The DOI matches its Crossref record; 231 of the 231 references it cites were found in a registry or an index.
Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models
Open accessAbstract
In the face of the growing challenges brought about by human activities, effective planning and decision-making in biodiversity and ecosystem conservation, restoration, and sustainable development are urgently needed. Ecological models can play a key role in supporting this need and helping to safeguard the natural assets that underpin human wellbeing and support life on land and below water (United Nations Sustainable Development Goals; SDG 15 & 14). The urgency and complexity of safeguarding forest (SDG 15.2) and mountain ecosystems (SDG 15.4), for example, and halting decline in biodiversity (SDG 15.5) in the Anthropocene requires a re-envisioning of how ecological models can best support the comprehensive assessments of biodiversity and its change that are required for successful action. A key opportunity to advance ecological modeling for both predictive and explanatory purposes arises through a collaboration between ecologists and the Earth observation community, and a close integration of remote sensing and species distribution models. Remote sensing products have the capacity to provide continuous spatiotemporal information about key factors driving the distribution of organisms, therefore improving both the use and accuracy of these models for management and planning. Here we first survey the literature on remote sensing data products available to ecological modelers interested in improving predictions of species range dynamics under global change. We specifically explore the key biophysical processes underlying the distribution of species in the Anthropocene including climate variability, changes in land cover, and disturbances. We then discuss potential synergies between the ecological modeling and remote sensing communities, and highlight opportunities to close the data and conceptual gaps that currently impede a more effective application of remote sensing for the monitoring and modeling of ecological systems. Specific attention is given to how potential collaborations between the two communities could lead to new opportunities to report on progress towards global agendas - such as the Agenda 2030 for sustainable development of the United Nations or the Post-2020 Global Biodiversity Framework of the Convention for Biological Diversity, and help guide conservation and management strategies towards sustainability.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
High-Resolution Global Maps of 21st-Century Forest Cover Change
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., et al. (2013) · Science · doi:10.1126/science.1244693 · Cited by 11,704
Barker, T. J., Bashmakov, I., Bernstein, L., Bogner, J. R., Bosch, P. C., Dave, R., Davidson, O., Fisher, B. S., Grubb, M. J., Gupta, S. K., et al. (2007) · Socio-Environmental Systems Modeling · Cited by 9,160
Predictive habitat distribution models in ecology
Guisan, A., & Zimmermann, N. E. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00354-9 · Cited by 7,359
Self-citationPredicting species distribution: offering more than simple habitat models
Guisan, A., & Thuiller, W. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00792.x · Cited by 6,587
Self-citationRapid Range Shifts of Species Associated with High Levels of Climate Warming
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B., & Thomas, C. D. (2011) · Science · doi:10.1126/science.1206432 · Cited by 5,518
Defaunation in the Anthropocene
Dirzo, R., Young, H. S., Galetti, M., Ceballos, G., Isaac, N. J. B., & Collen, B. (2014) · Science · doi:10.1126/science.1251817 · Cited by 4,474
Global effects of land use on local terrestrial biodiversity
Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., Börger, L., Bennett, D. J., Choimes, A., Collen, B., et al. (2015) · Nature · doi:10.1038/nature14324 · Cited by 4,352
Pearson, R. G., & Dawson, T. P. (2003) · Global Ecology and Biogeography · doi:10.1046/j.1466-822x.2003.00042.x · Cited by 4,252
Climatologies at high resolution for the earth’s land surface areas
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017) · Scientific Data · doi:10.1038/sdata.2017.122 · Cited by 4,171
Self-citation‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012) · Geomorphology · doi:10.1016/j.geomorph.2012.08.021 · Cited by 4,044
Cited by
Venter, Z. S., Barton, D. N., Chakraborty, T., Simensen, T., & Singh, G. (2022) · Remote Sensing · doi:10.3390/rs14164101 · Cited by 380
Berta Aneseyee, A., Noszczyk, T., Soromessa, T., & Elias, E. (2020) · Remote Sensing · doi:10.3390/rs12071103 · Cited by 283
Metadata
- Authors
- Randin, C. F., Ashcroft, M. B., Bolliger, J., Cavender-Bares, J., Coops, N. C., Dullinger, S., Dirnböck, T., Eckert, S., Ellis, E., Fernández, N., Giuliani, G., Guisan, A., Jetz, W., Joost, S., Karger, D., Lembrechts, J., Lenoir, J., Luoto, M., Morin, X., Price, B., Rocchini, D., Schaepman, M., Schmid, B., Verburg, P., Wilson, A., Woodcock, P., Yoccoz, N., & Payne, D.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 239
- Pages
- 111626
- ISSN
- 0034-4257 (ISSN-L)
- OpenAlex
- W3000080835
- Cited by
- 265
- References
- 231
- FWCI
- 62.42
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. https://app.cochat.ai/papers/10.1016/j.rse.2019.111626
- The paper
- Randin, C. F., Ashcroft, M. B., Bolliger, J., Cavender-Bares, J., Coops, N. C., Dullinger, S., Dirnböck, T., Eckert, S., Ellis, E., Fernández, N., Giuliani, G., Guisan, A., Jetz, W., Joost, S., Karger, D., Lembrechts, J., Lenoir, J., Luoto, M., Morin, X., . . . Payne, D. (2020). Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2019.111626
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.