Literature & Resources#
Subject |
Reference |
Download |
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Aerial Infrared Imaging |
Hennig, J. D., Schoenecker, K. A., Terwilliger, M. L. N., Holm, G. W., & Laake, J. L. (2021). Comparison of Aerial Thermal Infrared Imagery and Helicopter Surveys of Bison (Bison bison) in Grand Canyon National Park, USA. Sensors, 21(15), 5087. https://doi.org/10.3390/s21155087 |
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Bias and Error & Detection Probability |
Davis, K. L., Silverman, E. D., Sussman, A. L., Wilson, R. R., & Zipkin, E. F. (2022). Errors in aerial survey count data: Identifying pitfalls and solutions. Ecology and Evolution, 12(3), e8733. https://doi.org/10.1002/ece3.8733 |
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Bias and Error & Detection Probability |
Elphick, C. S. (2008). How you count counts: The importance of methods research in applied ecology. Journal of Applied Ecology, 45(5), 1313–1320. https://doi.org/10.1111/j.1365-2664.2008.01545.x |
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Bias and Error & Detection Probability |
Zabransky, C. J., Hewitt, D. G., Deyoung, R. W., Gray, S. S., Richardson, C., Litt, A. R., & Deyoung, C. A. (2016). A detection probability model for aerial surveys of mule deer: Mule Deer Detection Probability Model. The Journal of Wildlife Management, 80(8), 1379–1389. https://doi.org/10.1002/jwmg.21143 |
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Bias and Error & Detection Probability |
Lubow, B. C., & Ransom, J. I. (2016). Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations. PLOS ONE, 11(5), e0154902. https://doi.org/10.1371/journal.pone.0154902 |
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Bias and Error & Detection Probability |
Mason, R., Carpenter, L. H., Cox, M., Devos, J. C., Fairchild, J., Freddy, D. J., Heffelfinger, J. R., Kahn, R. H., Mccorquodale, S. M., Pac, D. F., Summers, D., White, G. C., & Williams, B. K. (2006). A Case for Standardized Ungulate Surveys and Data Management in the Western United States. Wildlife Society Bulletin, 34(4), 1238–1242. https://doi.org/10.2193/0091-7648(2006)34[1238:ACFSUS]2.0.CO;2{.uri} |
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Bias and Error & Detection Probability |
Delisle, Z. J., McGovern, P. G., Dillman, B. G., & Swihart, R. K. (2023). Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors. Remote Sensing in Ecology and Conservation, 9(2), 222–234. https://doi.org/10.1002/rse2.305 |
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Citizen Science |
Belt, J. J., & Krausman, P. R. (2012). Evaluating population estimates of mountain goats based on citizen science. Wildlife Society Bulletin, 36(2), 264–276. https://doi.org/10.1002/wsb.139 |
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Drones |
Coops, N. C., Goodbody, T. R. H., & Cao, L. (2019). Four steps to extend drone use in research. Nature, 572(7770), 433–435. https://doi.org/10.1038/d41586-019-02474-y |
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Drones |
Corcoran, E., Winsen, M., Sudholz, A., & Hamilton, G. (2021). Automated detection of wildlife using drones: Synthesis, opportunities and constraints. Methods in Ecology and Evolution, 12(6), 1103–1114. https://doi.org/10.1111/2041-210X.13581 |
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Drones |
Cowie, N., Swinfield, T., Humpidge, R., Williams, J., Bridge, D., Casey, C., Asque, A., & Morris, D. (2020). Drones for GIS – Best Practice. Royal Society for the Protection of Birds Conservation Data Management Unit. https://wildlabs.net/sites/default/files/community/files/drones_for_gis_-_best_practice_v2.0.pdf |
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Drones |
López, J., & Mulero-Pázmány, M. (2019). Drones for Conservation in Protected Areas: Present and Future. Drones, 3(1), 10. https://doi.org/10.3390/drones3010010 |
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Drones |
McMahon, M. C., Ditmer, M. A., Isaac, E. J., Moore, S. A., & Forester, J. D. (2021). Evaluating Unmanned Aerial Systems for the Detection and Monitoring of Moose in Northeastern Minnesota. Wildlife Society Bulletin, 45(2), 312–324. https://doi.org/10.1002/wsb.1167 |
