Join 1,188 other followers
- American Journal of Botany
- arid zone
- Bayesian inference
- conceptual models
- Conservation biology
- conservation management
- critical thinking
- David Warton
- Dean Graetz
- ecological monitoring
- ecosystem management
- Evolutionary biology
- Fire responses of plants
- functional trait
- hierarchical GLM
- Ian Chubb
- informative priors
- Invasive species
- John Morgan
- Land use change
- multivariate statistics
- Native plant
- Prior probability
- science policy
- species composition
- species traits
- statistical graphics
- Statistical model
- STEM fields
- vegetation dynamics
pv’s paper titles
pv’s paper keywords
With colleagues, we have two grants supporting our work over the coming 3 years. Both concern eucalypt trees and the traits that govern performance: distribution along environmental gradients in one grant; and maximum attainable height in the other. Our plan is to recruit one postdoc responsible for statistical modelling of existing data, field sampling tree traits, and growth experiments, based at QAECO within the School of BioSciences at UM. The second postdoc will be based at UNSW and will mainly conduct modelling: ecoevolutionary models, some mechanistic models and some statistical models. We expect a high degree of collaboration with other projects the PIs have. We will also be looking to recruit students who are competitive for scholarships. Get in touch with me or Daniel if you are interested.
- Pollock LJ, Morris WK, Vesk PA (2012) The role of functional traits in species distributions revealed through a hierarchical model. Ecography 35:716-725.
- Falster DS, FitzJohn RG, Brännström Å, Dieckmann U & Westoby M. (2016). plant: A package for modelling forest trait ecology and evolution. Methods Ecol Evol, 7:136-146
Eucalypt futures: using functional traits to predict species distributions and responses to environmental change
Through the use of functional traits (physical, biochemical, phenological, and physiological adaptations affecting how species grow, survive, and reproduce), collected on hundreds of Eucalypts across diverse habitats, this project will develop unprecedented capacity to explain and predict distributions of diverse Eucalypts across environmental gradients. We will combine these trait data with species distribution, mechanistic eco-evolutionary, and physiological models to generate a robust, integrated assessment of the drivers of Eucalypt species distribution, and thereby the impact of climate change on Eucalypts.
Our overall objective is to improve our understanding and ability to predict Eucalypt responses to environments. To do this, we aim to resolve the conflicting predictions and diverse understanding from four approaches to modelling Eucalypt sensitivity to environment.
Escalating the arms race: Understanding when and how trees get really tall
(Daniel Falster (UNSW) and Peter Vesk (UM), funded by ARC Discovery Program)
Australia’s giant Eucalypt trees are an amazing phenomenon and resource; underpinning unique ecosystems, rich in timber, stored carbon, and animal habitat. While tree height generally arises via an evolutionary arms race for light, the race has escalated dramatically in some locations and species. Using a computational framework that simulates adaptation driven by size-structured competition, this project will quantify how distinct factors-including climate, recruitment, and disturbance-enhance the race for light and can thereby explain the origins of Australia’s giant Eucalypts. With calibrated models of species evolution, coupled with targeted fieldwork and big data, this project clarifies key forces shaping present and future vegetation.
Now, I am pretty terrible at keeping this up to date. But one thing that I’d like to highlight is my Eucalyptus trait project. This project is serving several aims. One is to test the generality of trait-based multi-species distribution models that were developed for a paper during Laura Pollock’s PhD . Another is to contribute to greater amount of trait measurements, and a third is to get out into the field! Collaborators include Will Morris, Will Neal, Laura Pollock and Karel Mokany. Keep your eyes peeled for work on eucalyptus distributions in SE Australia. This work is being supported by Eucalypt Australia and the Australian Academy of Science.
I love reading fiction, and I love science. Occasionally these two meet when I read a science fiction novel. And just occasionally I read a book set in contemporary society where a scientist is one of the protagonists and issues relevant to scientists and their lives are explored. And sometimes I’d like to pass the book on to friends, but mostly forget. So here are a few examples that have recently piqued my interest. Perhaps readers have their own suggestions. And maybe there is a website devoted to this. Any comments, please do tell.
Barbara Kingsolver, Flight behavior. Kingsolver worked in population genetics before becoming a full-time writer. This book captured the essence of scientific discovery as a process of ongoing questioning, the role of science in policy and media and eduction and a little insight into the folk who do science and how different they/we are from most of the wider population. Set against a backdrop of range expansion by Monarch butterflies and climate change in rural north east US, it is a great read, on many levels.
