How citizen science could improve species distribution models and their independent assessment

  1. Florence Matutini
  2. Jacques Baudry
  3. Guillaume Pain
  4. Morgane Sineau
  5. Joséphine Pithon

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Abstract

  • Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.

  • We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.

  • Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.

  • Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer's participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

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    1. Version published to 10.1002/ece3.7210
    2. Peer Community in Ecology

      Citizen science is becoming an important piece for the acquisition of scientific knowledge in the fields of natural sciences, and particularly in the inventory and monitoring of biodiversity (McKinley et al. 2017). The information generated with the collaboration of citizens has an evident importance in conservation, by providing information on the state of populations and habitats, helping in mitigation and restoration actions, and very importantly contributing to involve society in conservation (Brown and Williams 2019). An obvious advantage of these initiatives is the ability to mobilize human resources on a large territorial scale and in the medium term, which would otherwise be difficult to finance. The resulting increasing information then can be processed with advanced computational techniques (Hochachka et al 2012; Kelling et al. 2015), thus improving our interpretation of the distribution of species. Specifically, the ability to obtain information on a large territorial scale can be integrated into studies based on Species Distribution Models SDMs. One of the common problems with SDMs is that they often work from species occurrences that have been opportunistically recorded, either by professionals or amateurs. A great challenge for data obtained from non-professional citizens, however, remains to ensure its standardization and quality (Kosmala et al. 2016). This requires a clear and effective design, solid volunteer training, and a high level of coordination that turns out to be complex (Brown and Williams 2019). Finally, it is essential to perform a quality validation following scientifically recognized standards, since they are often conditioned by errors and biases in obtaining information (Bird et al. 2014). There are two basic approaches to obtain the necessary data for this validation: getting it from an external source (external validation), or allocating a part of the database itself (internal validation or cross-validation) to this function.
      Matutini et al. (2020) in his work 'How citizen science could improve Species Distribution Models and their independent assessment' shows a novel application of the data generated by a citizen science initiative ('Un Dragon dans mon Jardin') by providing an external source for the validation of SDMs, as a tool to construct habitat suitability maps for nine species of amphibians in western France. Importantly, 'Un Dragon dans mon Jardin' contains standardized presence-absence data, the approximation recognized as the most robust (Guisan, et al. 2017). The SDMs to be validated, in turn, were based on opportunistic information obtained by citizens and professionals. The result shows the usefulness of this external data source by minimizing the overestimation of model accuracy that is obtained with cross-validation with the internal evaluation dataset. It also shows the importance of properly filtering the information obtained by citizens by determining the threshold of sampling effort.
      The destiny of citizen science is to be integrated into the complex world of science. Supported by the increasing level of the formation of society, it is becoming a fundamental piece in the scientific system dedicated to the study of biodiversity and its conservation. After funding for scientists specialized in the recognition of biodiversity has been cut back, we are seeing a transformation of the activity of these scientists towards the design, coordination, training and verification of programs for the acquisition of field information obtained by citizens. A main goal is that a substantial part of this information will eventually get integrated into the scientific system, and rigorous verification process a fundamental element for such purpose, as shown by Matutini et al. (2020) work.

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    3. Version published to 10.1101/2020.06.02.129536 on bioRxiv