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Drones |
Mohsan, S. A. H., Khan, M. A., Noor, F., Ullah, I., & Alsharif, M. H. (2022). Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review. Drones, 6(6), 147. https://doi.org/10.3390/drones6060147 |
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Harvest Indices |
Priadka, P., Brown, G. S., Patterson, B. R., & Mallory, F. F. (2020). Sex and age‐specific differences in the performance of harvest indices as proxies of population abundance under selective harvesting. Wildlife Biology, 2020(3), 1–11. https://doi.org/10.2981/wlb.00629 |
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DNA |
Van Leeuwen, P., & Michaux, J. (2023). Using eDNA for mammal inventories still needs naturalist expertise, a meta‐analysis. Ecology and Evolution, 13(12), e10788. https://doi.org/10.1002/ece3.10788 |
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Integrated Population Models |
Furnas, B. J., Landers, R. H., Hill, S., Itoga, S. S., & Sacks, B. N. (2018). Integrated modeling to estimate population size and composition of mule deer. The Journal of Wildlife Management, 82(7), 1429–1441. https://doi.org/10.1002/jwmg.21507 |
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Integrated Population Models |
Moeller, A. K., Nowak, J. J., Neufeld, L., Bradley, M., Manseau, M., Wilson, P., McFarlane, S., Lukacs, P. M., & Hebblewhite, M. (2021). Integrating counts, telemetry, and non‐invasive DNA data to improve demographic monitoring of an endangered species. Ecosphere, 12(5), e03443. https://doi.org/10.1002/ecs2.3443 |
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Monitoring Frequency |
Conroy, M. J., Harris, G., Stewart, D. R., & Butler, M. J. (2018). Evaluation of desert bighorn sheep abundance surveys, southwestern Arizona, USA. The Journal of Wildlife Management, 82(6), 1149–1160. https://doi.org/10.1002/jwmg.21463 |
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Monitoring Frequency |
McDonald-Madden, E., Baxter, P. W. J., Fuller, R. A., Martin, T. G., Game, E. T., Montambault, J., & Possingham, H. P. (2010). Monitoring does not always count. Trends in Ecology & Evolution, 25(10), 547–550. https://doi.org/10.1016/j.tree.2010.07.002 |
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Monitoring Frequency |
Priadka, P., Brown, G. S., Fedy, B. C., & Mallory, F. F. (2022). When can model‐based estimates replace surveys of wildlife populations that span many discrete management units? Ecological Solutions and Evidence, 3(2), e12149. https://doi.org/10.1002/2688-8319.12149 |
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Multiple Methods |
Davis, A. J., Keiter, D. A., Kierepka, E. M., Slootmaker, C., Piaggio, A. J., Beasley, J. C., & Pepin, K. M. (2020). A comparison of cost and quality of three methods for estimating density for wild pig (Sus scrofa). Scientific Reports, 10(1), 2047. https://doi.org/10.1038/s41598-020-58937-0 |
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Multiple Methods |
Graves, T. A., Yarnall, M. J., Johnston, A. N., Preston, T. M., Chong, G. W., Cole, E. K., Janousek, W. M., & Cross, P. C. (2022). Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPS collar data. Ecological Applications, 32(5), e2600. https://doi.org/10.1002/eap.2600 |
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Multiple Methods |
Keegan, T. W., Ackerman, B. B., Aoude, A. N., Bender, L. C., Boudreau, T., Carpenter, L. H., Compton, B. B., Elmer, M., Heffelfinger, J. R., Lutz, D. W., Trindle, B. D., Wakeling, B. F., & Watkins, B. E. (2011). Methods For Monitoring Mule Deer Populations. Mule Deer Working Group, Western Association of Fish and Wildlife Agencies, USA. https://wafwa.org/wpdm-package/methods-for-monitoring-mule-deer-populations/ |
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Multiple Methods |
Forsyth, D. M., Comte, S., Davis, N. E., Bengsen, A. J., Côté, S. D., Hewitt, D. G., Morellet, N., & Mysterud, A. (2022). Methodology matters when estimating deer abundance: A global systematic review and recommendations for improvements. The Journal of Wildlife Management, 86(4), e22207. https://doi.org/10.1002/jwmg.22207 |
