Guiding and monitoring the efficiency of conservation efforts needs robust scientific background information, of which one key element is estimating wildlife abundance and its spatial and temporal variation. As raw counts are by nature incomplete counts of a population, correcting for detectability is required (Clobert, 1995; Turlure et al., 2018). This can be done with Capture-Mark-Recapture protocols (Iijima, 2020). Techniques for marking individuals are diverse, e.g. writing on butterfly wings, banding birds, or using natural specific patterns in the individual’s body such as leopard fur or whale tail. Advancement in technology opens new opportunities for developing marking techniques, including strategies to limit mark identification errors (Burchill & Pavlic, 2019), and for using active marks that can transmit data remotely or be read automatically.

The details of such methodological developments frequently remain unpublished, the method being briefly described in studies that use it. For a few years, there has been however a renewed interest in proper publishing of methods for ecology and evolution. This study by Folk & Mennerat (2023) fits in this context, offering a nice example of detailed description and testing of a method to mark salmon ectoparasites using RFID tags. Such tags are extremely small, yet easy to use, even with automatic recording procedure. The study provides a very good basis protocol that should help researchers working for small species, in particular invertebrates. The study is complemented by a video illustrating the placement of the tag so the reader who would like to replicate the procedure can get a very precise idea of it.

References

Burchill, A. T., & Pavlic, T. P. (2019). Dude, where’s my mark? Creating robust animal identification schemes informed by communication theory. Animal Behaviour, 154, 203–208. https://doi.org/10.1016/j.anbehav.2019.05.013

Clobert, J. (1995). Capture-recapture and evolutionary ecology: A difficult wedding ? Journal of Applied Statistics, 22(5–6), 989–1008.

Folk, A., & Mennerat, A. (2023). Methods for tagging an ectoparasite, the salmon louse Lepeophtheirus salmonis (p. 2023.08.31.555695). bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Ecology. https://doi.org/10.1101/2023.08.31.555695

Iijima, H. (2020). A Review of Wildlife Abundance Estimation Models: Comparison of Models for Correct Application. Mammal Study, 45(3), 177–188. https://doi.org/10.3106/ms2019-0082

Turlure, C., Pe’er, G., Baguette, M., & Schtickzelle, N. (2018). A simplified mark–release–recapture protocol to improve the cost effectiveness of repeated population size quantification. Methods in Ecology and Evolution, 9(3), 645–656. https://doi.org/10.1111/2041-210X.12900

Proposing efficient guidelines for biodiversity conservation often requires the use of forecasting tools. Species Distribution Models (SDM) are more and more used to predict how the distribution of a species will react to environmental change, including any large-scale management actions that could be implemented. Their use is also boosted by the increase of publicly available biodiversity databases[1]. The now famous aphorism by George Box "All models are wrong but some are useful"[2] very well summarizes that the outcome of a model must be adjusted to, and will depend on, the data that are used to parameterize it. The question of the reliability of using biodiversity databases to parameterize biodiversity models such as SDM –but the question would also apply to other kinds of biodiversity models, e.g. Population Viability Analysis models[3]– is key to determine the confidence that can be placed in model predictions. This point is often overlooked by some categories of biodiversity conservation stakeholders, in particular the fact that some data were collected using controlled protocols while others are opportunistic. 

In this study[4], the authors use a collection of databases covering a range of species as well as of geographic scales in France and using different data collection and validation approaches as a case study to evaluate the impact of data quality when performing Strategic Environmental Assessment (SEA). Among their conclusions, the fact that a large-scale database (what they call the “country” level) is necessary to reliably parameterize SDM. Besides this and other conclusions of their study, which are likely to be in part specific to their case study –unfortunately for its conservation, biodiversity is complex and varies a lot–, the merit of this work lies in the approach used to test the impact of data on model predictions.

References

1.  Feng, X. et al. A review of the heterogeneous landscape of biodiversity databases: Opportunities and challenges for a synthesized biodiversity knowledge base. Global Ecology and Biogeography 31, 1242–1260 (2022). https://doi.org/10.1111/geb.13497

2.  Box, G. E. P. Robustness in the Strategy of Scientific Model Building. in Robustness in Statistics (eds. Launer, R. L. & Wilkinson, G. N.) 201–236 (Academic Press, 1979). https://doi.org/10.1016/B978-0-12-438150-6.50018-2.

3.  Beissinger, S. R. & McCullough, D. R. Population Viability Analysis. (The University of Chicago Press, 2002).

4.  Ferraille, T., Kerbiriou, C., Bigard, C., Claireau, F. & Thompson, J. D. (2023) Integrating biodiversity assessments into local conservation planning: the importance of assessing suitable data sources. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community in Ecology.  https://doi.org/10.1101/2023.05.09.539999