Professional summary

Rob is a quantitative ecologist and methodologist. Lately, he has been developing methods to assess, mitigate and communicate the risk of bias in biodiversity monitoring. Species distribution modelling, mechanistic population modelling and causal inference also interest him.

Selected publications

Boyd Robin J. et al. , (2023), Descriptive inference using large, unrepresentative nonprobability samples: an introduction for ecologists. Ecology, http://dx.doi.org/10.1002/ecy.4214

Boyd Robin J. et al. , (2023), We need to talk about nonprobability samples. Trends in Ecology & Evolution, http://dx.doi.org/10.1016/j.tree.2023.01.001

Boyd Robin J. et al. , (2023), Causal inference and large‐scale expert validation shed light on the drivers of SDM accuracy and variance. Diversity and Distributions, 29, 774-784, http://dx.doi.org/10.1111/ddi.13698

Boyd Robin J. et al. , (2023), An operational workflow for producing periodic estimates of species occupancy at national scales. Biological Reviews, 98, 1492-1508, http://dx.doi.org/10.1111/brv.12961

Boyd Robin J. et al. , (2022), ROBITT: a tool for assessing the risk-of-bias in studies of temporal trends in ecology. Methods in Ecology and Evolution, https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13857

Boyd Robin J. et al. , (2022), Inferring trends in pollinator distributions across the Neotropics from publicly available data remains challenging despite mobilization efforts. Diversity and Distributions, 28, 1404-1415, http://dx.doi.org/10.1111/ddi.13551

Boyd Robin J. et al. , (2021), occAssess: an R package for assessing potential biases in species occurrence data. Ecology and Evolution, 11, 16177-16187, http://dx.doi.org/10.1002/ece3.8299

Boyd R.J. et al. , (2020), Simulating the summer feeding distribution of Northeast Atlantic mackerel with a mechanistic individual-based model. Progress in Oceanography, 183, http://dx.doi.org/10.1016/j.pocean.2020.102299

Boyd Robin et al. , (2020), Potential consequences of climate and management scenarios for the Northeast Atlantic mackerel fishery. Frontiers in Marine Science, 7, http://dx.doi.org/10.3389/fmars.2020.00639

Boyd Robin et al. , (2020), SEASIM-NEAM: a Spatially-Explicit Agent-based SIMulator of North East Atlantic Mackerel population dynamics. MethodsX, 7, http://dx.doi.org/10.1016/j.mex.2020.101044