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2. Comparing abundance estimates of a cryptic carnivore in southern Patagonia using two experimental methods.

Author

Elbroch, L. M., Williams, S. H., Ohrens, O., Pilgrim, K., Moeller, A., Arroyo‐Arce, S., Parker, M., Goic, D., Robinson, H., and Schwartz, M. K.

Subjects
*WILDLIFE conservation, *PUMAS, *BIOSPHERE reserves, *CONFLICT management, *PRESERVATION of architecture, *CARNIVOROUS animals
Abstract

Determining the abundance of cryptic carnivores is central to building successful conservation management to mitigate conflicts and support coexistence strategies. For these reasons, there is considerable investment in developing reliable, cost‐effective tools for estimating the abundance of wildlife. Nevertheless, field‐based comparisons of abundance methods remain uncommon, even while essential to refining methods and coming to consensus around best practices. Here, we compare two approaches still being tested in real‐world application for an emblematic puma (Puma concolor) population in the Torres del Paine UNESCO Biosphere Reserve in southern Chile: (1) the unmarked estimator, space‐to‐event model (STE), which utilizes photographs gathered with camera traps, and (2) the genotype spatial partial identity model (gSPIM), which is an adaptation of the more established spatially explicit genetic capture‐recapture method (SECR) based on genetic data extracted from scats collected in systematic surveys. We show the tremendous variation in resulting STE estimates depending upon the start time of the analysis and length of the sampling window, and showcase a refined iterative sampling approach in a Bayesian framework to both utilize the full camera data and to stabilize density estimates for a given sampling window. Across all sampling, estimates from the STE model ranged from 3.19 (1.6–5.1 representing 10th and 90th percentile of credible intervals) to 7.38 (3.3–11.6) independent pumas 100 km−2. By comparison, our gSPIM model estimated 5.1 independent pumas 100 km−2 (excluding kittens) (with credible intervals of 2.2–10.3). Neither method was compared with any known density to determine their accuracy. Nevertheless, we provide initial density estimates to guide conservation strategies for wildlife agencies and local communities overseeing and hosting nascent puma tourism and livestock ranching, as well as guidelines for the use of these methods for any wildlife species. [ABSTRACT FROM AUTHOR]

Published
2024
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4. <scp>SNAPSHOT USA</scp> 2020: A second coordinated national camera trap survey of the United States during the <scp>COVID</scp> ‐19 pandemic

Author

Kays R., Cove M. V., Diaz J., Todd K., Bresnan C., Snider M., Lee T. E., Jasper J. G., Douglas B., Crupi A. P., Weiss K. C. B., Rowe H., Sprague T., Schipper J., Lepczyk C. A., Fantle-Lepczyk J. E., Davenport J., Zimova M., Farris Z., Williamson J., Fisher-Reid M. C., Rezendes D., King S. M., Chrysafis P., Jensen A. J., Jachowski D. S., King K. C., Herrera D. J., Moore S., van der Merwe M., Lombardi J. V., Sergeyev M., Tewes M. E., Horan R. V., Rentz M. S., Driver A., Brandt L. R. S. E., Nagy C., Alexander P., Maher S. P., Darracq A. K., Barr E. G., Hess G., Webb S. L., Proctor M. D., Vanek J. P., Lafferty D. J. R., Hubbard T., Jimenez J. E., McCain C., Favreau J., Fogarty J., Hill J., Hammerich S., Gray M., Rega-Brodsky C. C., Durbin C., Flaherty E. A., Brooke J., Coster S. S., Lathrop R. G., Russell K., Bogan D. A., Shamon H., Rooney B., Rockhill A., Lonsinger R. C., O'Mara M. T., Compton J. A., Barthelmess E. L., Andy K. E., Belant J. L., Petroelje T., Wehr N. H., Beyer D. E., Scognamillo D. G., Schalk C., Day K., Ellison C. N., Ruthven C., Nunley B., Fritts S., Whittier C. A., Neiswenter S. A., Pelletier R., DeGregorio B. A., Kuprewicz E. K., Davis M. L., Baruzzi C., Lashley M. A., McDonald B., Mason D., Risch D. R., Allen M. L., Whipple L. S., Sperry J. H., Alexander E., Wolff P. J., Hagen R. H., Mortelliti A., Bolinjcar A., Wilson A. M., Van Norman S., Powell C., Coletto H., Schauss M., Bontrager H., Beasley J., Ellis-Felege S. N., Wehr S. R., Giery S. T., Pekins C. E., LaRose S. H., Revord R. S., Hansen C. P., Hansen L., Millspaugh J. J., Zorn A., Gerber B. D., Rezendes K., Adley J., Sevin J., Green A. M., Sekercioglu C. H., Pendergast M. E., Mullen K., Bird T., Edelman A. J., Romero A., O'Neill B. J., Schmitz N., Vandermus R. A., Alston J. M., Kuhn K. M., Hasstedt S. C., Lesmeister D. B., Appel C. L., Rota C., Stenglein J. L., Anhalt-Depies C., Nelson C. L., Long R. A., Remine K. R., Jordan M. J., Elbroch L. M., Bergman D., Cendejas-Zarelli S., Sager-Fradkin K., Conner M., Morris G., Parsons E., Hernandez-Yanez H., McShea W. J., Kays, R., Cove, M. V., Diaz, J., Todd, K., Bresnan, C., Snider, M., Lee, T. E., Jasper, J. G., Douglas, B., Crupi, A. P., Weiss, K. C. B., Rowe, H., Sprague, T., Schipper, J., Lepczyk, C. A., Fantle-Lepczyk, J. E., Davenport, J., Zimova, M., Farris, Z., Williamson, J., Fisher-Reid, M. C., Rezendes, D., King, S. M., Chrysafis, P., Jensen, A. J., Jachowski, D. S., King, K. C., Herrera, D. J., Moore, S., van der Merwe, M., Lombardi, J. V., Sergeyev, M., Tewes, M. E., Horan, R. V., Rentz, M. S., Driver, A., Brandt, L. R. S. E., Nagy, C., Alexander, P., Maher, S. P., Darracq, A. K., Barr, E. G., Hess, G., Webb, S. L., Proctor, M. D., Vanek, J. P., Lafferty, D. J. R., Hubbard, T., Jimenez, J. E., Mccain, C., Favreau, J., Fogarty, J., Hill, J., Hammerich, S., Gray, M., Rega-Brodsky, C. C., Durbin, C., Flaherty, E. A., Brooke, J., Coster, S. S., Lathrop, R. G., Russell, K., Bogan, D. A., Shamon, H., Rooney, B., Rockhill, A., Lonsinger, R. C., O'Mara, M. T., Compton, J. A., Barthelmess, E. L., Andy, K. E., Belant, J. L., Petroelje, T., Wehr, N. H., Beyer, D. E., Scognamillo, D. G., Schalk, C., Day, K., Ellison, C. N., Ruthven, C., Nunley, B., Fritts, S., Whittier, C. A., Neiswenter, S. A., Pelletier, R., Degregorio, B. A., Kuprewicz, E. K., Davis, M. L., Baruzzi, C., Lashley, M. A., Mcdonald, B., Mason, D., Risch, D. R., Allen, M. L., Whipple, L. S., Sperry, J. H., Alexander, E., Wolff, P. J., Hagen, R. H., Mortelliti, A., Bolinjcar, A., Wilson, A. M., Van Norman, S., Powell, C., Coletto, H., Schauss, M., Bontrager, H., Beasley, J., Ellis-Felege, S. N., Wehr, S. R., Giery, S. T., Pekins, C. E., Larose, S. H., Revord, R. S., Hansen, C. P., Hansen, L., Millspaugh, J. J., Zorn, A., Gerber, B. D., Rezendes, K., Adley, J., Sevin, J., Green, A. M., Sekercioglu, C. H., Pendergast, M. E., Mullen, K., Bird, T., Edelman, A. J., Romero, A., O'Neill, B. J., Schmitz, N., Vandermus, R. A., Alston, J. M., Kuhn, K. M., Hasstedt, S. C., Lesmeister, D. B., Appel, C. L., Rota, C., Stenglein, J. L., Anhalt-Depies, C., Nelson, C. L., Long, R. A., Remine, K. R., Jordan, M. J., Elbroch, L. M., Bergman, D., Cendejas-Zarelli, S., Sager-Fradkin, K., Conner, M., Morris, G., Parsons, E., Hernandez-Yanez, H., and Mcshea, W. J.