T. Coraghessan Boyle, When the killing’s done. California’s Channel Islands are the setting for a story about managing island invasions and eradications. It iss a great story concerning environmental management problems that some of my colleagues actively deal with on a daily basis: setting objectives, tradeoffs between conflicting objectives, multiple stakeholders, tradeoffs between conflicting objectives, uncertain system models… classic, hard environmental decision problems. The narrative entwines animal rights activists, pastoralists, career scientists and managers and relationships among them. The major protagonist is a female mid-career biologist working for the parks service and charged with overseeing an eradiction program focused on invasive species such as rats that prey on ground nesting seabirds on these islands. I was instantly empathetic.
Less contemporary is the bizarre novel by Daniel Kehlmann. Measuring the world, which provides fanciful double biographies of two of the greatest scientists of the 1800s, one an aristocratic committed empiricist, Alexander von Humboldt who laid the foundations of (plant) biogeography, and the other the lowly-born, brilliant mathematician and theorist Carl Friedrich Gauss. Both possessed of enormous self-belief and drive, they approached the generation of scientific knowledge from fundamentally opposite stances: theory and observational empiricism. The novel charts their lives, including von Humboldt’s epic travels in South America with Aime Bonpland. But for me, its the tension between ways of trying to make sense of the world that is of interest here.
A nice review can be found here.
I’m sure there are many other great reads that concern science as a process, scientists as some of those real people I know, and others that I could never know like von Humboldt. I’d be glad to know of them, do tell…
Last night I sat with my youngest daughter, learning how to convert between decimals and fractions. It lead me to the role of maths and science in our society . And the future ability of my daughter to engage with it–to understand the role of science in developing our understanding, critically evaluating argument and evidence, to understand the contribution science makes to our well being, and to be able to use it in whatever endeavours she pursues.
Both my daughters can readily identify that without science we wouldn’t be able to heat our leftovers in the microwave, would be shivering against the Melbourne winter owing to poor insulation and heating, peering at at our rising power bills in the gloom cast by inefficient lighting, would be unable to watch favourite programs on our laptop, listen to tunes on the ipod, etc.
But many people don’t realize the pervasive importance of science in our everyday lives, it seems. Which brings me to what should be our (Australia’s) plan for STEM (thats Science, Technology , Engineering and Mathematics).
I recently attended a talk from Australia’s , and read a , Prof Ian Chubbposition paper recently published by his office. It assesses the current status of our Science enterprise in Australia, its history, trends and asks what future plan might be sensible for it, with the objective of improving our health and well-being and dealing with significant challenges: changing environments, managing our food and water assets and lifting productivity to maintain Australia’s place in a changing world.
The strategy Chubb outlines makes for compelling reading–and I can’t do justice to it here other than to say it is about Education, Knowledge generation, Innovation and Influence–but is this picked up by the politicians currently campaigning in Australia? Not a whit. Where is the evidence that the political parties have a plan for managing our future? This would seem to be a ripe opportunity.
Land use is expected to affect species and vegetation and those effects should leave a signature in functional trait distributions. Or as we put it elsewhere, land use change causes changes to the ecological filters determining what traits are viable under particular conditions. Possession of such viable trait values is what specifies which species may be viable under such land use change (ignoring effects of biotic filters and immigration subsidies). Yet we know relatively little about the outcomes of the interaction between land use change and species traits on vegetation.
So our broad question was, do equivalent land use changes in different parts of the world (different abiotic and biotic environments) result in similar changes to trait distributions?
Commonly, one might ask whether the mean of some trait distribution changes, e.g. does the mean of potential heights of species decrease? You could also ask whether the trait diversity changes, e.g., whether the distribution becomes narrower or broader. Diversity changes might reflect patterns of convergence or divergence in traits. But another way to think of it is to consider the trait distributions, and to ask whether species are lost from particular parts of the distribution, or added at particular parts of the distribution. For instance, you might expect additions to tails of the distribution if a particular land use change causes the shift of a biotic or abiotic filter.
In this work we analysed 15 datasets (from five continents) of comparisons between intact forest and logged, or logged and grazed, or logged, grazed and abandoned forest. We measured changes to trait distributions, in location, dispersion and at the tails. And we assessed whether those changes are consistent between the tree canopy and understory, and between regions. In particular, the abiotic conditions resulting in gradients of productivity might be expected to affect the response of traits.
There is a wealth of individual results in the paper, but at the broad scale we found that:
- Species richness and trait diversity did not show coordinated reductions with land use change (as we’d predicted before).
- Interestingly, understorey trait changes were more consistent than canopy responses, reminding us to measure both components of vegetation.