Article |
Multiple Methods |
National Boreal Caribou Knowledge Consortium [NBCKC]. (2019). Boreal Caribou Monitoring In Canada—Part I: Perspectives from the NBCKC Monitoring Working Group 2019. https://www.cclmportal.ca/resource/boreal-caribou-monitoring-canada-part-i-perspectives-nbckc-monitoring-working-group |
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Multiple Methods |
National Boreal Caribou Knowledge Consortium [NBCKC]. (2024). Knowing the Herd: A summary of Boreal Caribou Monitoring In Canada—Part I: Perspectives from the NBCKC Monitoring Working Group 2019. https://www.cclmportal.ca/resource/knowing-herd-summary-boreal-caribou-monitoring-canada-part-i-perspectives-nbckc-monitoring |
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Multiple Methods |
National Boreal Caribou Knowledge Consortium [NBCKC / CNSCB]. (2024). National Boreal Caribou Knowledge Consortium Glossary: Creating a common language. https://www.cclmportal.ca/resource/creating-common-language-glossary#:~:text=The goal of the National,A Toolkit for Respectful Collaboration |
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Multiple Methods |
Delisle, Z. J., McGovern, P. G., Dillman, B. G., Reeling, C. J., Caudell, J. N., & Swihart, R. K. (2023). Using cost‐effectiveness analysis to compare density‐estimation methods for large‐scale wildlife management. Wildlife Society Bulletin, 47(2), e1430. https://doi.org/10.1002/wsb.1430 |
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Multiple Methods |
Koitzsch, K. B., Anton, C. B., Koitzsch, L. O., Tjepkes, T. L., Schumann, A. C., & Strasburg, J. L. (2022). A noninvasive and integrative approach for improving density and abundance estimates of moose. The Journal of Wildlife Management, 86(4), e22200. https://doi.org/10.1002/jwmg.22200 |
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Multiple Methods |
Mayer, M., Furuhovde, E., Nordli, K., Myriam Ausilio, G., Wabakken, P., Eriksen, A., Evans, A. L., Mathisen, K. M., & Zimmermann, B. (2024). Monitoring GPS‐collared moose by ground versus drone approaches: Efficiency and disturbance effects. Wildlife Biology, e01213. https://doi.org/10.1002/wlb3.01213 |
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Multiple Methods |
Moll, R. J., Poisson, M. K. P., Heit, D. R., Jones, H., & Kantar, L. (2022). A Review of Methods to Estimate and Monitor Moose Density and Abundance. ALCES VOL., 58. https://alcesjournal.org/index.php/alces/article/view/1881 |
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Multiple Methods |
Found, R., & Patterson, B. R. (2020). Assessing Ungulate Populations in Temperate North America. Canadian Wildlife Biology and Management, 9(1). https://www.researchgate.net/publication/364337281_Assessing_ungulate_populations_in_temperate_North_America |
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Remote Cameras |
Becker, M., Huggard, D. J., Dickie, M., Warbington, C., Schieck, J., Herdman, E., Serrouya, R., & Boutin, S. (2022). Applying and testing a novel method to estimate animal density from motion‐triggered cameras. Ecosphere, 13(4), e4005. https://doi.org/10.1002/ecs2.4005 |
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Remote Cameras |
Burgar, J. M., Stewart, F. E. C., Volpe, J. P., Fisher, J. T., & Burton, A. C. (2018). Estimating density for species conservation: Comparing camera trap spatial count models to genetic spatial capture-recapture models. Global Ecology and Conservation, 15, e00411. https://doi.org/10.1016/j.gecco.2018.e00411 |
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Remote Cameras |
Burton, A. C., Neilson, E., Moreira, D., Ladle, A., Steenweg, R., Fisher, J. T., Bayne, E., & Boutin, S. (2015). REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. Journal of Applied Ecology, 52(3), 675–685. https://doi.org/10.1111/1365-2664.12432 |
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Remote Cameras |
Clarke, J., Bohm, H., Burton, C., & Constantinou, A. (2023). Using Camera Traps to Estimate Medium and Large Mammal Density: Comparison of Methods and Recommendations for Wildlife Managers. https://doi.org/10.13140/RG.2.2.18364.72320 |
