Virtual #ISEC2020

Held every two years, the International Statistical Ecology Conference has shifted Down Under this year. But given COVID-19, it is running virtually. And it has just kicked off, and the organisers are to be commended for what appears to be an extremely well-run event.

Various QAECO people are presenting – here is the list of talks (times are AEST):

Name: Tianxiao (August) Hao
Talk title: Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models
Location: #acacia
Day: Tuesday
Time: 19:45

Name: Gurutzeta Guillera-Arroita (@GGuillera)
Talk title: Can occupancy dynamics models improve predictions of species’ range dynamics? A test using Swiss birds.
Location: #banksia
Day: Tuesday
Time: 20:45

Name: Ian Flint
Talk title: Point process models to account for interactions across different ranges in joint species distribution models
Location: #banksia
Day: Wednesday
Time: 8:45 am

Name: Matthew Rees (@matt_w_rees)
Talk title: Using spatial mark-resight and generalized additive models to infer predator interactions
Location: #darwinia
Day: Wednesday
Time: 09:00

Name: Michael McCarthy (@mickresearch)
Talk title: Statistics in Biology Textbooks
Location: #displayhall2 – Speed talks and posters
Day: Thursday
Time: 08:00

Name: Gerry Ryan (@silverlangur)
Talk title: How much modelling is enough to manage biodiversity in your forests, should you bother with population viability analysis, and should you use new PVA tool STEPS to do that bothering?
Location: #banksia
Day: Thursday
Time: 09:15

Name: Casey Visintin
Talk title: steps: open-source software for spatially- and temporally-explicit population simulations
Location: #darwinia
Day: Thursday
Time: 10:00

Name: Saras Windecker
Talk title: Integrating citizen science records, abundance estimates, and scat survey data in a species distribution model
Location: #banksia
Day: Thursday
Time: 18:45

Name: Kathryn Knights
Talk title: Efficient effort allocation in line-transect distance sampling of high-density species: when to walk further, measure less-often and gain precision
Location: #darwinia
Day: Thursday
Time: 18:45

Name: Kevin D Newman (@kdnewman87)
Talk title: Optimal Surveillance Methods
Location: #displayhall1
Day: Thursday
Time: 19:00

Name: Roozbeh Valavi
Talk title: Block cross-validation for species distribution modelling: introducing the blockCV package
Location: #banksia
Day: Thursday
Time: 20:00

Name: Emily McColl-Gausden (@EcoEmcg)
Talk title: The power of one (or many): A comparison between single and multi-species eDNA detection methods using site occupancy-detection models
Location: #acacia
Day: Thursday
Time: 20:30

Name: David Wilkinson
Talk title: Defining and evaluating the predictions of joint species distribution models
Location: #banksia
Day: Friday
Time: 09:00

Name: Rebecca Groenewegen (@bEcologist)
Talk title: An application of spatial count models to assess invasive predator management
Location: #corymbia
Day: Friday
Time: 09:15

 

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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.
and
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.

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What is HPC and why would I use it?

—————————————————————————————————————————————-Given the recent focus of the coding club sessions on all things boab and HPC, we thought a post on what-the-H is HPC would do us all some good. So, read on if you’re curious, confused or confident. There’s something in it for everyone.
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Do you dread the word big data? Does parallel computing make your palms sweaty? For those of you trying to convince yourselves you’re braver than that, let’s see how you roll with…high performance computing, servers, slurm! Yikes!

scream

You are not alone. Almost everyone in QAECO and the School of Biosciences is grappling with these very fears.

So, what is so scary? We’re all scientists after all and are trained to take on the new and beat it into submission with experiments and theory. Well for a start, we don’t understand the terminology used in this space and find it difficult to articulate what we want to do with it.“It” is the great unknown, which makes it scary.

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Kickstarting the year with HGAMs

To start off Reading Group for 2019, we dove headfirst into 43 brilliant pages on hierarchical generalised additive models (HGAMs) by Pederson et al. 2018, a pre-print titled Hierarchical generalised additive models: an introduction with mgcv. Although at first intimidated by the subject matter and length of the reading, we found that the paper presented rather complicated statistical models in a series of extremely approachable explanations and R code. Talk about speaking our language! So, what are HGAMs, when might we use them instead of other models such as nonlinear models, and what else did we take away from this reading?

