Detection of rabbit and wombat warrens in broad-scale satellite imagery
Natarsha McPherson![https://orcid.org/0000-0002-0004-6119](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-5868-3567](/media/client/orcid_16x16.png)
A
Abstract
The ability to accurately assess the spatial pattern of wild animal distributions is essential for conservation management. Warrens constructed by burrowing species offer proxies for estimating geographic occupation. We tested the feasibility of open-access satellite-based detection for two semi-fossorial mammals, the southern hairy-nosed wombat (Lasiorhinus latifrons) and European rabbit (Oryctolagus cuniculus), across the Nullarbor Plain, South Australia. Along two 100 km road sections, we collected GPS locations of warrens from ground-walked transects (~1 km) at 22 sites. Wombat and rabbit warrens were identified and digitised using Google Earth and Microsoft Bing imagery (<1.5 m resolution) for each transect. We found a significant correlation between satellite and field estimates with R2 values of 0.98 (P < 1 × 10−15) and 0.56 (P < 1 × 10−4) for wombat and rabbit warrens, respectively. User accuracy was high for both wombat (91%) and rabbit (81%) warrens. Omission in the satellite imagery was low for wombats (14%) but high for rabbit warrens (44%). However, small warrens less than 10 m in diameter accounted for 79% of rabbit warren omissions. This demonstrates that the geospatial pattern of warrens constructed by two semi-fossorial mammals can be detected and distinguished in broad-scale satellite imagery across Australia’s semi-arid landscape.
Keywords: burrowing, rabbits, remote detection, satellite imagery, semi-fossorial mammals, species distribution, wildlife monitoring, wombats.
References
Ainley, D. G., Larue, M. A., Stirling, I., Stammerjohn, S., and Siniff, D. B. (2015). An apparent population decrease, or change in distribution, of Weddell seals along the Victoria Land coast. Marine Mammal Science 31(4), 1338-1361.
| Crossref | Google Scholar |
Allen, C. R., Johnson, A. R., and Parris, L. (2006). A Framework for Spatial Risk Assessments: Potential Impacts of Nonindigenous Invasive Species on Native Species. Ecology and Society 11(1), 39.
| Crossref | Google Scholar |
Attard, M. R. G., Phillips, R. A., Bowler, E., Clarke, P. J., Cubaynes, H., Johnston, D. W., and Fretwell, P. T. (2024). Review of satellite remote sensing and unoccupied aircraft systems for counting wildlife on land. Remote Sensing 16(4), 627.
| Crossref | Google Scholar |
Bachman, S. P., Field, R., Reader, T., Raimondo, D., Donaldson, J., Schatz, G. E., and Lughadha, E. N. (2019). Progress, challenges and opportunities for Red Listing. Biological Conservation 234, 45-55.
| Crossref | Google Scholar |
Bassing, S. B., DeVivo, M., Ganz, T. R., Kertson, B. N., Prugh, L. R., Roussin, T., Satterfield, L., Windell, R. M., Wirsing, A. J., and Gardner, B. (2023). Are we telling the same story? Comparing inferences made from camera trap and telemetry data for wildlife monitoring. Ecological Applications 33(1), e2745.
| Crossref | Google Scholar |
Bentze, C., Burningham, H., and Magee, E. (2023). Down the rabbit-hole: satellite-based analysis of spatiotemporal patterns in wild European rabbit burrows for better coastal dune management. Journal of Coastal Conservation 27(6), 61.
| Crossref | Google Scholar |
Bolyn, C., Lejeune, P., Michez, A., and Latte, N. (2022). Mapping tree species proportions from satellite imagery using spectral–spatial deep learning. Remote Sensing of Environment 280, 113205.
| Crossref | Google Scholar |
Borchers, D. L., and Marques, T. A. (2017). From distance sampling to spatial capture–recapture. AStA. Advances in Statistical Analysis 101(4), 475-494.
| Crossref | Google Scholar |
Burgess, M. D., Eaton, M. A., and Gregory, R. D. (2020). A review of spatial patterns across species ranges to aid the targeting of conservation interventions. Biological Conservation 251, 108755.
