QAECO’s tips for running a successful workshop

Our group runs a lot of workshops in many forms: structured decision making processes; expert elicitation; targeted analyses with many participants; brainstorming of broad issues; and everything in between. Collectively we’ve learnt a few dos and don’ts. In a previous post we covered workshop facilitation and introduced the four Ps: here, we expand on these points and add a fifth (Place).

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Strategic approaches to planning for conservation and development: Putting a stop to cumulative impacts on threatened species

By Brendan Wintle (This article was first published in the April 2014 issue of Decision Point, The Monthly Magazine of the Environmental Decisions Group)

NERP Environmental Decisions (NERP ED) is working closely with the Strategic Approaches Branch (Department of the Environment) to apply state-of-the-art decision analysis to guard against cumulative impacts on threatened species and ecological communities (as listed under the EPBC Act).

The idea of strategic assessment, supported by regional sustainability planning (RSP), is to move away from case-by-case approvals of actions under the EPBC Act towards a plan for sustainable development and conservation of biodiversity in a region or State. Case-by-case approval can lead to the ‘death by a thousand cuts’ whereby the cumulative impacts of many small actions may lead to serious biodiversity declines (and ultimately extinction).

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Australia’s favourite eucalypt

Voting closed on Monday 24 March at 11:40 p.m.

Congratulations to Mountain Ash; regnans in excelsis. E. regnans reigns on high.

Snow Gum E. pauciflora won silver, and it was bronze for Ghost Gum C. aparrerinja. Honourable mentions go to River Red Gum E. camaldulensis an early leader, Lemon-scented Gum C. citriodora and Yellow Box  E. melliodora.


The decisive moment of the vote in Australia’s favourite eucalypt occurred on Twitter about 24 hours before the close of voting. Euan Ritchie went head to head with Sarah Rees; it was Mountain Ash versus Ghost Gum. The sledging culminated in campaigns swinging into gear.SarahReesTweet

Votes for mountain ash and ghost gum, which had been fourth and third behind snow gum and river red gum, began to surge.

By midday on the last day of polling, mountain ash had taken the lead, but ghost gum was in pursuit, moving into second place by mid afternoon. River red gum was blown away by the cyclonic campaigns, but snow gum hung in there, moving back to second within hours of the polls closing.

But not even a last minute Eucalypt High Court ruling from Chief Justice Laura Pollock to amend the ballot could change the course of the election.


FavEucStartSo how did #FavEuc come about? We ran the competition on a whim, after Peter Vesk helped Pia Lentini identify a specimen via Twitter.

From there, a round of nominations preceded the voting, which was open for a week. You can see the full results below.

A massive thank you to everyone who participated in the vote for Australia’s favourite eucalypt. We hope you enjoyed some of the great diversity of Australia’s eucalypts. One of my biggest thrills was chatting about eucalypt taxonomy, complete with scientific names of species and plant families, with Australia’s Communications Minister.


The nominations received prior to the poll officially opening:

A. costata
C. aparrerinja
C. citriodora
C. maculata
E. blakelyi
E. cadens
E. caesia
E. camaldulensis
E. cladocalyx
E. coolabah
E. cordata
E. dalrympleana
E. deanei
E. deglupta
E. delegatensis
E. diversicolor
E. diversifolia
E. ewartiana
E. globulus
E. gongylocarpa
E. haemastoma
E. kitsoniana
E. largiflorens
E. leucoxylon
E. macrocarpa
E. marginata
E. melliodora
E. microcarpa
E. niphophila
(might get lumped with E. pauciflora)
E. ocrophloia
E. olida
E. pauciflora
E. perriniana
E. phoenicea
E. polyanthemos
E. pulchella
E. regnans
E. rubida
E. salmonophloia
E. salubris
E. sclerophylla
(might get lumped with E. haemastoma)
E. sheathiana
E. similis
E. socialis
E. stellulata
E. strzeleckii
E. tenuiramis
E. tetraptera
E. tricarpa
E. vernicosa
E. verrucata
E. viminalis
E. viridis

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Now you see me, now you don’t: The impact of imperfect detection on species distribution models

By Gurutzeta Guillera-Arroita and José Lahoz-Monfort (This article was first published in the March 2014 issue of Decision Point, The Monthly Magazine of the Environmental Decisions Group)

All ecologists know that wildlife surveys are almost never perfect. Individuals are often missed and even whole species may remain completely undetected even though they are present. Imperfect detection has important implications for wildlife monitoring and species distribution modelling (SDM) and yet it has largely been overlooked in the SDM literature. In an attempt to better understand how imperfect detection affects SDMs, we simulated a range of different detectability scenarios. Our results highlight how managers and conservation workers can improve SDM practice by accounting for detectability (Lahoz-Monfort et al, 2014).

As a nice example of the problems of imperfect detection, consider the Alaotran gentle lemur in northeast Madagascar. It is the only species of primate that inhabits a wetland, and surveys are carried out by canoe along channels cutting through the dense marsh vegetation. Individuals can be easily spotted when they stand at the edge of the vegetation, but their detection becomes extremely difficult as soon as they move slightly deeper into the reeds and papyrus. You don’t see them anymore, but they are still there.

Alaotran gentle lemur

Now you see it, now you don’t! As with so many species, the Alaotran gentle lemur (Hapalemur alaotrensis) is difficult to detect in the wild. Due to the dense structure of most of its marsh habitat, individuals are easily missed unless they stand right at the vegetation edge (as pictured left). In the picture on the right, two individuals stand slightly deeper among the papyrus. Can you spot them?

