Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

EU-Trees4F, a dataset on the future distribution of European tree species

We present "EU-Trees4F", a dataset of current and future potential distributions of 67 tree species in Europe at 10 km spatial resolution. We provide both climatically suitable future areas of occupancy and the future distribution expected under a scenario of natural dispersal for two emission scenarios (RCP 4.5 and RCP 8.5) and three time steps (2035, 2065, and 2095). Also, we provide a version of the dataset where tree ranges are limited by future land use. These data-driven projections were made using an ensemble species distribution model calibrated using EU-Forest, a comprehensive dataset of tree species occurrences for Europe, and driven by seven bioclimatic parameters derived from EURO-CORDEX regional climate model simulations, and two soil parameters. "EU-Trees4F", can benefit various research fields, including forestry, biodiversity, ecosystem services, and bio-economy. Possible applications include the calibration or benchmarking of dynamic vegetation models, or informing forest adaptation strategies based on assisted tree migration. Given the multiple European policy initiatives related to forests, this dataset represents a timely and valuable resource to support policymaking.

Known unknowns: Filling the gaps in scientific knowledge production in the Caatinga

The Caatinga is an ecologically unique semi-arid region of northeast Brazil characterized by high levels of endemism and severe anthropogenic threats from agricultural development and climate change. It is also one of the least known biomes in Brazil due to a combination of inadequate investment, low regional research capacity and difficult working conditions. However, while the lack of scientific knowledge of the Caatinga is well known, the spatial and temporal distribution of knowledge production has not been investigated. This is important because such biases undermine the development of effective conservation policy and practice and increase the uncertainty associated with conservation actions. Here, we map the geography of conservation knowledge production in the Caatinga and use an innovative hurdle model to identify the presumptive factors driving these patterns. Our analysis revealed strong geographic patterns, with research sites concentrated in the east of the region and in areas close to roads and research centres. There was also a positive association between conservation knowledge production and risk of desertification, indicating that conservation scientists are responding to conservation challenges faced by Caatinga’s fauna and flora arising from climate change. Our results also highlight the pivotal role of pioneer scientists (those who develop research sites in previously unstudied/understudied areas) in determining the future geographic patterns of knowledge production. We conclude our article with a brief discussion of potential policies for increasing the spatial representativeness of conservation research in this remarkable ecosystem.

The DIARS toolbox: a spatially explicit approach to monitor alien plant invasions through remote sensing

The synergies between remote sensing technologies and ecological research have opened new avenues for the study of alien plant invasions worldwide. Such scientific advances have greatly improved our capacity to issue warnings, develop early-response systems and assess the impacts of alien plant invasions on biodiversity and ecosystem functioning. Hitherto, practical applications of remote sensing approaches to support nature conservation actions are lagging far behind scientific advances. Yet, for some of these technologies, knowledge transfer is difficult due to the complexity of the different data handling procedures and the huge amounts of data it involves per spatial unit.

In this context, the next logical step is to develop clear guidelines for the application of remote sensing data to monitor and assess the impacts of alien plant invasions, that enable scientists, landscape managers and policy makers to fully exploit the tools which are currently available. It is desirable to have such guidelines accompanied by freely available remote sensing data and generated in a free and open source environment that increases the availability and affordability of these new technologies.

Here we present a toolbox that provides an easy-to-use, flexible, transparent and open source set of tools to sample, map, model and assess the impact of alien plant invasions using two high-resolution remote sensing products (hyperspectral and LiDAR images). This online toolbox includes a real case dataset designed to facilitate testing and training in any computer system and processing capacity.