| Crossref | Google Scholar |
Camino, M., Thompson, J., Andrade, L., Cortez, S., Matteucci, S. D., and Altrichter, M. (2020). Using local ecological knowledge to improve large terrestrial mammal surveys, build local capacity and increase conservation opportunities. Biological Conservation 244, 108450.
| Crossref | Google Scholar |
Cappelle, N., Howe, E. J., Boesch, C., and Kühl, H. S. (2021). Estimating animal abundance and effort–precision relationship with camera trap distance sampling. Ecosphere 12, e03299.
| Crossref | Google Scholar |
Chabot, D., Stapleton, S., and Francis, C. M. (2022). Using Web images to train a deep neural network to detect sparsely distributed wildlife in large volumes of remotely sensed imagery: A case study of polar bears on sea ice. Ecological Informatics 68, 101547.
| Crossref | Google Scholar |
Chrétien, L.-P., Théau, J., and Ménard, P. (2016). Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system. Wildlife Society Bulletin 40, 181-191.
| Crossref | Google Scholar |
Cooke, B. D. (2012). Rabbits: manageable environmental pests or participants in new Australian ecosystems? Wildlife Research (East Melbourne) 39(4), 279-289.
| Crossref | Google Scholar |
Corcoran, E., Winsen, M., Sudholz, A., and Hamilton, G. (2021). Automated detection of wildlife using drones: Synthesis, opportunities and constraints. Methods in Ecology and Evolution 12(6), 1103-1114.
| Crossref | Google Scholar |
Cosentino, F., and Maiorano, L. (2021). Is geographic sampling bias representative of environmental space? Ecological Informatics 64, 101369.
| Crossref | Google Scholar |
Cox, T. E., Matthews, R., Halverson, G., and Morris, S. (2021). Hot stuff in the bushes: Thermal imagers and the detection of burrows in vegetated sites. Ecology and Evolution 11(11), 6406-6414.
| Crossref | Google Scholar | PubMed |
Dean, A. T., Brandle, R., Barmuta, L. A., Jones, M. E., and Jansen, J. (2023). Rabbit warrens: an important resource for invasive alien species in semi-arid Australia. Wildlife Research 51, WR22154.
| Crossref | Google Scholar |
Delplanque, A., Théau, J., Foucher, S., Serati, G., Durand, S., and Lejeune, P. (2024). Wildlife detection, counting and survey using satellite imagery: are we there yet? GIScience and Remote Sensing 61(1), 2348863.
| Crossref | Google Scholar |
Duckworth, J. W. (1998). The difficulty of estimating population densities of nocturnal forest mammals from transect counts of animals. Journal of Zoology 246(4), 466-468.
| Crossref | Google Scholar |
Duporge, I., Isupova, O., Reece, S., Macdonald, D. W., Wang, T., Pettorelli, N., and Buchanan, G. (2021). Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. Remote Sensing in Ecology and Conservation 7(3), 369-381.
| Crossref | Google Scholar |
Elith, J., and Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annual Review of Ecology, Evolution, and Systematics 40(1), 677-697.
| Crossref | Google Scholar |
Estevo, C., Nagy-Reis, M., and Nichols, J. (2017). When habitat matters: Habitat preferences can modulate co-occurrence patterns of similar sympatric species. PLoS One 12(7), e0179489.
| Crossref | Google Scholar | PubMed |
Feldman, M. J., Imbeau, L., Marchand, P., Mazerolle, M. J., Darveau, M., and Fenton, N. J. (2021). Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS One 16(3), e0234587.
| Crossref | Google Scholar | PubMed |
Finlayson, G. R., Shimmin, G. A., Temple-Smith, P. D., Handasyde, K. A., and Taggart, D. A. (2005). Burrow use and ranging behaviour of the southern hairy-nosed wombat (Lasiorhinus latifrons) in the Murraylands, South Australia. Journal of Zoology 265(2), 189-200.
| Crossref | Google Scholar |
Finlayson, G., Taggart, P., and Cooke, B. (2022). Recovering Australia’s arid-zone ecosystems: learning from continental-scale rabbit control experiments. Restoration Ecology 30, e13552.
