presents a potential alternative or complement to conventional threatened species monitoring. Using a case study of ~700,000 observations of >10,000 IUCN Red List Threatened species within iNaturalist observations, we illustrate the potential risks and rewards of using community science to monitor threatened species. Poor data quality and risks of sending untrained volunteers to sample species that are sensitive to disturbance or harvesting are key barriers to overcome. Yet community science can expand the breadth of monitoring at little extra cost, while indirectly benefiting conservation through outreach and education. We conclude with a list of actionable recommendations to further mitigate the risks and capitalize on the rewards of community science as a threatened species monitoring tool.

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.

Researchers in ecology and evolutionary biology are increasingly dependent on computational code to conduct research, and the use of efficient methods to share, reproduce, and collaborate on code as well as any research-related documentation has become fundamental. GitHub is an online, cloud-based service that can help researchers track, organize, discuss, share, and collaborate on software and other materials related to research production, including data, code for analyses, and protocols.Despite these benefits, the use of GitHub by EEB researchers is not widespread due to the lack of domain-specific information and guidelines. To help EEB researchers adopt useful features from GitHub in their own workflows, we review twelve practical ways to use the platform. We outline features ranging from low to high technical difficulty: storing code, managing projects, coding collaboratively, conducting peer review, and writing a manuscript. Given that members of a research team may have different technical skills and responsibilities, we describe how the optimal use of GitHub features may vary among members of a research collaboration. As more ecologists and evolutionary biologists establish their workflows using GitHub, the field can continue to push the boundaries of collaborative, transparent, and open research.

Public health and safety concerns around the SARS-CoV-2 novel coronavirus and the COVID-19 pandemic have greatly changed human behaviour. Such shifts in behaviours including travel patterns, consumerism, and energy use, are variously impacting biodiversity during the human-dominated geological epoch known as the Anthropocene. Indeed, the dramatic reduction in human mobility and activity has been termed the "Anthropause". COVID-19 has highlighted the current environmental and biodiversity crisis and has provided an opportunity to redefine our relationship with nature. Here we share 10 considerations for conservation policy makers to support and rethink the development of impactful and effective policies in light of the COVID-19 pandemic. There are opportunities to leverage societal changes as a result of COVID-19, focus on the need for collaboration and engagement, and address lessons learned through the development of policies (including those related to public health) during the pandemic. The pandemic has had devastating impacts on humanity that should not be understated, but it is also a warning that we need to redefine our relationship with nature and restore biodiversity. The considerations presented here will support the development of robust, evidence-based, and transformative policies for biodiversity conservation in a post-COVID-19 world.

Bird monitoring in North America over several decades has generated many open databases, housing millions of structured and semi‐structured bird observations. These provide the opportunity to estimate bird densities and population sizes, once variation in factors such as underlying field methods, timing, land cover, proximity to roads, and uneven spatial coverage are accounted for. To facilitate integration across databases, we introduce NA‐POPS: Point Count Offsets for Population Sizes of North American Landbirds. NA‐POPS is a large‐scale, multi‐agency project providing an open‐source database of detectability functions for all North American landbirds. These detectability functions allow the integration of data from across disparate survey methods using the QPAD approach, which considers the probability of detection (q) and availability (p) of birds in relation to area (a) and density (d). To date, NA‐POPS has compiled over 7.1 million data points spanning 292 projects from across North America, and produced detectability functions for 338 landbird species. Here, we describe the methods used to curate these data and generate these detectability functions, as well as the open‐access nature of the resulting database.