Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management

Townsend, Philip A.; Clare, John; Liu, Nanfeng; Stenglein, Jennifer L.; Anhalt‐Depies, Christine; Deelen, Timothy R. Van; Gilbert, Neil A.; Singh, Aditya; Martin, Karl J.; Zuckerberg, Benjamin

doi:10.1002/eap.2436

Cited by 13 publications

(8 citation statements)

References 83 publications

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“…Our data are drawn from trail-camera images collected across the 2017 calendar year by Snapshot Wisconsin (Townsend et al, 2021) and supplementary efforts. We expand on data parameters and processing in Appendix S1.…”

Section: Methodsmentioning

confidence: 99%

A phenology of fear: Investigating scale and seasonality in predator–prey games between wolves and white‐tailed deer

Clare

Zuckerberg

Liu

et al. 2023

Ecology

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Predators and prey engage in games where each player must counter the moves of the other, and these games include multiple phases operating at different spatiotemporal scales. Recent work has highlighted potential issues related to scale‐sensitive inferences in predator–prey interactions, and there is growing appreciation that these may exhibit pronounced but predictable dynamics. Motivated by previous assertions about effects arising from foraging games between white‐tailed deer and canid predators (coyotes and wolves), we used a large and year‐round network of trail cameras to characterize deer and predator foraging games, with a particular focus on clarifying its temporal scale and seasonal variation. Linear features were strongly associated with predator detection rates, suggesting these play a central role in canid foraging tactics by expediting movement. Consistent with expectations for prey contending with highly mobile predators, deer responses were more sensitive to proximal risk metrics at finer spatiotemporal scales, suggesting that coarser but more commonly used scales of analysis may miss useful insights into prey risk‐response. Time allocation appears to be a key tactic for deer risk management and was more strongly moderated by factors associated with forage or evasion heterogeneity (forest cover, snow and plant phenology) than factors associated with the likelihood of predator encounter (linear features). Trade‐offs between food and safety appeared to vary as much seasonally as spatially, with snow and vegetation phenology giving rise to a “phenology of fear.” Deer appear free to counter predators during milder times of year, but a combination of poor foraging state, reduced forage availability, greater movements costs, and reproductive state dampen responsiveness during winter. Pronounced intra‐annual variation in predator–prey interactions may be common in seasonal environments.

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Section: Methodsmentioning

confidence: 99%

A phenology of fear: Investigating scale and seasonality in predator–prey games between wolves and white‐tailed deer

Clare

Zuckerberg

Liu

et al. 2023

Ecology

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“…These biases can be controlled for, by breaking down analyses by habitat category. It is also possible to integrate camera trap wildlife observations with satellite remote sensing data, such as habitat types or plant productivity that might help to explain the observations (Townsend et al., 2021).…”

Section: The Potential For Ecological Insightsmentioning

confidence: 99%

“…Partially due to this success, the effort in the Serengeti has been expanded into the Snapshot Safari network, encompassing camera‐trapping surveys across southern and eastern Africa (Pardo et al., 2021). Citizen scientists have also deployed camera trap surveys, such as under the American initiatives eMammal (https://www.eMammal.org/) (McShea et al., 2015), Candid Critters (Lasky et al., 2021) or Snapshot Wisconsin (Townsend et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Large‐scale mammal monitoring: The potential of a citizen science camera‐trapping project in the United Kingdom