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Remote Cameras |
Clapham, M., Miller, E., Nguyen, M., & Darimont, C. T. (2020). Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears. Ecology and Evolution, 10(23), 12883–12892. https://doi.org/10.1002/ece3.6840 |
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Remote Cameras |
Fennell, M., Beirne, C., & Burton, A. C. (2022). Use of object detection in camera trap image identification: Assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology. Global Ecology and Conservation, 35. https://doi.org/10.1016/j.gecco.2022.e02104 |
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Remote Cameras |
Fuller, A. K., Augustine, B. C., Morin, D. J., Pigeon, K., Boulanger, J., Lee, D. C., Bisi, F., & Garshelis, D. L. (2022). The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears. Global Ecology and Conservation, 35, e02075. https://doi.org/10.1016/j.gecco.2022.e02075 |
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Remote Cameras |
Goward, S. (2024). Using remote camera traps to monitor population demographics and community ecology of divii (Dall’s sheep): Part of a community-based monitoring program in the Northern Richardson Mountains, NT [University of Victoria]. https://dspace.library.uvic.ca/items/ff44ded3-8e82-4d5c-86ea-795c2fa75d65 |
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Remote Cameras |
Howe, E. J., Buckland, S. T., Després‐Einspenner, M., & Kühl, H. S. (2017). Distance sampling with camera traps. Methods in Ecology and Evolution, 8(11), 1558–1565. https://doi.org/10.1111/2041-210X.12790 |
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Remote Cameras |
Moeller, A. K., Lukacs, P. M., & Horne, J. S. (2018). Three novel methods to estimate abundance of unmarked animals using remote cameras. Ecosphere, 9(8), e02331. https://doi.org/10.1002/ecs2.2331 |
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Remote Cameras |
Morin, D. J., Boulanger, J., Bischof, R., Lee, D. C., Ngoprasert, D., Fuller, A. K., McLellan, B., Steinmetz, R., Sharma, S., Garshelis, D., Gopalaswamy, A., Nawaz, M. A., & Karanth, U. (2022). Comparison of methods for estimating density and population trends for low-density Asian bears. Global Ecology and Conservation, 35. https://doi.org/10.1016/j.gecco.2022.e02058 |
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Remote Cameras |
Sollmann, R. (2018). A gentle introduction to camera‐trap data analysis. African Journal of Ecology, 56(4), 740–749. https://doi.org/10.1111/aje.12557 |
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Remote Cameras |
Wearn, O. R., & Glover-Kapfer, P. (2017). Camera-trapping for conservation: A guide to best-practices (Vol. 1). http://dx.doi.org/10.13140/RG.2.2.23409.17767 |
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Remote Cameras |
Resources Information Standards Committee (RISC). (2019). Wildlife Camera Metadata Protocol; Standards for Components of British Columbia’s Biodiversity No. 44. https://www2.gov.bc.ca/assets/gov/environment/natural-resource-stewardship/nr-laws-policy/risc/wcmp_v1.pdf |
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Remote Cameras |
Alberta Remote Camera Steering Committee (RCSC). 2024. Remote Camera Metadata Standards: Standards for Alberta. Version 2.0. Edmonton, Alberta. https://ab-rcsc.github.io/RCSC-WildCAM_Remote-Camera-Survey-Guidelines-and-Metadata-Standards/2_metadata-standards/2_0.1_Citation-and-Info.html |
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Remote Cameras |
Alberta Remote Camera Steering Committee (RCSC), Stevenson, C., Hubbs, A., & Wildlife Cameras for Adaptive Management (WildCAM). (2024). Remote Camera Survey Guidelines: Guidelines for Western Canada. Edmonton, Alberta. https://ab-rcsc.github.io/RCSC-WildCAM_Remote-Camera-Survey-Guidelines-and-Metadata-Standards/1_survey-guidelines/1_0.1_Citation-and-Info.html |
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Remote Cameras |
WildCAM - “Software and Data Management Resources” (https://wildcams.ca/library/camera-trap-software-and-data-management/) |
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Remote Cameras |
WildCAM - “A comparison of different camera data platforms” |
