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Jung M. Predictability and transferability of local biodiversity environment relationships. PeerJ 2022; 10:e13872. [PMID: 36032939 PMCID: PMC9415358 DOI: 10.7717/peerj.13872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/19/2022] [Indexed: 01/18/2023] Open
Abstract
Background Biodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures-such as species richness or abundance-are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted. Methods Here I combine estimates of biodiversity measures from terrestrial local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within ("predictability") and across similar ("transferability") biodiversity surveys. Results I found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question-particular for transferability-our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.
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Tuck SL, Phillips HR, Hintzen RE, Scharlemann JP, Purvis A, Hudson LN. MODISTools - downloading and processing MODIS remotely sensed data in R. Ecol Evol 2014; 4:4658-68. [PMID: 25558360 PMCID: PMC4278818 DOI: 10.1002/ece3.1273] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 08/29/2014] [Accepted: 09/01/2014] [Indexed: 11/30/2022] Open
Abstract
Remotely sensed data – available at medium to high resolution across global spatial and temporal scales – are a valuable resource for ecologists. In particular, products from NASA's MODerate-resolution Imaging Spectroradiometer (MODIS), providing twice-daily global coverage, have been widely used for ecological applications. We present MODISTools, an R package designed to improve the accessing, downloading, and processing of remotely sensed MODIS data. MODISTools automates the process of data downloading and processing from any number of locations, time periods, and MODIS products. This automation reduces the risk of human error, and the researcher effort required compared to manual per-location downloads. The package will be particularly useful for ecological studies that include multiple sites, such as meta-analyses, observation networks, and globally distributed experiments. We give examples of the simple, reproducible workflow that MODISTools provides and of the checks that are carried out in the process. The end product is in a format that is amenable to statistical modeling. We analyzed the relationship between species richness across multiple higher taxa observed at 526 sites in temperate forests and vegetation indices, measures of aboveground net primary productivity. We downloaded MODIS derived vegetation index time series for each location where the species richness had been sampled, and summarized the data into three measures: maximum time-series value, temporal mean, and temporal variability. On average, species richness covaried positively with our vegetation index measures. Different higher taxa show different positive relationships with vegetation indices. Models had high R2 values, suggesting higher taxon identity and a gradient of vegetation index together explain most of the variation in species richness in our data. MODISTools can be used on Windows, Mac, and Linux platforms, and is available from CRAN and GitHub (https://github.com/seantuck12/MODISTools).
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Affiliation(s)
- Sean L Tuck
- Department of Plant Sciences, University of Oxford Oxford, OX1 3RB, U.K
| | - Helen Rp Phillips
- Department of Life Sciences, Imperial College London, Silwood Park Buckhurst Road, Ascot, Berkshire, SL5 7PY, U.K ; Department of Life Sciences, Natural History Museum Cromwell Road, London, SW7 5BD, U.K
| | - Rogier E Hintzen
- Department of Life Sciences, Imperial College London, Silwood Park Buckhurst Road, Ascot, Berkshire, SL5 7PY, U.K ; Department of Life Sciences, Natural History Museum Cromwell Road, London, SW7 5BD, U.K
| | | | - Andy Purvis
- Department of Life Sciences, Imperial College London, Silwood Park Buckhurst Road, Ascot, Berkshire, SL5 7PY, U.K ; Department of Life Sciences, Natural History Museum Cromwell Road, London, SW7 5BD, U.K
| | - Lawrence N Hudson
- Department of Life Sciences, Natural History Museum Cromwell Road, London, SW7 5BD, U.K
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