| Crossref | Google Scholar |
Fretwell, P. T., and Trathan, P. N. (2009). Penguins from space: faecal stains reveal the location of emperor penguin colonies. Global Ecology and Biogeography 18(5), 543-552.
| Crossref | Google Scholar |
Fretwell, P. T., Trathan, P. N., Scales, K., and Bouchet, P. (2021). Discovery of new colonies by Sentinel2 reveals good and bad news for emperor penguins. Remote Sensing in Ecology and Conservation 7(2), 139-153.
| Crossref | Google Scholar |
Gedeon, C. I., Árvai, M., Szatmári, G., Brevik, E. C., Takáts, T., Kovács, Z. A., and Mészáros, J. (2022). Identification and counting of European Souslik burrows from UAV images by pixel-based image analysis and random forest classification: a simple, semi-automated, yet accurate method for estimating population size. Remote Sensing 14(9), 2025.
| Crossref | Google Scholar |
Gillieson, D., Wallbrink, P., and Cochrane, A. (1996). Vegetation change, erosion risk and land management on the Nullarbor Plain, Australia. Environmental Geology 28(3), 145-153.
| Crossref | Google Scholar |
Haussmann, N. S. (2017). Soil movement by burrowing mammals: A review comparing excavation size and rate to body mass of excavators. Progress in Physical Geography 41, 29-45.
| Crossref | Google Scholar |
Hollings, T., Burgman, M., van Andel, M., Gilbert, M., Robinson, T., Robinson, A., and McPherson, J. (2018). How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals. Methods in Ecology and Evolution 9(4), 881-892.
| Crossref | Google Scholar |
Ingram, D. J., Willcox, D., and Challender, D. W. S. (2019). Evaluation of the application of methods used to detect and monitor selected mammalian taxa to pangolin monitoring. Global Ecology and Conservation 18, e00632.
| Crossref | Google Scholar |
Jachmann, H. (2002). Comparison of aerial counts with ground counts for large African herbivores. Journal of Applied Ecology 39(5), 841-852.
| Crossref | Google Scholar |
Jansen, J., Jansen, J., Dean, A. T., Brandle, R., Peacock, D. E., and Jones, M. E. (2023). High-resolution mapping of rabbit (Oryctolagus cuniculus) densities for targeted conservation management. Journal of Applied Ecology 60(12), 2602-2612.
| Crossref | Google Scholar |
Johnston, A., Matechou, E., and Dennis, E. B. (2023). Outstanding challenges and future directions for biodiversity monitoring using citizen science data. Methods in Ecology and Evolution 14, 103-116.
| Crossref | Google Scholar |
Knoblauch, W., Carver, S., Driessen, M. M., Gales, R., and Richards, S. A. (2023). Abundance and population growth estimates for bare‐nosed wombats. Ecology and Evolution 13(9), e10465.
| Crossref | Google Scholar | PubMed |
Koshkina, A., Grigoryeva, I., Tokarsky, V., Urazaliyev, R., Kuemmerle, T., Hölzel, N., and Kamp, J. (2019). Marmots from space: assessing population size and habitat use of a burrowing mammal using publicly available satellite images. Remote Sensing in Ecology and Conservation 6(2), 153-167.
| Crossref | Google Scholar |
Kramer‐Schadt, S., Niedballa, J., Pilgrim, J. D., Schröder, B., Lindenborn, J., Reinfelder, V., Stillfried, M., Heckmann, I., Scharf, A. K., Augeri, D. M., Cheyne, S. M., Hearn, A. J., Ross, J., Macdonald, D. W., Mathai, J., Eaton, J., Marshall, A. J., Semiadi, G., Rustam, R., Bernard, H., Alfred, R., Samejima, H., Duckworth, J. W., Breitenmoser‐Wuersten, C., Belant, J. L., Hofer, H., Wilting, A., Robertson, M., Robertson, M., and Robertson, M. (2013). Importance of correcting for sampling bias in MaxEnt species distribution models. Diversity & Distributions 19(11), 1366-1379.
| Crossref | Google Scholar |
Löffler, E., and Margules, C. (1980). Wombats detected from space. Remote Sensing of Environment 9, 47-56.
