Aim Ecological data collected by the general public are valuable for addressing a wide range of ecological research and conservation planning, and there has been a rapid increase in the scope and volume of data available. However, data from eBird or other large‐scale projects with volunteer observers typically present several challenges that can impede robust ecological inferences. These challenges include spatial bias, variation in effort and species reporting bias. Innovation We use the example of estimating species distributions with data from eBird, a community science or citizen science (CS) project. We estimate two widely used metrics of species distributions: encounter rate and occupancy probability. For each metric, we critically assess the impact of data processing steps that either degrade or refine the data used in the analyses. CS data density varies widely across the globe, so we also test whether differences in model performance are robust to sample size. Main conclusions Model performance improved when data processing and analytical methods addressed the challenges arising from CS data; however, the degree of improvement varied with species and data density. The largest gains we observed in model performance were achieved with 1) the use of complete checklists (where observers report all the species they detect and identify, allowing non‐detections to be inferred) and 2) the use of covariates describing variation in effort and detectability for each checklist. Occupancy models were more robust to a lack of complete checklists. Improvements in model performance with data refinement were more evident with larger sample sizes. In general, we found that the value of each refinement varied by situation and we encourage researchers to assess the benefits in other scenarios. These approaches will enable researchers to more effectively harness the vast ecological knowledge that exists within CS data for conservation and basic research.

Information on species’ distributions, abundances, and how they change over time is central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at landscape scales across range‐wide extents for two main reasons. First, landscape‐scale processes that affect populations vary throughout the year and across species’ ranges, requiring high‐resolution, year‐round data across broad, sometimes hemispheric, spatial extents. Second, while citizen science projects can collect data at these resolutions and extents, using these data requires appropriate analysis to address known sources of bias. Here, we present an analytical framework to address these challenges and generate year‐round, range‐wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long‐distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and abundance across a range of spatial scales throughout the annual cycle. Additionally, we generate intra‐annual estimates of the range, intra‐annual estimates of the associations between species and characteristics of the landscape, and interannual trends in abundance for breeding and non‐breeding seasons. The range‐wide population trajectories for Wood Thrush show a close correspondence between breeding and non‐breeding seasons with steep declines between 2010 and 2013 followed by shallower rates of decline from 2013 to 2016. The breeding season range‐wide population trajectory based on the independently collected and analyzed North American Breeding Bird Survey data also shows this pattern. The information provided here fills important knowledge gaps for Wood Thrush, especially during the less studied migration and non‐breeding periods. More generally, the modeling framework presented here can be used to accurately capture landscape scale intra‐ and interannual distributional dynamics for broadly distributed, highly mobile species.

Citizen science data are valuable for addressing a wide range of ecological research questions, and there has been a rapid increase in the scope and volume of data available. However, data from large-scale citizen science projects typically present a number of challenges that can inhibit robust ecological inferences. These challenges include: species bias, spatial bias, variation in effort, and variation in observer skill.To demonstrate key challenges in analysing citizen science data, we use the example of estimating species distributions with data from eBird, a large semi-structured citizen science project. We estimate three widely applied metrics for describing species distributions: encounter rate, occupancy probability, and relative abundance. For each method, we outline approaches for data processing and modelling that are suitable for using citizen science data for estimating species distributions.Model performance improved when data processing and analytical methods addressed the challenges arising from citizen science data. The largest gains in model performance were achieved with two key processes 1) the use of complete checklists rather than presence-only data, and 2) the use of covariates describing variation in effort and detectability for each checklist. Including these covariates accounted for heterogeneity in detectability and reporting, and resulted in substantial differences in predicted distributions. The data processing and analytical steps we outlined led to improved model performance across a range of sample sizes.When using citizen science data it is imperative to carefully consider the appropriate data processing and analytical procedures required to address the bias and variation. Here, we describe the consequences and utility of applying our suggested approach to semi-structured citizen science data to estimate species distributions. The methods we have outlined are also likely to improve other forms of inference and will enable researchers to conduct robust analyses and harness the vast ecological knowledge that exists within citizen science data.

Limited knowledge of the distribution, abundance, and habitat associations of migratory species hinders effective conservation actions. We use Neotropical migratory birds as a model group to compare approaches to prioritize land conservation needed to support ≥30% of the global abundances of 117 species. Specifically, we compare scenarios from spatial optimization models to achieve conservation targets by: 1) area requirements for conserving >30% abundance of each species for each week of the year independently vs. combined; 2) including vs. ignoring spatial clustering of species abundance; and 3) incorporating vs. avoiding human-dominated landscapes. Solutions integrating information across the year require 56% less area than those integrating weekly abundances, with additional reductions when shared-use landscapes are included. Although incorporating spatial population structure requires more area, geographical representation among priority sites improves substantially. These findings illustrate that globally-sourced citizen science data can elucidate key trade-offs among opportunity costs and spatiotemporal representation of conservation efforts.

A 60-cm sediment core, representing approximately 50 years of deposition, was collected from the major depositional basin of Onondaga Lake, a calcareous, hypereutrophic system in Syracuse, New York (USA). Sequential chemical extractions were performed to obtain fractional phosphorus (P) profiles, and these suggest that the sediment P available for solubilization and recycling to the water column is contained within the CaCO3-associated, Fe&Al-bound, and extractable biogenic-P fractions. Corrections were applied to account for the presence of refractory P within the CaCO3-associated and Fe&Al-bound fractions. Labile phosphorus comprises ~50% of the particulate P at the time of deposition. Reductions in labile P with depth suggest losses due to diagenesis with subsequent release to the lake water column. Rate constants for diagenesis were calculated from measurements of labile P on dated cores. Two labile components were apparent: one, exhibiting 'fast' decay (k = 4.8 year-1) was located within the upper 1 cm of sediment, and the other, characterized by 'slow' decay (k = 0.1 year-1) was located throughout the sediment to a depth of ~30 cm, below which sediment P was refractory. The ratio of the 'fast' to 'slow' fractions is approximately 1 : 1 in freshly deposited sediment. These findings facititate sediment P modelling and have bearing on the recovery of lakes following reductions in external phosphorus loads.

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Most spatial conservation planning for wide‐ranging or migratory species is constrained by poor knowledge of species’ spatiotemporal dynamics and is only based on static species’ ranges. However, species have substantial variation in abundance across their range and migratory species have important spatiotemporal population dynamics. With growing ecological data and advancing analytics, both of these can be estimated and incorporated into spatial conservation planning. However, there is limited information on the degree to which including this information affects conservation planning. We compared the performance of systematic conservation prioritizations for different scenarios based on varying the input species’ distributions by ecological metric (abundance distributions versus range maps) and temporal sampling resolution (weekly, monthly, or quarterly). We used the example of a community of 41 species of migratory shorebirds that breed in North America, and we used eBird data to produce weekly estimates of species’ abundances and ranges. Abundance distributions at a monthly or weekly resolution led to prioritizations that most efficiently protected species throughout the full annual cycle. Conversely, spatial prioritizations based on species’ ranges required more sites and left most species insufficiently protected for at least part of their annual cycle. Prioritizations with only quarterly species ranges were very inefficient as they needed to target 40% of species’ ranges to include 10% of populations. We highlight the high value of abundance information for spatial conservation planning, which leads to more efficient and effective spatial prioritization for conservation. Overall, we provide evidence that spatial conservation planning for wide‐ranging migratory species is most robust and efficient when informed by species’ abundance information from the full annual cycle.