Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds

Schindler, Alexander R.; Cunningham, Stephanie A.; Schafer, Toryn L. J.; Sinnott, Emily; Clements, Sarah J.; DiDonato, Frances M.; Mosloff, Alisha R.; Walters, Clay M.; Shipley, Amy A.; Weegman, Mitch D.; Zhao, Qing

doi:10.1038/s41598-022-23603-0

Cited by 4 publications

(6 citation statements)

References 61 publications

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“…eBird's data can be used to guide the planning of formal surveys, such as in the initial stage of the two-stage study design described by Pacifici et al (2012). Volunteer-collected data such as eBird's can also be combined with formally collected data from other sources in occupancy models (von Hirschheydt et al 2023), although idiosyncratic differences between data sources may need to be accounted for during modeling (Schindler et al 2022, Emmet et al 2023).…”

Section: Discussionmentioning

confidence: 99%

Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data

2023

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An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order estimate the actual probability of occupancy (i.e., proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. Because the majority of eBird’s data are not collected in the form of repeated observations at individual locations, we explore two ways in which the single-visit records could be used in occupancy models. First, we assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. On average, eBird’s observers did not make their observations at locations that were representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Thus, the use of space-for-time substitution is not always appropriate. Second, we explored the utility of including data from single-visit records to supplement sets of repeated-visit data. In a simulation study we found that inclusion of single-visit records increased the precision of occupancy estimates, but only when detection probabilities are high. When detection probability was low, the addition of single-visit records exacerbated biases in estimates of occupancy probability. We conclude that subsets of data from eBird, and likely from similar projects, can be used for occupancy modeling either using space-for-time substation or supplementing repeated-visit data with data from single-visit records. The appropriateness of either alternative will depend on the goals of a study and on the probabilities of detection and occupancy of the species of interest.

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Section: Discussionmentioning

confidence: 99%

Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data

2023

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“…Our modelling approach addresses a major challenge in statistical ecology that using participatory science data in integrated models may bias parameter estimates (Di Febbraro et al, 2023;Hochachka et al, 2012;Johnston et al, 2023;Pacifici et al, 2017). We addressed this challenge by using a parameter in the integrated model that scale the estimates of participatory science data to the same level of the estimates of rigorous survey data (Schindler et al, 2022;Stillman al., 2023). In this way, we successfully used participatory science data in a dynamic N-mixture model to inform population growth, while previous studies that integrated participatory science data were mostly constrained to static (Conn et al, 2022;Dambly et al, 2023;Farr et al, 2021;Gelfand & Shirota, 2019;Koshkina et al, 2017;Robinson et al, 2020) or trend models (Schindler et al, 2022;Stillman et al, 2023).…”

Section: Model Characteristicsmentioning

confidence: 99%

“…Integrated distribution models (IDMs) provide a way to leverage the strengths while overcoming the weaknesses of multiple data sets from unmarked populations (count, detection/non-detection and presence-only; Dorazio, 2014, Pacifici et al, 2017, Fletcher et al, 2019, Miller et al, 2019, Isaac et al, 2020, sometimes involving participatory science data (Di Febbraro et al, 2023;Farr et al, 2021;Pagel et al, 2014;Robinson et al, 2020;Schindler et al, 2022;Stillman et al, 2023). Given the relative ease with which participatory science data can be obtained across broad spatiotemporal extents, IDMs can thus provide new opportunities for understanding relationships between ecological patterns and global change drivers (Di Febbraro et al, 2023;Doser et al, 2021;Grüss et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…One potential solution is to introduce a parameter that scales the estimates based on participatory science data to the same level as the estimates based on rigorous survey data (Schindler et al, 2022;Stillman et al, 2023). This scaling parameter, however, has only been used in models that inform population trends (Schindler et al, 2022, Stillman et al, 2023, but not in models that inform population processes.…”

Section: Introductionmentioning

confidence: 99%

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Integrating counts from rigorous surveys and participatory science to better understand spatiotemporal variation in population processes

Zhao,

Latif,

Nuse

et al. 2024

Methods Ecol Evol

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Knowledge of variation in population processes (e.g. population growth) across broad spatiotemporal scales is fundamental to population ecology and critical for conservation decision‐making. Count data from rigorous surveys (e.g. surveys with probabilistic sampling design and distance sampling information) can inform population processes but are often limited in space and time. Participatory science data cover broader spatiotemporal extents but are prone to bias due to limited to no sampling design and lack of distance sampling information, hindering their capability of informing population processes. Here, we developed an integrated dynamic N‐mixture model that jointly analyses rigorous survey and participatory science data to inform population growth at broad spatiotemporal extents. The model contains a flexible scaling parameter that allows fixed and random effects to account for biases and errors in participatory science data. We conducted simulations to evaluate the inference performance of this model across a broad range of spatial and temporal overlap between rigorous survey and participatory science data. We also conducted a case study of Baird's Sparrow (Centronyx bairdii), a species of conservation concern, to illustrate the application of the integrated model with rigorous survey data from the Integrated Monitoring in Bird Conservation Regions programme and participatory science North American Breeding Bird Survey and eBird data. Simulations showed that the integrated model improved precision without biasing parameter estimates, in comparison with a model informed by rigorous survey data alone. The case study further demonstrated the utility of the integrated model for quantifying range‐wide, long‐term population processes and environmental drivers despite limited spatiotemporal extent of rigorous survey data. In particular, we found that population growth rate peaked under medium temperature, which were only apparent in the integrated model. The integrated model developed in this study is useful for understanding wildlife population processes at broad spatiotemporal scales with count data. The flexible structure of this model, in particular the scaling parameter, makes it highly adaptable to a broad range of ecological systems and survey procedures. These properties make this modelling approach highly relevant for both population ecology and conservation practice.

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“…limited extent and quantity) or unstructured (i.e. limited information content) data to model dynamics of animal populations (Fletcher et al., 2019; Isaac et al., 2020; Miller et al., 2019; Schindler et al., 2022; Zipkin et al., 2021; Zipkin & Saunders, 2018). For migratory species, integrated modelling can resolve discrepancies in estimated population trends from separate periods of the annual cycle by linking seasonal dynamics (Saunders, Farr, et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Overcoming data gaps using integrated models to estimate migratory species' dynamics during cryptic periods of the annual cycle

Farr,

Zylstra,

Ries

et al. 2024

Methods Ecol Evol

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Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect. To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs. Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions. Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.

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Joint analysis of structured and semi-structured community science data improves precision of relative abundance but not trends in birds

Cited by 4 publications

References 61 publications

Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data

Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data

Integrating counts from rigorous surveys and participatory science to better understand spatiotemporal variation in population processes

Overcoming data gaps using integrated models to estimate migratory species' dynamics during cryptic periods of the annual cycle

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