| Crossref | Google Scholar |
Łopucki, R., Klich, D., and Kociuba, P. (2022). Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques. Scientific Reports 12, 8264.
| Crossref | Google Scholar | PubMed |
Lynch, H. J., and Schwaller, M. R. (2014). Mapping the abundance and distribution of Adélie penguins using landsat-7: First steps towards an integrated multi-sensor pipeline for tracking populations at the continental scale. PLoS One 9(11), e113301.
| Crossref | Google Scholar | PubMed |
Martin, J. T., and Zickefoos, E. (1976). The effectiveness of aerial survey for determining the distribution of rabbit warrens in a semiarid enviroment. Wildlife Research 3, 79-84.
| Crossref | Google Scholar |
Matthews, A., Spooner, P. G., Lunney, D., Green, K., and Klomp, N. I. (2010). Influences of snow cover, vegetation and topography on the upper range limit of common wombats Vombatus ursinus in the subalpine zone, Australia. Diversity & Distributions 16(2), 277-287.
| Crossref | Google Scholar |
Matthias, L., Allison, M. J., Maslovat, C. Y., Hobbs, J., and Helbing, C. C. (2021). Improving ecological surveys for the detection of cryptic, fossorial snakes using eDNA on and under artificial cover objects. Ecological Indicators 131, 108187.
| Crossref | Google Scholar |
Molyneux, J., Pavey, C. R., James, A. I., and Carthew, S. M. (2017). The efficacy of monitoring techniques for detecting small mammals and reptiles in arid environments. Wildlife Research (East Melbourne) 44(6–7), 534-545.
| Crossref | Google Scholar |
Norris, R., Koertner, G., Meek, P., Cairns, S. C., and Old, J. (2024). Digging for answers: defining the external architecture of the southern hairy-nosed wombats’ (Lasiorhinus latifrons) subterranean excavations. Australian Mammalogy 46(3), AM24027.
| Crossref | Google Scholar |
Nuttall, M. N., Griffin, O., Fewster, R. M., McGowan, P. J. K., Abernethy, K., O’Kelly, H., Nut, M., Sot, V., and Bunnefeld, N. (2022). Long‐term monitoring of wildlife populations for protected area management in Southeast Asia. Conservation Science and Practice 4(2), e614.
| Crossref | Google Scholar |
O’Brien, C., Sparrow, E., Dibben, R., Ostendorf, B., and Taggart, D. (2021). Evaluation of olfactory and visual cues to deter southern hairy-nosed wombats (Lasiorhinus latifrons) from their burrows. Australian Mammalogy 43(1), 110-119.
| Crossref | Google Scholar |
Ostendorf, B. (2011). Overview: Spatial information and indicators for sustainable management of natural resources. Ecological Indicators 11, 97-102.
| Crossref | Google Scholar |
Ostendorf, B., Boardman, W. S. J., and Taggart, D. A. (2016). Islands as refuges for threatened species: multispecies translocation and evidence of species interactions four decades on. Australian Mammalogy 38(2), 204-212.
| Crossref | Google Scholar |
Otis, D. (1998). Analysis of the Influence of Spatial Pattern in Habitat Selection Studies. Journal of Agricultural, Biological, and Environmental Statistics 3(3), 254-267.
| Crossref | Google Scholar |
Pedler, R. D., Brandle, R., Read, J. L., Southgate, R., Bird, P., and Moseby, K. E. (2016). Rabbit biocontrol and landscape-scale recovery of threatened desert mammals. Conservation Biology 30(4), 774-782.
| Crossref | Google Scholar | PubMed |
Phillips, S. J., Dudík, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., and Ferrier, S. (2009). Sample Selection Bias and Presence-Only Distribution Models: Implications for Background and Pseudo-Absence Data. Ecological Applications 19(1), 181-197.
| Crossref | Google Scholar | PubMed |
Pike, D. A., and Mitchell, J. C. (2013). Burrow-dwelling ecosystem engineers provide thermal refugia throughout the landscape. Animal Conservation 16(6), 694-703.
| Crossref | Google Scholar |
Psiroukis, V., Malounas, I., Mylonas, N., Grivakis, K.-E., Fountas, S., and Hadjigeorgiou, I. (2021). Monitoring of free-range rabbits using aerial thermal imaging. Smart Agricultural Technology 1, 100002.