Subjects
United State, Carnivora, Wild, mammal, Animals, Wild, Didelphimorphia, species distribution modeling, Birds, Bird, camera traps, biodiversity, biogeography, Cetartiodactyla, Lagomorpha, mammals, occupancy modeling, Animals, Humans, Mammals, Pandemics, United States, COVID-19, Ecology, Evolution, Behavior and Systematics, Pandemic, camera trap, Animal, Human
Abstract

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

Published
2022
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6. Tracking science : An alternative for those excluded by citizen science

Author

Liebenberg, L., Ao, A., Lombard, M., Shermer, M., Xhukwe, U., Biesele, M., Xao, D., Carruthers, P., Kxao, O., Hansson, Sven Ove, Langwane, H., Elbroch, L. M., Ui, N., Keeping, D., Humphrey, G., Newman, G., Gaq'o, U., Steventon, J., Kashe, N., Stevenson, R., Benadie, K., du Plessis, P., Minye, J., Kxunta, U., Ludwig, B., Daqm, O., Louw, M., Debe, D., Voysey, M., Liebenberg, L., Ao, A., Lombard, M., Shermer, M., Xhukwe, U., Biesele, M., Xao, D., Carruthers, P., Kxao, O., Hansson, Sven Ove, Langwane, H., Elbroch, L. M., Ui, N., Keeping, D., Humphrey, G., Newman, G., Gaq'o, U., Steventon, J., Kashe, N., Stevenson, R., Benadie, K., du Plessis, P., Minye, J., Kxunta, U., Ludwig, B., Daqm, O., Louw, M., Debe, D., and Voysey, M.

Abstract

In response to recent discussion about terminology, we propose "tracking science" as a term that is more inclusive than citizen science. Our suggestion is set against a post-colonial political background and large-scale migrations, in which "citizen" is becoming an increasingly contentious term. As a diverse group of authors from several continents, our priority is to deliberate a term that is all-inclusive, so that it could be adopted by everyone who participates in science or contributes to scientific knowledge, regardless of socio-cultural background. For example, current citizen science terms used for Indigenous knowledge imply that such practitioners belong to a sub-group that is other, and therefore marginalized. Our definition for "tracking science" does not exclude Indigenous peoples and their knowledge contributions and may provide a space for those who currently participate in citizen science, but want to contribute, explore, and/or operate beyond its confinements. Our suggestion is not that of an immediate or complete replacement of terminology, but that the notion of tracking science can be used to complement the practice and discussion of citizen science where it is contextually appropriate or needed. This may provide a breathing space, not only to explore alternative terms, but also to engage in robust, inclusive discussion on what it means to do science or create scientific knowledge. In our view, tracking science serves as a metaphor that applies broadly to the scientific community-from modern theoretical physics to ancient Indigenous knowledge., QC 20220216

Published
2021
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