Ecology and Environmental Sciences star in ERA15

The results of the latest assessment of research excellence in Australia have been released. Now, every university will spin the results to suit their own purpose*. While we can leave universities to report their results so they appear to shine in the best possible light, it would be interesting to see how different research fields performed.

Which are Australia’s strongest research fields? The broad research fields with the most universities rated above world standard are “Medical and Health Sciences” and “Environmental Science”.


The number of Australian universities “above” or “well above” world standard as rated by the Excellence in Research for Australia process in 2015 for each of the 22 broad research fields.

Following them are “Chemical Sciences”, “Biological Sciences”, and “Engineering”, with “Mathematical Sciences”, “Agricultural and Veterinary Sciences”, and “History and Archaeology” not far behind.

Within the two strong fields in which I am involved (Environmental Sciences and Biological Sciences), the strongest areas are “Environmental Science and Management” and “Ecology”. Somewhat perversely, “Ecological Applications” is separated from “Ecology” – many of the publications assigned to one during the ERA process could just have easily be assigned to the other.  However, it is clear that Environmental Sciences and Ecology are two of Australia’s strongest research fields.


The number of Australian universities “above” or “well above” world standard as assessed by the Excellence in Research for Australia process in 2015 for the disciplines within the Environmental Sciences and Biological Sciences fields.

This strength of Environmental Sciences and Ecology in Australia is also reflected in Australia’s representation in the list of the most highly-cited authors in the Thomson-Reuters list, something I’ve noted previously.


The proportion of the world’s most highly-cited scientists within each of Thomson-Reuters’ 21 research categories who have their primary affiliation in Australia. The field of Environment/Ecology tops the list for Australia – approximately 1 in 12 of the world’s most highly-cited ecologists/environmental scientists are Australian.

Some other interesting data exist in ERA15, such as total research funding (see below). With that much research funding, you’d hope medical research in Australia would perform well!

But in terms of bang for buck, it is hard to go past some other fields, such as mathematics, environmental sciences and history.


Research funding to different fields for the three years 2011-2013 as reported for ERA15. Ecology makes up about 15% of the research income in Biological Sciences – a touch under $50 million annually.

So, while the Environmental Sciences and Ecology are not the most heavily funded, they are two of Australia’s strongest research fields. Not only that, this research, conducted across many of Australia’s universities, has a large impact, helping manage Australia’s and the world’s environment more efficiently.

So people, let’s recognize the excellence of environmental and ecological research that occurs across Australia!

* Footnote: ANU even devised their own method for ranking institutions, and you won’t be surprised to know that (judged by their own criteria) ANU won. That outcome was parroted by Campus Review, which failed to note that at least one other university out-performed ANU on at least one of their criteria (the proportion of broad research fields rated above word standard). Universities love playing the ranking game, but I’m surprised a news outlet would publish claims without checking them.


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Lecturer in Ecological Modelling

Come and work with us!We’re looking for an outstanding academic to join the School of BioSciences within QAECO. We are particularly interested in applicants with expertise in modelling the distributions of species or biodiversity, or more generally in spatial modelling, working with Dr Jane Elith and others within QAECO.

The closing date for applications is 25 August. Information about the position and how to apply is available at:

Please consider applying. Also, please spread the word by drawing this opportunity to the attention of potential applicants.

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Alpine Grazing Update

My blog has been a little quiet lately. But I’ve written a few things elsewhere. The latest is an update on the issue of cattle grazing in the Alpine National Park. You can read that over at The Conversation – an article I wrote with Libby Rumpff and Georgia Garrard.

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Biodiversity management and unplanned fires

Edit: Missed my seminar? You can listen to a recording with a copy of the slides:

I’m doing a seminar today at Creswick on fire and biodiversity (9:30 a.m., Melbourne time). My talk will discuss unplanned fires, how their incidence can be modelled to predict impacts on biodiversity, and then how those impacts can be managed. A key point I wish to make is that unplanned fires should be anticipated and that relatively simple models can help understand their impacts.


I’ll draw on research from my PhD (20 years ago!), plus some more recent work such as this, this, and this.

If you’d like to listen, I believe the seminar will be streamed live via the link on this page.

Creswick is one of The University of Melbourne’s regional campuses. QAECO is here for a retreat, but it is a bit of a nostalgia trip for me – I spent two years of my undergraduate degree on this campus.