| Crossref | Google Scholar |
Read, J. L., Carter, J., Moseby, K. M., and Greenville, A. (2008). Ecological roles of rabbit, bettong and bilby warrens in arid Australia. Journal of Arid Environments 72(11), 2124-2130.
| Crossref | Google Scholar |
Rivas, A., Chamoso, P., González-Briones, A., and Corchado, J. M. (2018). Detection of cattle using drones and convolutional neural networks. Sensors 18(7), 2048.
| Crossref | Google Scholar | PubMed |
Seidlitz, A., Bryant, K. A., Armstrong, N. J., Calver, M. C., and Wayne, A. F. (2021). Sign surveys can be more efficient and cost effective than driven transects and camera trapping: a comparison of detection methods for a small elusive mammal, the numbat (Myrmecobius fasciatus). Wildlife Research 48(6), 491-500.
| Crossref | Google Scholar |
Shalom, I., Calfayan, L. M., Rospide, M., Thornton, L., Burgos, E. F., and Gómez Villafañe, I. E. (2024). Do exotic invasive mammals disturb the native fauna? Spatiotemporal distribution and overlap between species in a national park of Argentina. Integrative Zoology 0, 1-17.
| Crossref | Google Scholar | PubMed |
Shimmin, G. A., Skinner, J., and Baudinette, R. V. (2002). The warren architecture and environment of the southern hairy-nosed wombat (Lasiorhinus latifrons). Journal of Zoology 258(4), 469-477.
| Crossref | Google Scholar |
Sinclair, S. J., White, M. D., and Newell, G. R. (2010). How Useful Are Species Distribution Models for Managing Biodiversity under Future Climates? Ecology and Society 15(1), 8.
| Crossref | Google Scholar |
Smith, J. E., and Pinter‐Wollman, N. (2021). Observing the unwatchable: Integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. Journal of Animal Ecology 90, 62-75.
| Crossref | Google Scholar | PubMed |
Sofaer, H. R., Jarnevich, C. S., Pearse, I. S., Smyth, R. L., Auer, S., Cook, G. L., Edwards, T. C., Guala, G. F., Howard, T. G., Morisette, J. T., and Hamilton, H. (2019). Development and Delivery of Species Distribution Models to Inform Decision-Making. BioScience 69(7), 544-557.
| Crossref | Google Scholar |
Southwell, D., Skroblin, A., Moseby, K., Southgate, R., Indigo, N., Backhouse, B., Bellchambers, K., Brandle, R., Brenton, P., Copley, P., Dziminski, M. A., Galindez‐Silva, C., Lynch, C., Newman, P., Pedler, R., Rogers, D., Roshier, D. A., Ryan‐Colton, E., Tuft, K., Ward, M., Zurell, D., and Legge, S. (2023). Designing a large‐scale track‐based monitoring program to detect changes in species distributions in arid Australia. Ecological Applications 33(2), e2762.
| Crossref | Google Scholar |
Stapleton, S., LaRue, M., Lecomte, N., Atkinson, S., Garshelis, D., Porter, C., and Atwood, T. (2014). Polar bears from space: Assessing satellite imagery as a tool to track arctic wildlife. PloS One 9(7), e101513.
| Crossref | Google Scholar | PubMed |
Steinbeiser, C. M., Kioko, J., Maresi, A., Kaitilia, R., and Kiffner, C. (2019). Relative abundance and activity patterns explain method-related differences in mammalian species richness estimates. Journal of Mammalogy 100, 192-201.
| Crossref | Google Scholar |
Sudholz, A., Denman, S., Pople, A., Brennan, M., Amos, M., and Hamilton, G. (2022). A comparison of manual and automated detection of rusa deer (Rusa timorensis) from RPAS-derived thermal imagery. Wildlife Research 49, 46-53.
| Crossref | Google Scholar |
Swinbourne, M. J., Taggart, D. A., Swinbourne, A. M., Lewis, M., and Ostendorf, B. (2018). Using satellite imagery to assess the distribution and abundance of southern hairy-nosed wombats (Lasiorhinus latifrons). Remote Sensing of Environment 211, 196-203.
