Biodiversity Informatics: Building a Lifeboat for High Functionality Data to Decision Pipeline

Introduction: Species distribution models (SDMs) are often used to produce risk maps to guide conservation management and decision-making with regard to invasive alien species (IAS). However, gathering and harmonizing the required species occurrence and other spatial data, as well as identifying and coding a robust modeling framework for reproducible SDMs, requires expertise in both ecological data science and statistics.

Methods: We developed WiSDM, a semi-automated workflow to democratize the creation of open, reproducible, transparent, invasive alien species risk maps. To facilitate the production of IAS risk maps using WiSDM, we harmonized and openly published climate and land cover data to a 1 km² resolution with coverage for Europe. Our workflow mitigates spatial sampling bias, identifies highly correlated predictors, creates ensemble models to predict risk, and quantifies spatial autocorrelation. In addition, we present a novel application for assessing the transferability of the model by quantifying and visualizing the confidence of its predictions. All modeling steps, parameters, evaluation statistics, and other outputs are also automatically generated and are saved in a R markdown notebook file.

Results: Our workflow requires minimal input from the user to generate reproducible maps at 1 km² resolution for standard Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emission representative concentration pathway (RCP) scenarios. The confidence associated with the predicted risk for each 1km² pixel is also mapped, enabling the intuitive visualization and understanding of how the confidence of the model varies across space and RCP scenarios.

Discussion: Our workflow can readily be applied by end users with a basic knowledge of R, does not require expertise in species distribution modeling, and only requires an understanding of the ecological theory underlying species distributions. The risk maps generated by our repeatable workflow can be used to support IAS risk assessment and surveillance.

Plant and animal checklists, with conservation status information, are fundamental for conservation management. Historical field data, more recent data of digital origin and data-sharing platforms provide useful sources for collating species locality data. However, different biodiversity datasets have different formats and inconsistent naming systems. Additionally, most digital data sources do not provide an easy option for download by protected area. Further, data-entry-ready software is not readily available for conservation organization staff with limited technical skills to collate these heterogeneous data and create distribution maps and checklists for protected areas. The insights presented here are the outcome of conceptualizing a biodiversity information system for South African National Parks. We recognize that a fundamental requirement for achieving better standardization, sharing and use of biodiversity data for conservation is capacity building, internet connectivity, national institutional data management support and collaboration. We focus on some of the issues that need to be considered for capacity building, data standardization and data support. We outline the need for using taxonomic backbones and standardizing biodiversity data and the utility of data from the Global Biodiversity Information Facility and other available sources in this process. Additionally, we make recommendations for the fields needed in relational databases for collating species data that can be used to inform conservation decisions and outline steps that can be taken to enable easier collation of biodiversity data, using South Africa as a case study.