The University of Melbourne campus at Creswick is delightful, and a complete nostalgia trip for me (photo by Peter Vesk;

The University of Melbourne campus at Creswick is delightful, and a complete nostalgia trip for me (photo by Peter Vesk)

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Brunswick in the Victorian 2014 election

I do like an election analysis! After having some fun predicting the result in Indi for the last federal election, I thought I’d look at my home seat of Brunswick in the 2014 Victorian state election.

Jane Garrett, the sitting member for Labor, won Brunswick in 2010 with a 2-candidate preferred (2CP) vote of 19545 votes against Cyndi Dawes, the Greens candidate, on 16974; Garrett won with a margin of 3.5%.

In 2014, the Greens candidate is Tim Read. With 54.08% of the vote counted, the 2CP count gives Garrett 53.9% of the 2CP vote and a lead of 1897 – on the face of it a slight swing to her. Garrett might be re-elected quite safely. However, I’m not so sure it is clear cut just yet.

Jane Garrett and Tim Read are contesting the election for the seat of Brunswick in the 2014 Victorian election (photos from their Twitter accounts).

Jane Garrett and Tim Read are contesting the election for the seat of Brunswick in the 2014 Victorian election (photos from their Twitter accounts).

In 2010, Garrett won 56.3% of the ordinary votes from the 16 booths in Brunswick plus the postal votes. If these are the votes counted to date in the 2CP (it seems they are), then Garrett has actually suffered a swing of 2.4% against her.

In 2010, Garrett won 45.6% of early, provisional and absent votes; she didn’t get a majority of any of those vote types on a 2CP basis, and these are the votes yet to be counted. The question is whether Read, the Greens candidate in this election, can claw back enough votes to overcome the current deficit of almost 2000 votes. It might seem unlikely, but let’s look at whether it is possible.

From 9407 2CP early, provisional and absent votes in 2010, the Greens won by 827 votes. If we add the -2.4% swing seen in the 16 booths and postal votes to 45.6%, we might predict Garrett would get 43.2% of these remaining votes. If there were 10,000 remaining votes, Garrett might get 4320 votes, and Read would then get 5680 – not enough to overcome Read’s current deficit of 1897.

However, there were many more postal and early votes in this election. Antony Green reports that almost 30% of Brunswick’s electors cast postals or early votes in 2014. With an electoral role of 46954, that is almost 14000 votes. Take out the currently listed postals that have already been counted, and we’d expect 12000 early votes, almost 10000 more than in 2010.

In 2010, these early votes favoured the Greens, but it is very difficult to predict where they might fall in 2014 – many more people have taken advantage of the early voting in 2014, so it is hard to guess their preferences.

But add on almost 7000 absent and provisional votes (there were 6773 formal absent votes in 2010; only 400 provisionals), and we might expect 19000 further votes in the election for the seat of Brunswick. If Read wins 55% of these, he will claw back 1900 votes from Garrett – enough for him to win. That seems within the realm of possibilities if we see a swing of 2.4% against Garrett in these votes, noting that the Greens won 52% of the early votes and 55% of the absent votes in 2010.

The election in Brunswick is not over yet. It comes down to where the early and absent votes fall. If they move away from Garrett, Read might spring a surprise.

Edit (3:00 p.m., 30 November): Kevin Bonham doesn’t expect the early votes to be quite as different from the ordinary votes this year as compared to 2010. I’d tend to agree with his assessment. It is hard to see such a large swag of early votes being radically different from those cast on the day. Still, it is worth keeping an eye on this one.

Edits: I corrected some typos in the original version where I typed the wrong figures for the percentage of early and absent votes.

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Effects of stand age on fire severity

Our new paper shows that the probability of crown fire in mountain forests under extreme weather conditions is greatest when trees are about 15 years old. This has implications for debates about how timber harvesting influences the risk of fire. Please email me if you would like a copy.

Probability of canopy consumption versus stand age during extreme weather on Black Saturday. The different zones represent data from sites in different areas and different time zones relative to the wind change. The points are clustered into age classes. The solid line is the mean fitted relationship and the dashed lines are 95% credible intervals.

Probability of canopy consumption versus stand age during extreme weather on Black Saturday. The different zones represent data from sites in different areas and different time zones relative to the wind change. The points are clustered into age classes, showing that the results are relatively consistent across zones. The solid line is the mean fitted relationship and the dashed lines are 95% credible intervals (reproduced from Taylor et al. in press).

Crown fire is a major driver of the dynamics of mountain ash forest, so changes in the chance of crown fire with stand age are important. Crown fire also has a major impact on risk to humans. Crown fire typically has much greater intensity than fires that remain on the forest floor, partly because more fuel is being consumed in the crown. And the probability of houses being burnt and people dying increases with fire intensity.

