Two new postdoctoral positions in ecological network & vegetation modelling announced

21 07 2017

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With the official start of the new ARC Centre of Excellence for Australian Biodiversity and Heritage (CABAH) in July, I am pleased to announce two new CABAH-funded postdoctoral positions (a.k.a. Research Associates) in my global ecology lab at Flinders University in Adelaide (Flinders Modelling Node).

One of these positions is a little different, and represents something of an experiment. The Research Associate in Palaeo-Vegetation Modelling is being restricted to women candidates; in other words, we’re only accepting applications from women for this one. In a quest to improve the gender balance in my lab and in universities in general, this is a step in the right direction.

The project itself is not overly prescribed, but we would like something along the following lines of inquiry: Read the rest of this entry »





School finishers and undergraduates ill-prepared for research careers

22 05 2014

bad mathsHaving been for years now at the pointy end of the educational pathway training the next generation of scientists, I’d like to share some of my observations regarding how well we’re doing. At least in Australia, my realistic assessment of science education is: not well at all.

I’ve been thinking about this for some time, but only now decided to put my thoughts into words as the train wreck of our current government lurches toward a future guaranteeing an even stupider society. Charging postgraduate students to do PhDs for the first time, encouraging a US-style system of wealth-based educational privilege, slashing education budgets and de-investing in science while promoting the belief in invisible spaghetti monsters from space, are all the latest in the Fiberal future nightmare that will change our motto to “Australia – the stupid country”.

As you can appreciate, I’m not filled with a lot of hope that the worrying trends I’ve observed over the past 10 years or so are going to get any better any time soon. To be fair though, the problems go beyond the latest stupidities of the Fiberal government.

My realisation that there was a problem has crystallised only recently as I began to notice that most of my lab members were not Australian. In fact, the percentage of Australian PhD students and post-doctoral fellows in the lab usually hovers around 20%. Another sign of a problem was that even when we advertised for several well-paid postdoctoral positions, not a single Australian made the interview list (in fact, few Australians applied at all). I’ve also talked to many of my colleagues around Australia in the field of quantitative ecology, and many lament the same general trend.

Is it just poor mathematical training? Yes and no. Australian universities have generally lowered their entry-level requirements for basic maths, thereby perpetuating the already poor skill base of school leavers. Why? Bums (that pay) on seats. This means that people like me struggle to find Australian candidates that can do the quantitative research we need done. We are therefore forced to look overseas. Read the rest of this entry »





Malady of numbers

30 07 2012

Organism abundance is the parameter most often requiring statistical treatment. Statistics turn our field/lab notes into estimates of population density after considering the individuals we can see and those we can’t. Later, statistical analyses will relate our density estimates to other factors (climate, demography, genetics, human impacts), allowing the examination of key issues such as extinction risk, biomonitoring or ecosystem services (humus formation, photosynthesis, pollination, fishing, etc.). Photos – top: a patch of fungi (Lacandon Jungle, Mexico), next down: a palm forest (Belize river, Belize), next down: an aggregation of butterflies (Amazon, Peru), and bottom: a group of river dolphins (Amazon, Colombia). Photos by Salvador Herrando-Pérez.

Another interesting and provocative post from my (now ex-) PhD student, Dr. Salvador Herrando-Pérez. After reading this post, you might be surprised to know that Salva was one of my more quantitative students, and although he struggled to keep up with the maths at times, he eventually become quite an efficient ecological modeller (see for yourself in his recent publications here and here).

When an undergraduate faces the prospect of a postgraduate degree (MSc/PhD), he or she is often presented with an overwhelming contradiction: the host university expects the student to have statistical skills for which he/she might never have received instruction. This void in the education system forges professionals lacking statistical expertise, skills that are mandatory for cutting-edge research!

Universities could provide the best of their societal services if, instead of operating in isolation, they integrated the different phases of academic training students go through until they enter the professional world. Far from such integration in the last 20 years, universities have become a genuine form of business and therefore operate competitively. Thus, they seek public and private funding by means of student fees (lecturing), as well as publications and projects developed by their staff (research). In this kind of market-driven academia, we need indicators of education quality that quantify the degree by which early-career training methods make researchers useful, innovative and cost-effective for our societies, particularly in the long term.

More than a century ago, the geologist and educator Thomas Chamberlin (1) distinguished acquisitive from creative learning methods. The former are “an attempt to follow by close imitation the processes of other thinkers and to acquire the results of their investigation by memorising”. The latter represent “the endeavour… to discover new truth or to make a new combination of truth or at least to develop by one’s own effort an individualised assemblage of truth… to think for one’s self”. From the onset of their academic training, students of many countries are instructed in acquisitive methods of learning that reward the retention of information, much of which falls into oblivion after being regurgitated during an exam. Apart from being a colossal waste of resources (because it yields near null individual or societal benefits), this vicious machinery is reinforced by reward and punishment in convoluted manners. For instance, one of my primary-school teachers had boys seated in class by a ‘ranking of intelligence’; so one could lose the first seat if the classmate in the second seat answered a question correctly, which the up-to-then ‘most intelligent’ had failed to hit. Read the rest of this entry »





Fanciful mathematics and ecological fantasy

3 05 2010

© flickr/themadlolscientist

Bear with me here, dear reader – this one’s a bit of a stretch for conservation relevance at first glance, but it is important. Also, it’s one of my own papers so I have the prerogative :-)

As some of you probably know, I dabble quite a bit in population dynamics theory, which basically means examining the mathematics people use to decipher ecological patterns. Why is this important? Well, most models predicting extinction risk, estimating optimal harvest rates, determining minimum viable population size and metapopulation dynamics for species’ persistence rely on good mathematical abstraction to be realistic. Get the maths wrong, and you could end up overharvesting a species (e.g., 99.99 % of fisheries management), underestimating extinction risk from habitat degradation, and getting your predictions wrong about the effects of invasive species. Expressed as an equation itself, (conservation) ecology = mathematics.

A long-standing family of models known as ‘phenomenological’ models (i.e., because they deal with the phenomenon of population size which is an emergent property of the mechanisms of birth, death and immigration) has been used to estimate everything from maximum sustainable yield targets, temporal abundance patterns, wildlife management interventions, extinction risk to epidemiological patterns. The basic form of the model describes the growth response, or the relationship between the population’s rate of change (growth) and its size. The simplest form (known as the Ricker), assumes a linear decline in population growth rate (r) as the number of individuals increases, which basically means that populations can’t grow indefinitely (i.e., they fluctuate around some carrying capacity if unperturbed). Read the rest of this entry »