IMG_4423 2

“And this is what a GAM is!” Saras’ happy scribbling during reading group.

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Philosophical discussions in the lab: Žižek criticises ideological ecology

In a recent reading group, QAECO discussed a criticism of ecology from the contemporary philosopher Slavoj Žižek, as represented in two online resources:
http://www.nytimes.com/2010/12/02/opinion/global/02iht-GA12zizek.html
https://io9.gizmodo.com/5627925/slavoj-iek-wake-up-and-smell-the-apocalypse?IR=T

Slavoj Žižek [photo by By Amrei-Marie, from Wikimedia Commons]

Žižek is a strongly left-leaning critic of societal issues, who thinks many of society’s problems stem from an era of ideology. Žižek views ecology as one such ideology, believing that ecology “takes real problems and mystifies them”. He contends that ideologies provide an escape from real societal challenges and are a hindrance to true social progression. What makes him view ecology in such a light?

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Wanting to learn about Species Distribution Modelling? Consider enrolling in our online subject! – running again soon (Jul/Oct 2018)

Are you interested in modelling? Are you a graduate student, and your project involves studying species distributions? Or maybe you are a research professional or a manager wanting to expand your quantitative skills?

Species distribution modelling is one of the most highly cited areas of ecological research. And it is not just about research; species distribution models are also very useful for supporting a wide range of environmental decisions. So why not learn more about them?

At the University of Melbourne, we run a graduate subject on Species Distribution Modelling, delivered entirely online. The subject is offered to externals through the university’s Community Access Program (CAP). Through this program, you may choose to study in either assessed or non-assessed mode (costs are 3696AUD for non-assessed mode; 4612 for assessed mode). The subject runs this year from 23 July to 21 October (final assessments due around mid Nov).

The subject covers species distribution modelling from two different angles, ecophysiological models and correlative models (GLMs, Maxent, BRTs…), and consists of video lectures and guided computer practicals in R. The content emphasises an understanding of the problem, the data, and the model, and provides practical skills in fitting the models. The subject team includes Mike Kearney and Jane Elith, two internationally recognised experts in the field!

Tempted? Get in touch if you are; we will be happy to answer your queries. Please email José, the subject coordinator (jlahoz@unimelb.edu.au).

For information about the subject and the CAP program see:

https://handbook.unimelb.edu.au/2018/subjects/evsc90026

http://students.unimelb.edu.au/achieve/community-access-program

https://futurestudents.unimelb.edu.au/admissions/fees/CAP

Please spread the word through your networks!! Thanks!

The SDM subject team,

Mike, Jane, Guru and José

José Lahoz-Monfort

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Enquiries about the application process should be directed to ask.unimelb.edu.au or the 13MELB phone line.

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Grappling with reproducibility in science?

As a lab, we’ve made it a priority to increase the standards of our code to align with best practices for reproducibility and repeatability of our science. In keeping with this goal, this week in reading group Saras Windecker and Hannah Fraser lead a discussion on the British Ecological Society’s Guide to Reproducible Code in Ecology and Evolution, measures on how we can implement these guidelines in our research and the barriers that limit their uptake within the QAEco group. Here is a brief summary of that discussion.

The BES guide details workflow and project structure, version control, and techniques for defensive coding. While many of us already implement all or some of these suggestions into our workflow, it is by no means universal. Moreover, the QAEco group has a steady intake of new students, for whom these skills are, for the most part, completely new.

Whether a beginner or a veteran at maintaining ‘good’ workflows, we found over the course of the discussion that there are two major barriers to the uptake of reproducibility recommendations: lack of availability of resources/time, and fear/sense of intimidation to taking the plunge in to the great unknown of workflows, version control and GitHub!

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Qaeco’s favourite papers of 2017

We asked our lab members to nominate a paper published in 2017 that they had enjoyed. Recommendations ranged from the skill-based (scientific writing, reproducible coding, camera-trapping) to global reviews (plant traits, climate change, size-based models) and some great case studies (questionable psychologists, waterbirds at Lake Eyre, Finnish foxes).  We hope you find them as interesting as we did!