The relationship between stand age and the incidence of crown fire is also somewhat controversial. Some studies suggest that the probability of crown fire in mountain ash forests decreases with stand age, while others suggest it increases. These previous studies have tended to only look for monotonic relationships, and have sometimes not controlled for weather conditions at the time of fire.

Our recent paper shows that, under the most extreme fire weather conditions, the probability of crown fire is very low in the youngest forests (<5 years), but then increases rapidly until around 15 years of age. After that point, the probability of crown fire decreases substantially with age.

This pattern occurs in response to changes in the forest structure and fuel availability. In mountain ash forests, fuel loads approach their maximum levels at around 15-20 years of age. Beyond that age, fuel loads remain high, but various factors will reduce the risk of crown fire. The most obvious effect is that the crowns are further from the ground, so the fires are less easily able to climb into the crown. Secondly, moister forest elements (e.g., rainforest plant species) can become more prevalent over time, and these can reduce fire intensity.

One of the other interesting aspects of this paper is our method of analysis. We used a spatially-correlated probit regression model to model the probability of fire. This is a form of the multi-variate probit model used in our recent joint species distribution model. The difference is that in the fire paper we modelled the correlation as a simple (negative exponential) function of distance. That means that fire was perfectly correlated in its incidence for points immediately adjacent to each other (i.e., at zero distance apart, pairs of points either both burnt or both remained unburnt). As distance between points increased, the correlation decayed toward zero (i.e., at large distances between points, the incidence of fire was independent).

The controversy has hit the press, largely focusing on the consequence for fire risk. Essentially, if an area is logged or burnt a decade or three previously, then the risk of crown fire is substantially increased. In areas where the Black Saturday fires burnt, many of these younger areas were places where 1939 regrowth had been harvested. Therefore, we see headlines such as “Study finds logging increased intensity of Black Saturday fires“.

If you’d like to know more, please read the paper – email me if you don’t have access.

Taylor, C., McCarthy, M.A., and Lindenmayer, D.B. (in press). Non-linear effects of stand age on fire severity. Conservation Letters. [Online]

Posted in CEED, Communication, New research, Probability and Bayesian analysis | Tagged , , , , | 1 Comment

My talk at #ISEC2014

I’m speaking tomorrow on the last afternoon of the International Statistical Ecology Conference in Montpellier. I’ll be arguing that usual metrics (e.g., AIC) to measure the performance of species distribution models (SDMs) might not actually be relevant for selecting models that are to be used for management. There are two reasons for this.

The first reason is the metrics are usually based on the ability to fit the calibration data, not the quality of their predictions where they will be used.

Secondly, most metrics of SDMs consider statistical fit (e.g., AIC can be thought of as a bias-corrected estimate of deviance), which also might not be relevant to management. Mangers will want to select the model that will provide the best management outcome. The link between the usual metrics and management outcomes is indirect at best.

Instead, I develop a metric that is relevant when using SDMs to optimize eradication or searches of species. This metric aims to reflect the number of occurrences of a species that will remain undetected when using a species distribution model for spatial allocation of search effort.

The paper by Lawson et al. (2014) suggests that metrics need to reflect the use, but I think falls short of stating that the metrics that are used are actually not as relevant as they could be.

Slides from my talk are available here.

Relevant references that I build on are:

  • Guillera-Arroita, G., Hauser, C. E. and McCarthy, M. A. (2009) Optimal surveillance strategy for invasive species management when surveys stop after detection. Ecology and Evolution 2014; 4(10):1751–1760. doi: 10.1002/ece3.1056
  • Hauser, C. E. and McCarthy, M. A. (2009) Streamlining ‘search and destroy’: cost-effective surveillance for invasive species management. Ecology Letters, 12: 683–692. doi: 10.1111/j.1461-0248.2009.01323.x
  • Lawson, C. R., Hodgson, J. A., Wilson, R. J., Richards, S. A. (2014) Prevalence, thresholds and the performance of presence–absence models. Methods in Ecology and Evolution, 5: 54–64. doi: 10.1111/2041-210X.12123
  • McCarthy, M. A., Thompson, C. J., Hauser, C., Burgman, M. A., Possingham, H. P., Moir, M. L., Tiensin, T. and Gilbert, M. (2010) Resource allocation for efficient environmental management. Ecology Letters, 13: 1280–1289. doi: 10.1111/j.1461-0248.2010.01522.x
Posted in CEED, Communication, Detectability, Ecological indices, Ecological models, New research, Probability and Bayesian analysis | 1 Comment