| Crossref | Google Scholar |
Swinbourne, M., Taggart, D., and Ostendorf, B. (2021). The population status of southern hairy-nosed wombats (Lasiorhinus latifrons). I. Distribution and abundance. Australian Mammalogy 43, 40-53.
| Crossref | Google Scholar |
Taggart, D. A., Finlayson, G. R., Sparrow, E. E., Dibben, R. M., Dibben, J. A., Campbell, E. C., Peacock, D. E., Ostendorf, B., White, C. R., and Temple-Smith, P. D. (2020). Environmental factors influencing hairy-nosed wombat abundance in semi-arid rangelands. Journal of Wildlife Management 84(5), 921-929.
| Crossref | Google Scholar |
Thornett, E., Ostendorf, B., and Taggart, D. A. (2017). Interspecies co-use of southern hairy-nosed wombat (Lasiorhinus latifrons) burrows. Australian Mammalogy 39(2), 205-212.
| Crossref | Google Scholar |
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M.W., van Langevelde, F., Burghardt, T., Kays, R., Klinck, H., Wikelski, M., Couzin, I. D., van Horn, G., Crofoot, M. C., Stewart, C.V., and Berger-Wolf, T. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications 13, 792.
| Crossref | Google Scholar | PubMed |
Vidal‐García, M., and Keogh, J. S. (2017). Invasive cane toads are unique in shape but overlap in ecological niche compared to Australian native frogs. Ecology and Evolution 7(19), 7609-7619.
| Crossref | Google Scholar | PubMed |
Wagner, B., Baker, P., and Nitschke, C. (2021). The influence of spatial patterns in foraging habitat on the abundance and home range size of a vulnerable arboreal marsupial in southeast Australia. Conservation Science and Practice 3(12), e566.
| Crossref | Google Scholar |
Walker, F. M., Taylor, A. C., and Sunnucks, P. (2007). Does soil type drive social organization in southern hairy-nosed wombats. Molecular Ecology 16(1), 199-208.
| Crossref | Google Scholar | PubMed |
Wang, K., Franklin, S. E., Guo, X., and Cattet, M. (2010). Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists. Sensors 10(11), 9647-9667.
| Crossref | Google Scholar | PubMed |
Whittington-Jones, G. M., Bernard, R. T. F., and Parker, D. M. (2011). Aardvark burrows: a potential resource for animals in arid and semi-arid environments. African Zoology 46(2), 362-370.
| Crossref | Google Scholar |
Wintle, B. A., Cadenhead, N. C. R., Morgain, R. A., Legge, S. M., Bekessy, S. A., Cantele, M., Possingham, H. P., Watson, J. E. M., Maron, M., Keith, D. A., Garnett, S. T., Woinarski, J. C. Z., and Lindenmayer, D. B. (2019). Spending to save: What will it cost to halt Australia’s extinction crisis? Conservation Letters 12(6), e12682.
| Crossref | Google Scholar |
Witmer, G. W. (2005). Wildlife population monitoring: some practical considerations. Wildlife Research 32(3), 259-263.
| Crossref | Google Scholar |
Woinarski, J., Crase, B., Garnett, S., and Rumpff, L. (2021). Addressing issues relating to the conservation of data-poor species, and options for their resolution. NESP Threatened Species Recovery Hub Project 5.2 report, Brisbane. https://www.nespthreatenedspecies.edu.au/media/svgbxrcm/5-2-addressing-issues-relating-to-the-conservation-of-data-poor-species-report_v5.pdf
Zurell, D., Franklin, J., König, C., Bouchet, P. J., Dormann, C. F., Elith, J., Fandos, G., Feng, X., Guillera‐Arroita, G., Guisan, A., Lahoz‐Monfort, J. J., Leitão, P.J., Park, D. S., Peterson, A. T., Rapacciuolo, G., Schmatz, D. R., Schröder, B., Serra‐Diaz, J. M., Thuiller, W., Yates, K. L., Zimmermann, N. E., and Merow, C. (2020). A standard protocol for reporting species distribution models. Ecography 43(9), 1261-1277.
| Crossref | Google Scholar |