- Pastures had more diverse leaf sizes and dispersal syndromes and lower diversity in heights, seed mass and pollination mechanisms compared with forest understories.
- When we found evidence of changes in trait variation with land use change, it could often be ascribed to additions or deletions from one end of the trait distribution, e.g., loss of the tallest heights or additions of really small seeds.
- Trait changes were relatively inconsistent across the regions, and those changes could not be explained by NPP, except for dispersal type.
What does this mean? Well, context probably plays a strong role. Broad classification of land use change may be insufficient to capture the processes involved in vegetation change. We still have a way to go towards generalized understanding of plant functional trait responses to land use change.
As a society, we need to know whether our conservation management is effective. Assessing the effectiveness of vegetation restoration is often hampered by insensitive baseline data. The data used to justify investing in restoration are rarely suitable for conducting sensitive assessments of change through time, even if sensitive, quantitative data are available from subsequent surveys. But can we salvage something from such situations, given that they are common, and that the ideal—high quality data before and after management—is rare?
For a while, Dave Duncan and I have been working away this problem (as has our student, Chris Jones, in his PhD problem). And in a paper to be published in Ecological Applications, we describe an approach for a first-cut analysis of initial, semi-quantitative, rapid assessments, and later, quantitative surveys. The case study is an analysis of investment in vegetation management on private land. And in no way do we intend that this would replace sensitive BACI-type or case-control longitudinal monitoring designs. This is an attempt to say something from a post hoc analysis where ordinarily we’d be forced to admit we can’t say anything about change, let alone the effect of management on that change.
The analysis is Bayesian. We model the change between two sets of values, unknown historical values, and the later observations. Those unknowns are informed by various assumptions, made explicit in the form of Bayesian prior distributions. We try various forms of prior information: the ranges implied by the historical semi-quantitative rapid assessments, and predictions from regional models, and a combination of the two. We show that some variables show changes that are robust to our (explicit) beliefs about the past.
Something this work taught me about Bayesian priors is that the more refined ones’ model, the more sensitive it is to prior assumptions. Models become tuned to the selection of covariates. Two simple things about survey designs for rapid vegetation condition assessment are the need for area-based assessment, rather than roving whole-of site assessment, and how difficult some people find counting the number of large trees.
I will be presenting a talk on the first morning (12 noon, Wed 26th in Peterson room) on using plant trait information in multi-species models of occurrence. My talk will be based on work that was developed by Laura Pollock, Will Morris and I to capture the way that species’ traits may modulate species responses to environmental gradients. We use multi-level GLMs fitted in R and the paper can be found here.
Tartu is where my father was born so I am looking forward to visiting. The whole 3 generations of my family will be getting together for a ‘Little Miss Sunshine’ trip in a ‘van round Estonia. 😉
Estimating abundances of plants in the field is not straightforward and is prone to variation between observers. Ideally we would minimize this variation between observers (and increase repeatability of an observers estimates too). Further, estimates can be biased. Lastly, estimates should reflect uncertainty, but rarely do.
But it is not clear how to best train observers with these problems in mind. Often in practical training, students are presented with some aids, such as thinking about how many cells in a grid are occupied, or mockup images of different cover values. Informally, instructors provide feedback about whether judgments are high or low based on their own experience. Crucially, rarely do we know the truth: what is the actual abundance being estimated. If we knew that, perhaps we could provide students with useful feedback. Occasionally, multiple methods will be taught, with one held to be a ‘gold standard’, e.g., point quadrats, but these are estimates themselves.
As a teacher, I’m concerned that our current practice this isn’t good enough. I believe that I am not very good at estimation, and so passing on my (and our demonstrators’) judgements doesn’t seem right. As a researcher interested in analysing vegetation data I am just as troubled, because sensitivity of analyses is limited by such errors of judgement.
This is what makes the recent work of Bonnie Wintle really interesting. In some work that I participated in, students were first introduced to a method for describing the uncertainty of their subjective estimates. Using the 4 point elicitation, observers estimated abundances in trial plots. Then the observers were provided with feedback. Subsequently the observers estimates some more abundances, enabling an assessment of their change in performance due to the feedback.
The work tested whether the ‘wisdom of the crowds’ could provide useful feedback, in the absence of knowledge of the ‘truth’. Another experiment tested whether the form of the feedback (active or passive) mattered. Briefly, it did. But interested readers, should check out the paper. Calibration (active) feedback involves the observer determining the hit rate of the intervals they estimated.
What is the take away message for training?
Provide feedback in in a structured process in which trainees systematically assess their own performance. Critically, that self assessment needs active consideration of one’s own accuracy and overconfidence.