We asked everyone to nominate their favourite paper of 2013, and here’s what they said. What we’ve learnt from this exercise is that as a group we have diverse interests, but particularly enjoy papers published in Methods in Ecology and Evolution and Science (three papers each), aren’t afraid to give shout-outs for our colleagues’ work, and also take notice of work that has been blogged about – so science blogging pays off! Enjoy, and Happy New Year from everyone at QAECO.
Merrow, C., Smith, M. J. and Silander, J. A. Jr. (2013) A practical guide to MaxEnt for modelling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1-12.
In this paper the authors list, explain and discuss the multiple decisions that a modeller must face when using MaxEnt to fit and evaluate species distribution models. Most importantly, the authors highlight the implications that different decisions in selecting settings–together with data quality- can have for model outputs and the inference drawn from them. As its title indicates, it is a good practical guide for both new and already-experienced MaxEnt users.
The use of “offsets” to compensate for anticipated loss of habitat or biodiversity is a vexing issue. It has considerable common sense appeal at face value, but in implementation it can and has been shown to be vulnerable to exploitation. Some fine recent work of Maron et al from 2012 set out how to think about the problem, and is one good exposition of how and why the implementation is flawed. Unhappily, in my experience, good arguments can be stepped around or even discounted in a way that a solid empirical treatment such as Curran et al put together here can not, and for that reason I was really pleased to see this work emerge.
This one is from very close to home. Making visual estimates for quantities in the field, such as the project foliage cover of a particular species or life-form group, is one of the reasons why I find learning to program and write code relaxing and enjoyable! I am partial to exploration of the difficulties of doing visual estimation well, and ways to mitigate the likely errors particularly when a team of fieldworkers is required. This work suggests to me that some intensive coaching should pay off for field campaigns, for novices and seasoned experts alike.
Els van Burm
Lahoz-Monfort, J. J., Harris, M. P., Morgan, B. J. T., Freeman, S. N. and Wanless, S. (in press). Exploring the consequences of reducing survey effort for detecting individual and temporal variability in survival. Journal of Applied Ecology.
I like this paper as it’s related to my PhD topic of how much data we need to make a reliable decision in environmental management. José’s paper is more focused on how much survey effort is needed to detect trends or ecological effects, but I can really identify with research that is trying to make things as cost-effective as possible.
Clarin, B.-M., Bitzilekis, E., Siemers, B. M. and Goerlitz, H. R. (in press) Personal messages reduce vandalism and theft of unattended scientific equipment. Methods in Ecology and Evolution.
My favourite paper for 2013 was one that took a clever scientific approach to a common but under-recognized issue in ecological study: vandalism of field gear. Blog on it by the journal here.
It’s more of an opinion piece, but I like this because it’s got a punchy name and I think it’s something that we’ve probably all worried/thought about. Also it has a nice EBB and flow article about it here.
Neat study that explores the role of experience, social learning and genetic factors on migratory performance (measured as deviations from a straight-line migratory path). The authors demonstrate importance of social learning for young birds, which deviated less from a migratory path when migrating with older, more experienced birds.
Sushinsky, J. R, Rhodes, J. R., Possingham, H. P., Gill, T. K. and Fuller, R. A. (2013) How should we grow cities to minimize their biodiversity impacts? Global Change Biology 19, 401–410.
A very interesting paper from our colleagues at UQ addressing one of the big questions in urban ecology – is urban sprawl or increased urban density better for biodiversity?
Addison, P. F. E., Rumpff, L., Bau, S. S., Carey, J. M., Chee, Y. E., Jarrad, F. C., McBride, M. F. and Burgman, M. A. (2013) Practical solutions for making models indispensable in conservation decision-making. Diversity and Distributions 19, 490-502.
Interested in doing research that’s useful to conservation decision makers? Then read this paper. The authors summarise common objections to using models in environmental decision making. They make a strong case for using a structured decision making framework to address these objections, and highlight the importance of participatory decision making. After reading this paper I thought “Why isn’t everyone doing this?”.
In this paper the authors develop a model that predicts interactions between species pairs using the host breadth and phylogenetic similarity. With this model they were able to forecast novel interactions between native herbivores and introduced plant species. I like this work, because the model can be practically applied various fields. It can predict herbivores reactions to new crops or invasive weeds, screen biocontrol agents for possible non-target impacts or anticipate the risk that arises from conservation translocations.
Ogle, K., Pathikonda, S., Sartor, K., Lichstein, J.W., Osnas, J. L. D. & Pacala, S.W. (2014) A model-based meta-analysis for estimating species-specific wood density and identifying potential sources of variation. Journal of Ecology 102, 194-208.
This paper interests me because it is about functional traits (wood density in trees), meta-analyses and is Bayesian. The impressive aspects are the model-based analysis that involves a rigorous attribution of variation and uncertainty. An interesting feature for those of us who seek to make inferences from sparse data matrices is the modularisation that restricts the flow of information within certain parts of the model so as to only estimate (covariate) parameters from data and to not be driven by sample means. This feature appears important when faced with large amounts of missing data that may result from generating databases from the literature. See also: Ogle, K., Barber, J. and Sartor, K. (2013) Feedback and modularization in a Bayesian meta–analysis of tree traits affecting forest dynamics. Bayesian Analysis 8, 133–168.
As someone with an interest in pollination and the land sparing/land sharing debate, I really enjoyed this paper: There has been much hype surrounding the collapse of honey bees in some areas of the world, and concern about what this means for food security. However, this paper presents conclusive empirical evidence that across all inhabited continents, wild pollinators are much more effective at pollinating the crops we rely on to feed us than honey bees. Therefore, from a production perspective it will be more beneficial to accommodate wild pollinators in farming landscapes by setting aside semi-natural vegetation, rather than trucking in honey bees.
The latter one is a little bit methody, but it is a good coverage of species accumulation curve methods and links to Shannon entropy.
Converse, S. J., Moore, C. T., Folk, M .J. and Runge, M.C. (2013). A matter of tradeoffs: reintroduction as a multiple objective decision. Journal of Wildlife Management 77, 1145-1156.
In reintroduction biology, as in many other conservation fields, we should really think hard about where we want to go before science can tell us how to get there. This paper highlights the need to consider all the objectives in reintroduction programs, and shows how we can use multi-criteria decision analysis to address them.