QAECO’s favourite papers of 2018

An oldy but a goody. We asked our lab members to nominate papers published in 2018 that they enjoyed. We discussed these in a reading group session, and the nominations are listed here for you to explore as well. Happy reading!

Bron, Payal, Martin & David – reading group hosts

Gerry Ryan

O’Kelly HJ, Rowcliffe JM, Durant S, Milner-Gulland EJ (2018) Experimental estimation of snare detectability for robust threat monitoring. Ecology and Evolution 8: 1778 – 1785.
O’Kelly HJ, Rowcliffe JM, Durant S, Milner-Gulland EJ (2018) Robust estimation of snare prevalence within a tropical forest context using N-mixture models. Biological Conservation 217: 75 – 82.

This pair of papers look at snares — indiscriminate, cheap, pervasive, slow-and-painful death for victims; these things are nasty. They’re also difficult to find, and therefore to remove or understand. The papers explore and apply rigorous ecological monitoring techniques to understand how well rangers and researchers can find snares, target searching for them, and better understand the extent of the threat they pose. There’s also a map that won’t do much to improve Vietnam-Cambodia relations.

Jian Yen

Dietze M.C., et al. (2018) Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences USA 115: 1424–1432.

I stumbled across this paper after reading a book by the same author (Dietze 2017, Ecological Forecasting, Princeton University Press). I highly recommend both the paper and the book. The general premise is simple: we can improve ecology as a discipline by being more like weather forecasters. Make lots of predictions on short timescales, check if these predictions are any good, and update our models accordingly. The paper presents a discussion of ecological, technological, and social challenges that stand in the way of good ecological forecasts. To paraphrase the authors, the time to forecast is now.

Nick Golding

Smith AM, Niemeyer KE, Katz DS et al. (2018). Journal of Open Source Software (JOSS): design and first-year review. PeerJ Computer Science 4:e147

Software written by, and for, researchers is critical to modern science, particularly in quantitative ecology. But the people who write the software don’t get academic credit for their work, even if it enables someone else’s publication in a glamour journal. This paper describes a new journal JOSS, which aims to make it easier for people to get credit for their research software. JOSS also reinvents the review and publication process into a much quicker (usually around a month), cheaper ($6 per paper!) and more collaborative experience. As well as clearly outlining academia’s problem with unrecognised research software, this paper provides a glimpse into what the future of academic publishing could look like.

Saras Windecker

Pedersen EJ, Miller DL, Simpson GL, Ross N. 2018. Hierarchical generalized additive models: an introduction with mgcv. PeerJ Preprints 6:e27320v1

I’ve been trying to actively increase the breadth of statistical models I’m familiar with, to open up new ways of analysing complex data. To this end, I really enjoyed this paper by Eric Pederson and colleagues, because it presented complex models in a way a non-statistician could easily digest. The figures excellently illustrate key concepts such as how a GAM is constructed and how grouping levels can be modelled with different formulations of a hierarchical GAM. In addition, the authors do a fantastic job highlighting the model selection choices that need to be made without the aid of sophisticated tools — namely how knowledge of your system and clearly defined aims will often determine the ‘best’ model for your question.

Esti Palma

Pearson DE, Ortega YK, Eren Ö & Hierro JL (2018) Community assembly theory as a framework for biological invasions. Trends in Ecology & Evolution 33: 313-325.

Pearson and colleagues write about two of my passions: community assembly and plant invasions! They do a terrific job merging those two topics on this very clear and easy-to-follow paper. I especially love their net figures and how they highlight the key role of functional traits and human-driven introduction bias to understand invasive species’ success.

Matthew Rees

Legge, S. (2018). Searching for meaning in the interface between research and management. Pacific Conservation Biology24(3), 222-229.

Feeling disillusioned about the practicalities of researching external management programs in the space of my PhD, I made some tea and found distraction in this article. Legge hauled me away to all the wild landscapes where she has worked – places where “it smells of orange peel that has gone dry in the sun; its red rocks are polished by the sound of diamond doves and the feet of rock wallabies and ningbings”. Beautifully synthesising how individual pieces of research have fitted together to inform large-scale, multi-threat management—these musings hit me with some much-needed perspective. I often reread this piece during days like this; a pocket of optimism about where this career may take me.

Jutta Beher

Cinner J (2018) How behavioural science can help conservation. Science 362: 889-890

This paper is a great intro/overview of key cognitive biases and group-dynamic effects that can impact human interaction (e.g. planning and communicating conservation efforts) in ways that are counterproductive. Knowing about these effects and thinking about how to counteract or make use of them, is interesting, and hopefully helpful. It is written well (=easy to understand), and is quite short. I hope I can infect others with the interest to know more about these things with this paper! Also: It is the kind of paper I had envisioned to write, but then was talked into doing a lit review…

Jane Elith

Dormann CF, Bobrowski M, Dehlin DM et al (2018). Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecology and Biogeography, 27(9), 1004–1016.

This paper grew out of a meeting organised by the first and last authors, Carsten Dormann and Casper Kraan. Carsten is well known for his comprehensive papers tackling major issues in species modelling. I particularly enjoyed this for its advice to modellers using co-occurrence patterns to infer biotic interactions. Because it can be challenging to work out whether species associations are due to biotic interactions or other things, the authors detail ten questions that help guide modellers through a rigorous thought-process and analytical approach to help them make sound conclusions from their models.

Bronwyn Hradsky

Neilson EW, Avgar T, Burton AC, Broadley K, Boutin S (2018) Animal movement affects interpretation of occupancy models from camera-trap surveys of unmarked animals. Ecosphere 9: e02092

This paper uses a neat simulation model to tackle some questions that have been buzzing in the back of my brain for years: what does “occupancy” mean in a camera trapping context? How is it influenced by home range size and mobility?  Neilson et al. show that adjusting for imperfect detection can lead to biased estimates if animals have large home ranges. The direction of bias depends on the species’ movement speed (positive if the animals move quickly, negative if they move slowly). They emphasis that the interpretation of “occupancy” depends on the processes underlying the detection history, including movement and density (which may change between survey seasons or regions), and suggest the importance of exploring spatial and temporal correlations inherent in this type of data.  Although the authors don’t come up with definitive solutions to these problems, the subsequent citing papers also make interesting reading.

This entry was posted in Uncategorized. Bookmark the permalink.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s