https://github.com/densitymodelling/dsmextra
Extrapolation assessments in density surface models
https://github.com/densitymodelling/dsmextra
abundance-estimation cetacean ecological-modelling extrapolation marine
Last synced: 4 months ago
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Extrapolation assessments in density surface models
- Host: GitHub
- URL: https://github.com/densitymodelling/dsmextra
- Owner: densitymodelling
- License: lgpl-3.0
- Created: 2019-09-12T07:57:46.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2025-04-18T16:16:44.000Z (about 1 year ago)
- Last Synced: 2025-04-19T05:21:55.758Z (about 1 year ago)
- Topics: abundance-estimation, cetacean, ecological-modelling, extrapolation, marine
- Language: R
- Size: 16.6 MB
- Stars: 5
- Watchers: 4
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Extrapolation tools for density surface models 
[](https://www.tidyverse.org/lifecycle/#maturing)
[](https://doi.org/10.5281/zenodo.3529465)
`dsmextra` provides a toolkit for quantifying and visualising extrapolation in spatially-explicit ecological models (with a focus on density surface models, as implemented in package [dsm](https://cran.r-project.org/web/packages/dsm/index.html)) projected into novel environmental space. Currently, `dsmextra` defines extrapolation on the basis of two metrics: **(1) ExDet** (Mesgaran et al. 2014), and **(2) %N** (the percentage of data nearby, King & Zeng 2007).
`dsmextra` offers a variety of numerical and graphical outputs, including summary plots and interactive maps created as [ggplot2](https://ggplot2.tidyverse.org/) and [html](https://rstudio.github.io/leaflet/) objects, respectively. Additional functionality (e.g. assessment methods for dynamic covariates) will be added in future releases.
The idea behind `dsmextra` is to aid ecologists, practitioners, and model end-users in identifying conditions (e.g. areas) under which predicted density surfaces may be prone to errors. In so doing, `dsmextra` may support:
+ Better-informed interpretations of (density surface) model outputs and their associated uncertainties.
+ Improvements to model development and covariate selection protocols.
+ Cost-effective allocation of future survey effort towards priority, data-poor areas.
### Getting started
If you are just getting started with `dsmextra`, we recommend reading the introductory ('Get started') tutorial [vignette](https://densitymodelling.github.io/dsmextra/articles/dsmextra.html), which provides a quick introduction to the package. You may also find the below paper and technical report useful:
* Bouchet et al. (2020). dsmextra: Extrapolation assessment tools for density surface models. Methods in Ecology and Evolution, 11(11): 1464-1469. DOI: [10.1111/2041-210X.13469](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13469)
* Bouchet et al. (2020). [From here and now to there and then: Practical recommendations for extrapolating cetacean density surface models to novel conditions](https://research-repository.st-andrews.ac.uk/bitstream/handle/10023/18509/Denmod_ExtrapolationReport_final_Aug2019.pdf?sequence=1&isAllowed=y). CREEM technical report 2019-01 v2.0, Centre for Research into Ecological & Environmental Modelling (CREEM), University of St Andrews, 59 p.
### Additional reading
* Mannocci et al. (2018). Assessing cetacean surveys throughout the mediterranean sea: A gap analysis in environmental space. *Scientific Reports* **8**, art3126. DOI: [10.1038/s41598-018-19842-9](https://www.nature.com/articles/s41598-018-19842-9).
* Mannocci et al. (2017). Extrapolating cetacean densities to quantitatively assess human impacts on populations in the high seas. *Conservation Biology* **31**, 601–614. DOI: [10.1111/cobi.12856](https://conbio.onlinelibrary.wiley.com/doi/full/10.1111/cobi.12856).
* Mesgaran et al. (2014). Here be dragons: A tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models. *Diversity and Distributions* **20**, 1147–1159. DOI: [10.1111/ddi.12209](https://onlinelibrary.wiley.com/doi/full/10.1111/ddi.12209).
* Miller et al. (2013). Spatial models for distance sampling data: Recent developments and future directions. *Methods in Ecology and Evolution* **4**, 1001–1010. DOI: [10.1111/2041-210X.12105](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12105).
* King G & Zeng L (2007). When can history be our guide? The pitfalls of counterfactual inference. *International Studies Quarterly* **51**, 183–210. DOI: [10.1111/j.1468-2478.2007.00445.x](https://doi.org/10.1111/j.1468-2478.2007.00445.x).
### Acknowledgements
This R package was developed for the [DenMod project](https://synergy.st-andrews.ac.uk/denmod/) (Working group for the advancement of marine species density surface modelling), and was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, being managed by the U.S. Navy’s Living Marine Resources program under Contract No. N39430-17-C-1982. The sperm whale data showcased in the [online vignette](https://densitymodelling.github.io/dsmextra/articles/dsmextra.html) were provided by Debi Palka (NOAA North East Fisheries Science Center) and Lance Garrison (NOAA South East Fisheries Science Center). Initial data processing was undertaken by Jason Roberts (Marine Geospatial Ecology Lab, Duke University).
### Installation
The latest development version can be installed from Github (requires the [remotes](https://github.com/r-lib/remotes) package):
```r
if (!require("remotes")) install.packages("remotes")
remotes::install_github("densitymodelling/dsmextra")
```
### Found a bug? Have a feature request?
Please submit an issue or send a pull request to the [Github repository](https://github.com/densitymodelling/dsmextra/).