Hawkins, B.A., 2012. Eight (and a half) deadly sins of spatial analysis. Journal of Biogeography, 39(1), pp.1–9.
Hawkins argues that many ecologists are of the understanding that the existence of spatial autocorrelation is a bias or artifact of sampling that must be removed rather than embraced when trying to model their distribution. In this opinion essay, he argues against this notion as well as against eight (and a half) other commonly held perceptions in ecology. First, spatial autocorrelation is not bias. Instead this autocorrelation in nature is what we wish to understand. Indeed, spatial autocorrelation in nature is separate from residuals in a model. Second, spatial regression is not always the best according to Hawkins and some statisticians. In the Beale (2010) paper that we read, we did see that OLS produced estimates that were not all that different from spatial models and here, Hawkins notes that even when studies find that spatially explicit model are best arise in simulation studies of species rather than using real data. Thus, muddying the waters for making generalized claims about how spatial models are always better. Third is the assumption of stationarity among the entire landscape. Authors often fail to report whether or not they have determined whether their data abides by this assumption. Fourth, Hawkins argues that partial regression coefficients are not very meaningful for these contexts. The main idea of this section is just that we can’t quibble over which particular type of multiple regression, and values of coefficients, is the best method for understanding a process. Fifth, was correlation does not equal causation and argues that although many ecologists have heard this mantra, some still don’t uphold the value of the statements. Sixth was the idea that species richness causes bias. Hawkins emphasizes that if the species is prevalent in an area, we should not wish to remove this phenomenon from our data. Usually, he states, this type of misconception is due to a confusion of precision with bias. Next, he argues that spatial processes explain spatial patterns (i.e. there are biological processes operating in a spatially structured environment that we wish to understand). Finally, he notes that spatial autocorrelation causes red shifts in regression models. This means that there is an over-estimation of importance of broad scale predictors in OLS multiple regression models. He argues that this point is not important at all. Take away points from this paper include: (1) that we should not try too much to focus on methodology when describing species distributions. If we do this then we run the risk of trying to capture all complexity in our data rather than understanding it. (2) Many of the disagreements among researchers using multiple regression tools stem from the lack of understanding of the assumptions that these models make. Although I agree with his findings about understanding assumptions and autocorrelation for multiple regression models, I find it difficult to validate his opinions on all matters of these subjects because he lacks citations backing up his claims.