Hsing

Hill

Smith

et al. 2022

Ecol Sol and Evidence

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1. In light of global biodiversity loss, there is an increasing need for large-scale wildlife monitoring. This is difficult for mammals, since they can be elusive and nocturnal. In the United Kingdom, there is a lack of systematic, widespread mammal monitoring, and a recognized deficiency of data. Innovative new approaches are required.2. We developed MammalWeb, a portal to enable UK-wide camera trapping by a network of citizen scientists and partner organizations. MammalWeb citizen scientists contribute to both the collection and classification of camera trap data.Following trials in 2013-2017, MammalWeb has grown organically to increase its geographic reach (e.g. ∼2000 sites in Britain). It has so far provided the equivalent of over 340 camera trap-years of wild mammal monitoring, and produced nearly 440,000 classified image sequences and videos, of which, over 180,000 are mammal detections.3. We describe MammalWeb, its background, its development and the novel approaches we have for participation. We consider the data collected by Mammal-Web participants, especially in light of their relevance to the main goals of wildlife monitoring: to provide spatial data, abundance data and temporal behavioural data. MammalWeb can complement existing approaches to mammal monitoring. Explicit accounting for spatial and temporal patterns in animal activity enables accountingThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Because bioblitzes often occur during a single outing (Lundmark, 2003), seasonality and stochastic processes, such as weather, can affect which taxa are observed (Wintle et al, 2005). In loosely structured sampling designs, citizen Example application and further reading Sanderson et al, 2021;Townsend et al, 2021;Downes et al, 2016;Brown & Williams, 2019Lundmark, 2003Postles & Bartlett, 2018 Current article Parker et al, 2018;Brown & Williams, 2019 scientists are likely to survey convenient locations and taxa that interest them (e.g., birds and flowering plants) (Karns et al, 2006;Robinson et al, 2018;Johnston et al, 2023). Spatial bias can lead to inaccurate conclusions when the sampled population does not represent the target population (Zhang & Zhu, 2018), and hyperdiverse or cryptic taxa (e.g., invertebrates) may be underrepresented in loosely structured bioblitzes because identification may require expertise (Brown & Williams, 2019).…”

Section: Introductionmentioning

confidence: 99%

Rapid, recurring, structured survey versus bioblitz for generating biodiversity data and analysis with a multispecies abundance model

Gigliotti

Franzem

Ferguson

2023

Conservation Biology

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A bioblitz inexpensively and quickly generates biodiversity data, but bioblitzes are often conducted with haphazard, unreplicated sampling. Results tend to be taxonomically, geographically, or temporally biased, lack metadata, and consist of lists of observed taxa that do not enable further analyses or correction for imperfect detection. A rapid, recurring, structured survey (RRSS) uses a structured sampling design and temporal and spatial replication to survey randomly selected sites on a conservation property. We participated in a loosely structured bioblitz and a subsequent RRSS at Big Canoe Creek Nature Preserve in Springville (St. Clair County), Alabama (USA) to compare observed richness derived from the 2 survey approaches. The RRSS data structure enabled us to fit models that accounted for imperfect detection to estimate abundances, occupancy probabilities, and habitat associations. The loosely structured bioblitz data could not be used in such models. We present a new integrated multispecies abundance model that we applied to avian RRSS data. Our model extension enables estimation for the community, employs data augmentation to estimate the number of undetected species, and incorporates covariates. The RRSS generated a more comprehensive and less biased list of observed taxonomic richness than the loosely structured bioblitz (e.g., 73 vs. 45 bird species and 104 vs. 63 insect families from the RRSS vs. loosely structured bioblitz, respectively). Models fit to the RRSS data identified seasonal patterns in avian community composition and allowed for estimation of habitat-occupancy relationships for insect taxa. The RRSS protocol has potential for broad transferability as a standardized, quick, and inexpensive way to inventory biodiversity and estimate ecological parameters while providing an outreach opportunity.

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Snapshot Wisconsin: networking community scientists and remote sensing to improve ecological monitoring and management

Cited by 13 publications

References 83 publications

A phenology of fear: Investigating scale and seasonality in predator–prey games between wolves and white‐tailed deer

A phenology of fear: Investigating scale and seasonality in predator–prey games between wolves and white‐tailed deer

Large‐scale mammal monitoring: The potential of a citizen science camera‐trapping project in the United Kingdom

Rapid, recurring, structured survey versus bioblitz for generating biodiversity data and analysis with a multispecies abundance model

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