If you’re anything like me, you might experience a minor existential crisis weekly. As scientists we question the world around us and, for me, this questioning turns all-too-often inwards to my career. I don’t think that’s unusual: ask any scientist about their ‘Plan B’, and the extent to which it’s thought through is often astonishing (if a café-cum-cocktail bar ever opens in Glasgow’s West End, which specialises in drinks that employ spice blends from around the world and are named after old spice trade routes and trading vessels, then you know I’ve jumped the science ship).
Contributing open-source software is something which has made my work feel a bit more relevant and helped me feel a bit less of an imposter. I’ll explain why that is, give some tips to beginners for building a first R package, and hopefully persuade other (especially early-career) researchers to do the same. Continue reading →
If there is one question I hear over and over again, it’s this: “why, oh why, do you use satellite data instead of ground-based data in your research?” People seem to think that I believe satellite data are better than ground-based data. Do I not value fieldwork? Do I not trust ground-based data? My answer to all of this is: you’ll never catch me preaching that satellite remote sensing can solve the entire data collection gap in ecological monitoring.
Yes, satellite-based techniques can address spatial and temporal domains inaccessible to traditional, on-the-ground, approaches, but I am the first to acknowledge that satellite remote sensing cannot match the accuracy, precision and thematic richness of in-situ measurement and monitoring.
In spite of this, data collected on the ground are currently difficult to use for mapping and predicting regional or global changes in the spatio-temporal distribution of biodiversity (a problem for those of us trying to tackle these kinds of issues). Ground-based data can also be expensive and tend to come from a single annual time period. This makes it difficult to gather information on temporal changes and phenology. Continue reading →
Mark is a statistician with Biomathematics & Statistics Scotland, based in Aberdeen. His main statistical research interests are Species Distribution Modelling, Compositional Data Analysis, Bayesian Mixture Modelling and Bayesian Ordinal Regression. Mark was one of the presenters at the UK half of the Methods in Ecology and Evolution 5th Anniversary Symposium in April. You can watch his talk, ‘Model Selection and the Cult of AIC’ here.
The level of statistical analysis in ecology journals is far higher than in most other disciplines. Ecological journals lead the way in the development of statistical methodology, necessitated by challenging practical problems involving complex data sets. As a statistician, publishing also in hydrology, soil science, social science and forensic science journals, I’ve found papers in those areas are much more likely to only use well-established methods than papers in ecology.
Here’s the big question though: why then do I have the most difficulty with ecological journals when it comes to statistical analyses? Let’s be clear here: when I say “difficulty”, I mean I receive reviews which are just plain wrong. Most statisticians I’ve spoken to who work in ecology have anecdotes from reviews which demonstrate a lack of understanding by the non-statistician reviewer (including the all-too-frequent “perhaps you should consult a statistician”). So, why the apparent disconnect?
The difference seems to be in how non-statisticians in different disciplines treat the statistics in a paper. In many subject areas, reviewers are almost deferential to the statistical analysis; in ecology, reviewers can be forthright in their condemnation, often without justification. Reviewers have every right to question the statistical analysis in a paper, but the authors have the exact same right to expect a high quality review from a genuine expert in the field. Has ecology become blasé about statistics? Continue reading →