– Kate & Bron, hosts of QAECO Reading Group


Natasha Cadenhead

Doubleday, Z.A. & Connell, S.D. (2017) Publishing with objective charisma: breaking science’s paradoxTrends in Ecology & Evolution32, 803-805.

At the risk of sounding a little too Eat, Pray, Love, 2017 was a year in which I decided to try and actively improve my scientific writing. Not only is there an inherent joy to reading a well-written paper, but it makes the scientific message within that much clearer. Doubleday & Connell’s (thankfully, eloquent) paper recognises the problem with much of scientific writing – we spend a lot of our time doing it, but we’re never taught how to do it in an engaging way. They run through some steps for bringing out that charismatic “Ingredient X” in your writing, without falling prey to sensationalism. On this topic (although less science-focused) I would also recommend anything by the excellent Helen Sword.

Dave Duncan

Watson, D.M., Doerr, V.A.J., Banks, S.C., Driscoll, D.A., van der Ree, R., Doerr, E.D. & Sunnucks, P. (2017) Monitoring ecological consequences of efforts to restore landscape-scale connectivity. Biological Conservation, 206, 201–209.

My favourite paper from this year is the culmination of a monitoring framework I saw presented by one of the authors back in about 2015.  The article sets out a framework for thinking about the design of monitoring activities for projects whose intention is to restore connectivity at landscape scales.

Now, you might be saying to yourself: does the world really need another monitoring framework? Sadly, apparently yes. Well that and some kind of institutional revolution on the other side of which we actually implement some of these great ideas.

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Modellers v.s. Experimentalists – why can’t we all just get along?

Are modellers trying to steal your data?

Field ecologists not bothering to read your equations?

If so, you’re not alone, because the authors in Heuschele et al 2017 share your concern. They reckon that ecological research is being limited by a lack of communication and collaboration between modellers and experimentalists.

QAECO is made up of a diverse bunch of researchers, who use many different approaches to skin a cat. So this week in reading group, I (Matt Rees) bought this paper in to see what everyone thought. Turns out, quite a bit.

The authors of this paper conducted an online survey of ecologists, a bibliometric analysis of highly cited papers, and examined the background of highly cited ecologists. In doing so, they identified two key aspects that seem to be preventing collaboration between modellers and experimentalists: journal articles being written in “cryptic” ways that make it difficult for their counterparts to decipher, as well as a lack of data being exchanged. They showed that the recipe for being a highly cited paper/author, was to model, or use a combination of experimental and modelling approaches (but not just experimental).

Despite a couple people not being convinced by their review methods (and they didn’t use any modelling… tsk tsk), these concerns did seem to ring true in our discussion. The majority of experimentalists they surveyed were keen to share their data, however, had reservations about modellers using their data inappropriately (i.e. not recognising the limitations of their methods) and wanted appropriate acknowledgement. These results prompted a debate amongst us about what constitutes authorship, should collecting the data automatically mean you are a co-author? Unsurprisingly, our modelling folks tended not to think so, and generally those who have conducted experiments did. However, we all agreed that it was context specific and that those who collected the data should at least get an opportunity to contribute to the manuscript. Regardless, it was clear that like the authors, we all agreed that collaboration and sharing data is essential to make sense of the complex systems we study. Although, this shouldn’t just be a one-way street of experimentalists handing over their data without apprehension, modellers should also seek to collaborate in the experimental design process.

IMG_7292

Just a standard day for QAECO experimentalist Jessie Moyses

But hang on a second, what the blooming heck is an experimentalist and who is a modeller? We were a little confused with their definitions (for example, they excluded statistical modelling from the definition of being a modeller, yet classified papers using statistical models of non-manipulative experiments as modelling papers in their bibliometric analysis). We also felt that this paper tended to reinforce the stereotypes of being either a modeller or an experimentalist. These labels can stick very strongly in science and can dictate what you work on in the future. In some sections, the paper seemed discouraging of using both approaches – something that many of us in QAECO strive to do.

A particular concern we found from this research was that experimentalists do not seem to be drawing inspiration from modelling papers. Surveyed experimentalists stated that these papers were difficult to understand and that they were sceptical about the model being a realistic representation of the system. A key recommendation the authors put forward was to increase mathematical teaching in ecology, which is of course, well justified and often called for. However, a few of us also thought that there is more that modellers could be doing to bridge this gap that didn’t get mentioned in the paper (and rarely does). For example, modellers could head into the field to see how this data is being collected, talk to land managers, communicate their findings directly to experimentalists that would be interested (especially if your highlighting an information gap), encourage them to test your model and collect their data in a way that would increase the modelling opportunities.