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Remote Cameras |
Garland, L., Neilson, E., Avgar, T., Bayne, E., & Boutin. S. (2020) Random Encounter and Staying Time Model Testing with Human Volunteers. The Journal of Wildlife Management, 84(6), 1179–84. https://doi.org/10.1002/jwmg.21879. |
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Remote Cameras |
Oedekoven, C. S., Buckland, S. T., Mackenzie, M. L., King, R., Evans, K. O., & Burger, L. W. (2014). Bayesian Methods for Hierarchical Distance Sampling Models. Journal of Agricultural, Biological, and Environmental Statistics, 19(2), 219–39. https://doi.org/10.1007/s13253-014-0167-0. |
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Remote Cameras |
Kenney, A. J., S. Boutin, T. S. Jung, Murray. D. L., & N. Johnson. (2024). Motion-Sensitive Cameras Track Population Abundance Changes in a Boreal Mammal Community in Southwestern Yukon, Canada. Journal of Wildlife Management, 88(4), e22564. https://doi.org/10.1002/jwmg.22564. |
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Remote Cameras |
Coltrane, J., DeCesare, N. J., Horne, J. S., & Lukacs, P. M. (2024). Comparing camera-based ungulate density estimates: A case study using island populations of bighorn sheep and mule deer. The Journal of Wildlife Management, 88(7), e22636. https://doi.org/10.1002/jwmg.22636 |
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Remote Cameras |
Schmidt, G. M., Graves, T. A., Pederson, J. C., & Carroll, S. L. (2022). Precision and bias of spatial capture–recapture estimates: A multi‐site, multi‐year Utah black bear case study. Ecological Applications, 32(5), e2618. https://doi.org/10.1002/eap.2618 |
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Remote Cameras |
Delisle, Z. J., Miller, D. L., & Swihart, R. K. (2023). Modelling density surfaces of intraspecific classes using camera trap distance sampling. Methods in Ecology and Evolution, 14(5), 1287–1298. https://doi.org/10.1111/2041-210X.14093 |
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Telemetry |
Epps, C. W., Holton, P. B., Monello, R. J., Crowhurst, R. S., Gaulke, S. M., Janousek, W. M., Creech, T. G. & Graves, T. A. (2024) Population and Spatial Dynamics of Desert Bighorn Sheep in Grand Canyon during an Outbreak of Respiratory Pneumonia. Frontiers in Ecology and Evolution, 12, 1-22. https://doi.org/10.3389/fevo.2024.1377214 |
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Survey Design |
Efford, M. G., & Boulanger, J. (2019). Fast evaluation of study designs for spatially explicit capture–recapture. Methods in Ecology and Evolution, 10(9), 1529–1535. https://doi.org/10.1111/2041-210X.13239 |
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Survey Design |
Reynolds, J. H., Thompson, W. L., & Russell, B. (2011). Planning for success: Identifying effective and efficient survey designs for monitoring. Ecoregional-Scale Monitoring within Conservation Areas, in a Rapidly Changing Climate, 144(5), 1278–1284. https://doi.org/10.1016/j.biocon.2010.12.002 |
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Collaboration |
Merrill, E. H. (2015). Are management implications for the Journal ceremonial?: Editor’s Message. The Journal of Wildlife Management, 79(1), 1–2. https://doi.org/10.1002/jwmg.819 |
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Collaboration |
Merkle, J. A., Anderson, N. J., Baxley, D. L., Chopp, M., Gigliotti, L. C., Gude, J. A., Harms, T. M., Johnson, H. E., Merrill, E. H., Mitchell, M. S., Mong, T. W., Nelson, J., Norton, A. S., Sheriff, M. J., Tomasik, E., & VanBeek, K. R. (2019). A collaborative approach to bridging the gap between wildlife managers and researchers. The Journal of Wildlife Management, 83(8), 1644–1651. https://doi.org/10.1002/jwmg.21759 |
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Collaboration |
Lindenmayer, D. B., Lavery, T., & Scheele, B. C. (2022). Why We Need to Invest in Large-Scale, Long-Term Monitoring Programs in Landscape Ecology and Conservation Biology. Current Landscape Ecology Reports, 7(4), 137–146. https://doi.org/10.1007/s40823-022-00079-2 |
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Attendee Submissions (Unsorted) |
