Biology subjects

Cameron, E.

Publications and source records attributed to Cameron, E..

2 recordsLinked to original sources

bioRxiv2019

Global distribution of earthworm diversity

Soil organisms provide crucial ecosystem services that support human life. However, little is known about their diversity, distribution, and the threats affecting them. Here, we compiled a global dataset of sampled earthworm communities from over 7000 sites in 56 countries to predict patterns in earthworm diversity, abundance, and biomass. We identify the environmental drivers shaping these patterns. Local species richness and abundance typically peaked at higher latitudes, while biomass peaked in the tropics, patterns opposite to those observed in aboveground organisms. Similar to many aboveground taxa, climate variables were more important in shaping earthworm communities than soil properties or habitat cover. These findings highlight that, while the environmental drivers are similar, conservation strategies to conserve aboveground biodiversity might not be appropriate for earthworm diversity, especially in a changing climate.\n\nOne sentence summaryGlobal patterns of earthworm diversity, abundance and biomass are driven by climate but patterns differ from many aboveground taxa.

ecology↗

bioRxiv2019

Model ensembles with different response variables for base and meta models: malaria disaggregation regression combining prevalence and incidence data

Maps of infection risk are a vital tool for the elimination of malaria. Routine surveillance data of malaria case counts, often aggregated over administrative regions, is becoming more widely available and can better measure low malaria risk than prevalence surveys. However, aggregation of case counts over large, heterogeneous areas means that these data are often underpowered for learning relationships between the environment and malaria risk. A model that combines point surveys and aggregated surveillance data could have the benefits of both but must be able to account for the fact that these two data types are different malariometric units. Here, we train multiple machine learning models on point surveys and then combine the predictions from these with a geostatistical disaggregation model that uses routine surveillance data. We find that, in tests using data from Colombia and Madagascar, using a disaggregation regression model to combine predictions from machine learning models trained on point surveys improves model accuracy relative to using the environmental covariates directly.

epidemiology↗