Biology subjects

McCallum, I.

Publications and source records attributed to McCallum, I..

3 recordsLinked to original sources

bioRxiv2023

Biodiversity monitoring in Europe: user and policy needs

To implement the goals of the 2030 Global Biodiversity Framework, the European Biodiversity Strategy and the EU Green Deal, biodiversity monitoring is a pivotal instrument to achieve accountability and progress in conservation. Monitoring efforts in Europe, however, suffer from gaps and biases in taxonomy, spatial coverage, and temporal resolution, resulting in fragmented and disconnected data which does not provide sufficient evidence for policy making. To assess user and policy needs in biodiversity monitoring, we employed with EuropaBON a four-step user-centred stakeholder engagement process, including an international public stakeholder workshop, a standardised online survey, semi-structured interviews, and an expert meeting with representatives of EU member states, the European Commission and the European Environment Agency. The resulting insights into national and European biodiversity monitoring schemes identify policy needs, current challenges and potential solutions. Based on this in-depth policy and science stakeholder assessment, we recommend the establishment of a European Biodiversity Observation Network through a permanent Biodiversity Monitoring and Coordinating Centre to optimise existing observation efforts, harmonise data, and enhance our ability to predict and respond to key challenges related to biodiversity loss in a changing climate in Europe.

ecology↗

bioRxiv2023

Predicting the timing of ecological phenomena across regions using citizen science data

Spatial predictions of intra-annual ecological variation enhance ecological understanding and inform decision-making. Unfortunately, it is often challenging to use statistical or machine learning techniques to make such predictions, due to the scarcity of systematic, long-term observational data. Conversely, opportunistic time-stamped observation records, supported by highly informative data such as photographs, are increasingly available for diverse ecological phenomena in many regions. However, a general framework for predicting such phenomena using opportunistic data remains elusive. Here, we introduce a novel framework that leverages the concept of relative phenological niche to model observation records as a sample of temporal environmental conditions in which the represented ecological phenomenon occurs. We demonstrate its application using two distinct, management-relevant, ecological events: the emergence of the adult stage of the invasive Japanese beetle (Popillia japonica), and of fruiting bodies of the winter chanterelle mushroom (Craterellus tubaeformis). The framework accounts for spatial and temporal biases in observation data, and it contrasts the temporal environmental conditions (e.g., in temperature, precipitation, wind speed, etc.) associated with the observation of these events to those available in their occurrence locations. To discriminate between the two sets of conditions, we employ machine-learning algorithms (boosted regression trees and random forests). The proposed approach can accurately predict the temporal dynamics of ecological events across large geographical scales. Specifically, it successfully predicted the intra-annual timing of occurrence of adult Japanese beetles and of winter chanterelle mushrooms across Europe and North America. We further validate the approach by successfully predicting the timing of occurrence of adult Japanese beetles in Northern Italy, a recent hotspot of invasion in continental Europe, and the winter chanterelle mushroom in Denmark, a country with a high number of records of this mushroom. These results were also largely insensitive to temporal bias in recording effort. Our results highlight the potential of opportunistic observation data to predict the temporal variation of a wide range of ecological phenomena in near real-time. Furthermore, the conceptual and methodological framework is intuitive and easily applicable for the large number of ecologists already using machine-learning and statistical-based predictive approaches.

ecology↗

bioRxiv2018

Human-induced decrease of ectomycorrhizal vegetation led to loss in global soil carbon content

Vegetation impacts on ecosystem functioning are mediated by mycorrhiza, a plant-fungal association formed by most plant species. Ecosystems dominated by distinct mycorrhizal types differ strongly in their biogeochemistry. Quantitative analyses of mycorrhizal impacts on ecosystem functioning are hindered by the absence of information on mycorrhizal distribution. We present the first global high-resolution maps of vegetation biomass distribution among main types of mycorrhizal associations. Arbuscular, ecto-, ericoid and non-mycorrhizal vegetation store 241{+/-}15, 100{+/-}17, 7{+/-}1.8 and 29 {+/-} 5.5 GT carbon in aboveground biomass, respectively. Soil carbon stocks in both topsoil and subsoil are positively related to the biomass fraction of ectomycorrhizal plants in the community, though the strength of this relationship varies across biomes. We show that human-induced transformations of Earths ecosystems have reduced ectomycorrhizal vegetation, with potential knock-on effects on terrestrial carbon stocks. Our work provides a benchmark for spatially explicit global quantitative assessments of mycorrhizal impacts on ecosystem functioning and biogeochemical cycles.\n\nOne Sentence SummaryFirst maps of the global distribution of mycorrhizal plants reveal global losses of ectomycorrhizal vegetation, and quantitative links between mycorrhizal vegetation patterns and terrestrial carbon stocks.

ecology↗