Bayne, E., Boutin, S., & Moses, R. (2008). Ecological factors influencing the spatial pattern of Canada lynx relative to its southern range edge in Alberta, Canada. Canadian Journal Of Zoology-Revue Canadienne De Zoologie, 86(10), 1189–1197. https://doi.org/10.1139/Z08-099 |
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Attendee Submissions |
Bayne, E., Moses, R., & Boutin, S. (2005). Evaluation of winter tracking protocols as a method for monitoring mammals in the Alberta Biodiversity Monitoring Program. Alberta Biodiversity Monitoring Institute. https://abmi.ca/home/publications/51-100/66 |
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Attendee Submissions |
Boulanger, J., Adamczewski, J., Nishi, J., Cluff, D., Williams, J., Sayine-Crawford, H., & LeClerc, L. M. (2018). Estimates of Breeding Females & Adult Herd Size and Analyses of Demographics for the Bluenose-East Herd of Barren-ground Caribou: 2018 Calving Ground Photographic Survey. https://www.gov.nt.ca/ecc/sites/ecc/files/resources/326_manuscript.pdf |
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Attendee Submissions |
Boulanger, J., Gunn, A., Adamczewski, J., & Croft, B. (2011). A data‐driven demographic model to explore the decline of the Bathurst caribou herd. The Journal of Wildlife Management, 75(4), 883–896. https://doi.org/10.1002/jwmg.108 |
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Attendee Submissions |
Boyce, M. S., Baxter, P. W. J., & Possingham, H. P. (2012). Managing moose harvests by the seat of your pants. Theoretical Population Biology, 82(4), 340–347. https://doi.org/10.1016/j.tpb.2012.03.002 |
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Attendee Submissions |
Boyce, M. S., & Corrigan, R. (2017). Moose survey app for population monitoring. Wildlife Society Bulletin, 41(1), 125–128. https://doi.org/10.1002/wsb.732 |
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Attendee Submissions |
Boyce, M. S., Johnson, C. J., Merrill, E. H., Nielsen, S. E., Solberg, E. J., & Van Moorter, B. (2016). REVIEW: Can habitat selection predict abundance? Journal of Animal Ecology, 85(1), 11–20. https://doi.org/10.1111/1365-2656.12359 |
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Attendee Submissions |
Boyce, M. S., & McDonald, L. L. (1999). Relating populations to habitats using resource selection functions. Trends in Ecology & Evolution, 14(7), 268–272. https://doi.org/10.1016/S0169-5347(99)01593- |
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Attendee Submissions |
Broekman, M. J. E., Hoeks, S., Freriks, R., Langendoen, M. M., Runge, K. M., Savenco, E., Ter Harmsel, R., Huijbregts, M. A. J., & Tucker, M. A. (2023). HomeRange: A global database of mammalian home ranges. Global Ecology and Biogeography, 32(2), 198–205. https://doi.org/10.1111/geb.13625 |
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Attendee Submissions |
Buckland, S. T., Borchers, D. L., Marques, T. A., & Fewster, R. M. (2023). Wildlife Population Assessment: Changing Priorities Driven by Technological Advances. Journal of Statistical Theory and Practice, 17(2), 20. https://doi.org/10.1007/s42519-023-00319-6 |
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Attendee Submissions |
Nagy-Reis, M., Reimer, J. R., Lewis, M. A., Jensen, W. F., & Boyce, M. S. (2021). Aligning population models with data: Adaptive management for big game harvests. Global Ecology and Conservation, 26, e01501. https://doi.org/10.1016/j.gecco.2021.e01501 |
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Attendee Submissions |
Nielsen, S., Bayne, E., Schieck, J., Herbers, J., & Boutin, S. (2007). A new method to estimate species and biodiversity intactness using empirically derived reference conditions. Biological Conservation, 137(3), 403–414. https://doi.org/10.1016/j.biocon.2007.02.024 |
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Attendee Submissions |
Peters, W., Hebblewhite, M., Smith, K. G., Webb, S. M., Webb, N., Russell, M., Stambaugh, C., & Anderson, R. B. (2014). Contrasting aerial moose population estimation methods and evaluating sightability in west‐central Alberta, Canada. Wildlife Society Bulletin, 38(3), 639–649. https://doi.org/10.1002/wsb.433 |