Factors leading to imperfect detection

Failing to spot animals has direct implications for wildlife monitoring. When not taken into account, it leads to the underestimation of population abundance and species occurrence rates.

Many things contribute to how detectable a species is in a survey. It might be that the habitat of the Alaotran gentle lemur makes it hard to spot, but their behavior being able to stay still and quiet when people are around is also factor. How much effort is put into finding them is also important as is the skill level of the people looking for them.

So detectability is the product of species and habitat characteristics, survey effort and observer skills, and it is therefore likely to vary in space and time. This variation amplifies the problem of imperfect detection as it can lead to the missing of relevant ecological relationships and trends, or the detection of spurious ones. Given that the modeling of species distributions is a fundamental tool in a wide range of applications in ecology, species management and conservation, it is important that the impact that imperfect detection can have is fully accounted for.

Impact of imperfect detection in the estimation of the distribution of a virtual species.

Impact of imperfect detection in the estimation of the distribution of a virtual species. In this example, occupancy increases with elevation while detectability decreases, (i.e., occupancy and detectability are negatively correlated) (color range from red to dark blue corresponds to values from 0 to 1). If disregarded, imperfect detection causes the underestimation of species occurrence probabilities, especially at higher elevations (left column), and can lead to the misidentification of critical habitat (right column, in orange).

Assessing the impact of imperfect detection

We used simulations to explore a range of plausible scenarios where detectability was either constant, a function of the same factor driving species occupancy, or a function of an independent covariate. Using these scenarios, we assessed the performance of three classes of modeling methods (based on presence-absence, presence-background and occupancy-detection data), looking at two key performance aspects: model calibration (ie, how well the predicted probabilities match the observed proportions of sites occupied) and discrimination ability (ie, how well the model distinguishes between occupied and empty sites).

Summary of the impacts that disregarding imperfect detection can have on the performance of SDMs as a function of the structure of the detection process with respect to the occupancy process.

Summary of the impacts that disregarding imperfect detection can have on the performance of SDMs as a function of the structure of the detection process with respect to the occupancy process. Symbol ✗ represents that imperfect detection affects performance, and ✓ that it does not. This applies to both presence-absence and presence-background methods. Note that presence-background methods such as Maxent produce a suitability index rather than a probability and are thus not necessarily calibrated even assuming perfect detection.

The fact that imperfect detection can bias the estimation of ecologically relevant state variables is not new. However, it’s largely been overlooked in the SDM literature. Furthermore, there has been confusion about its impacts and about the performance of models that explicitly account for detectability. Having this in mind, there are three key messages that arise from our results:

  1. Presence-background methods are also affected by imperfect detection. Even if false absence records are avoided, imperfect detection may imply that presence records do not represent a random sample of sites where the species occurs, which leads to the biased estimation of environmental relationships. This means that presence-background methods such as Maxent are not any more immune to imperfect detection than presence-absence methods.
  2. The impact of imperfect detection depends on its relationship with the environment. Disregarding imperfect detection is a greater problem when detectability is negatively correlated with species occupancy, or when it depends on independent covariates that are included as candidate occupancy predictors. Not only is model calibration impacted, but discrimination ability is also undermined (see summary table). In practice this means that what seems the best habitat for the species may simply represent sites where the species is more easily observable (see example maps).
  3. Modeling detectability improves the performance of species distribution models. Modeling occupancy and detection simultaneously does not necessarily require a greater sampling effort, but rather that data are collected so that they are informative about detectability. While all models may have similar ability to describe where the species is observed, only models that account for detectability provide a reliable estimation of where the species occurs (i.e., true SDM). Past comparative studies that assessed the performance of SDMs by evaluating their ability to predict detections rather than presences failed to reveal the actual benefits of accounting for detectability.

So what can we conclude from all of this? Our recommendation is that recording data in ways that allow the modeling of the detection process should become standard practice in future surveys. Possible methods include recording the detection/non-detection of the species in multiple visits to sites, or inter-detection times within a single survey visit.

When such data are not available, diligent consideration and reporting of the possible impacts of imperfect detection, including how it is likely to bias inference and prediction, should be considered a minimum standard of good species distribution modeling practice.

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New paper in Nature: Eutrophication weakens stabilising effects of diversity in natural grasslands

Bogong_Jan10Plant diversity, eutrophication and the stability of natural grasslands is the topic of a paper by Yann Hautier and the Nutrient Network published online this week in Nature. Check out a blog about the paper by Jos Moore (QAECO associate and NutNet member).

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Research and teaching positions at The University of Melbourne

The University of Melbourne is recruiting outstanding researchers to research only positions that will convert to regular teaching and research positions after 5 years. These appointments are overseen by the University’s senior executive, with support of relevant faculties.

We’d love to nominate an outstanding researcher (relative to experience) in the area of plant ecology. That researcher would ideally complement QAECO (rather than simply replicate what we do). Someone with a strong field-based or experimental program, underpinned by solid theoretical foundations and quantitative methods would seem ideal.

If that sounds like you, or if you think you have other complementary research interests, and if you have a strong track record, then please get in touch so we can discuss your possible nomination for one of the available positions.

The scheme is targeting people who have completed their PhD 3-5 years ago and have established a strong independent research program, through to outstanding senior researchers. Nominated researchers will be assessed relative to opportunity; researchers at the more junior end of the range are particularly encouraged.

So far, the university has received many fewer nominations for women than men for this scheme. Everyone would like to redress that bias.

Please contact Mick if you want to discuss this opportunity:

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QAECO’s favourite ecology and conservation papers of 2013

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.

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