IMG_7308

The famous supermodel Jian Yen

This article promotes an important discussion about how we can improve our field. We feel very lucky at QAECO to have such a wide  spectrum of experimental-modelling approaches being used under one roof. It is clear that increasing collaboration and communication in ecology needs to be better encouraged, luckily there are feasible steps we can all take to bridge this gap (which are probably much easier than catching thousands of bandicoots or deriving new algorithms).

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Sense of place: the ecosystem service to align social and conservation values?

Many conservation issues are influenced by a complex mix of environmental, social, economic and cultural processes. At times, conservation decision-making can be complicated by opposing social and ecological values. In this week’s reading group, Anja Skroblin led a discussion on “sense of place”, focused on a paper by Hausmann et al. (2015).

The authors suggest that recognising the human concept of “sense of place” as an ecosystem service is an important link to help to resolve conflicts where conservation is at odds with human development needs. The authors of the paper develop a framework for how “sense of place” can be used to inform conservation decision making to benefit human well-being and biodiversity conservation in a seemingly win-win situation.

The Kimberleys

But what is “sense of place”?

The definition differs across the psychological, sociological, geographical and environmental management disciplines. We felt it was best summarised by the attachment and connections that people develop with, and the meanings they assign to, places.

Each of us could describe a location for which we had sense of place. Our places were all nature-based, perhaps because we were all ecologists with an outdoorsy bent: the bushland patches we played in as kids, the parks where we went camping with our families, the Otways, the Prom, the family farm…The common factor was that we were connected to these places by the webs of stories derived from lived experience, often in childhood.

We agreed that sense of place could be lost if the place was damaged, but also if the activities we associated with the place were no longer possible.

Ocean

If you care for it, you will protect it! But is sense of place correlated to biodiversity?

The small sample of Qaecologists in this discussion generally agreed that having developed a sense of place with the natural world was a guiding factor in our conservation consciousness. We recognised that engendering a sense of place can be used as a tool to encourage people’s support for conservation related to that place. People who have strong connections to a place are more likely to advocate for it.

But we had some reservations about how sense of place could be used within decision making:

  • Firstly, can we (and should we) quantify the value of sense of place as an ecosystem service in decision making? We are not so sure. It seems very difficult to assign a monetary value to sense of place.
  • Is sense of place a double-edged sword for conservation? Sense of place can be based on connections that are misaligned with biodiversity. Brumbies in the Australian Alps is just one example we discussed. Many Australians have a strong connection to the Alpine country. For some, their sense of place is interwoven with the stories of Banjo Patterson’s “The Man from Snowy River” and the “Silver Brumby” books, where the brumby plays a central role in sense of Alpine place more than native biodiversity. Placing higher value on sense of place in this context isn’t going to assist biodiversity.

    Brumbies_RMacRae

    Brumbies – iconic or feral? Image by Robyn MacRae/Flikr. Used under Creative Commons 2

  • Whose sense of place should be valued in decisions? It’s not an ecological example, but to make a point – should people climb Uluru? For the Anangu who are the traditional owners of Uluru, their sense of place and culture does not permit climbing. But for some other people, their sense of place is connected to the experience of being able to climb – their sense of place is diminished by not being able to climb. Where there is conflict, whose sense of place should take precedence in decision making? When it comes down to values, minority groups can be overlooked.

    Uluru_melalouise

    Whose “sense of place” should take precedence in managing Uluru? Image by MelaLouise/Flikr. Used under Creative Commons 2

We suspect that sense of place, or connection to places, values and activities, may already play a substantial role in decision making. In our experience, decisions in conservation often end up being strongly influenced by values (monetary or other) as well as scientific evidence.  People’s attachment to a place may affect their decision to advocate for a management outcome (whether or not it benefits biodiversity).

Regardless, for us, having “sense of place” for our own special places has brought joy and meaningful connection to nature. Opportunities for people to connect with nature is always something to encourage.

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