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Archive for the ‘Ecological’ Category
Friday, July 10th, 2009
Recently I’ve been working on a review of the latest contribution to The Science and Practice of Ecological Restoration book series, entitled New Models for Ecosystems Dynamics and Restoration (edited by Hobbs and Suding). Here’s an outline of what I’ve been reading and thinking about – the formal review will appear in print in Landscape Ecology sometime in the future.
The Society for Ecological Restoration defines ecological restoration as an “intentional activity that initiates or accelerates the recovery of an ecosystem with respect to its health, integrity and sustainability”. Restoration ecology is a relatively young academic field of study that addresses problems faced by land managers and other restoration practitioners. Young et al. suggest that models of succession, community assembly and state transitions are an important component of ecological restoration, and that seed and recruitment limitation, soil processes and diversity-function relationships are also important.
The ‘new’ models referenced in the title of the book are ‘threshold’ or ‘regime shift’ ecosystem models. These models are ‘new’, the editors argue, in the sense that they contrast gradual continual models and stochastic models. Gradual continuous models are described as those that assume post-disturbance ecosystem recovery follows a continuous, gradual trajectory and are associated with classical, Clementsian theory that assumes steady, uni-directional change towards some single equilibrium state. Stochastic models assume exogenous drivers dominate the behavior of ecosystems to the extent that non-equilibrium and unstable systems states are the norm. Threshold models assume there are multiple (in contrast to the Clementsian view) stable (in contrast to the stochastic view) ecosystem states and represent changes from one relatively distinct system state to another as the result of small changes in environmental (driving) conditions. Thresholds and regime shifts are important to consider in restoration ecology as there may be thresholds in system states beyond which recovery to the previous (healthy) state is not possible.
Two types of threshold model are considered in New Models;
i) state-and-transition (S-T) models that represent multiple (often qualitative) stable states and the potential transitional relationships between those states (including the rates of transition), and
ii) alternative stable state (ASS) models which are a subset of S-T models and generally represent systems with fewer states and faster transitions (flips) between the alternative states.
For example, S-T models are often used to represent vegetation and land cover dynamics (as I did in the LFSM I developed to examine Mediterranean landscape dynamics), whereas ASS models are more frequently used for aquatic systems (e.g. lake ecosystems) and chemical/nutrient dynamics.
New Models focuses on use of these models in ecological restoration and provides an excellent introduction to key concepts and approaches in this field. Two of the six background chapters in this introduction address models and inference, two introduce transition theory and dynamics in lake and terrestrial ecosystems (respectively), and two discuss issues in social-ecological and rangeland systems. These background chapters are clear and concise, providing accessible and cogent introductions to the systems concepts that arise in the later case studies. The case studies present research and practical examples of threshold models in a range of ecosystems types – from arid, grassland, woodland and savanna ecosystems, though forest and wetland ecosystems, to ‘production landscapes’ (e.g. restoration following mining activities). Although the case study chapters are interesting examples of the current state of the use and practice of threshold modeling for ecological restoration, from my perspective there are certain issues that are insufficiently addressed. Notably, there is limited explicit consideration of spatial interactions or feedbacks between social and ecological systems.
For example, in their background chapter King and Whisenant highlight that many previous studies of thresholds in social-ecological systems have investigated an ecological system driven by a social system, ignoring feedbacks to the social components. Explicitly representing the links between social and ecological components in models does remain a daunting task, and many of the case studies continue in the same vein as the ‘uni-directional’ models King and Whisenant hint at (and I’ve discussed previously). The editors themselves highlight that detailed consideration of social systems is beyond the scope of the book and that such issues are addressed elsewhere (including in other volumes of the Ecological Restoration book series – Aronson et al.). However, representing human-environment feedbacks is becoming increasingly vital to ensure appropriate understanding of many environmental systems and their omission here may prove unsatisfactory to some.
A second shortcoming of the book, from the perspective of a landscape ecologist, is the general lack of consideration for spatial pattern and scaling and their influences on the processes considered in the case studies. In their background chapter on resilience theory and rangelands, Bestelmeyer et al. do highlight the importance of a landscape perspective and considering land as being a ‘state mosaic’, but only a single case study really picks up on these concepts in earnest (Cale and Willoughby). Other case studies do indirectly consider spatial feedbacks and landscape context, but explicit representation of relationships between spatial patterns and ecosystems processes is lacking.
However, these criticisms do need to be considered in light of the objectives of New Models. At the outset, the editors state that the book aims to collectively evaluate threshold modeling approaches as applied to ecological restoration – to examine when and where these models have been used, what evidence is used to derive and apply them, and how effective they are for guiding management. In their synthesis chapter the editors highlight that the models presented in the book have been used heuristically with little testing of their assumptions and ask; “Does this indicate an obvious gap between ecological theory and restoration practice?” For example, in their chapter on conceptual models for Australian wetlands, Sim et al. argue that the primary value of threshold models is to provide a conceptual framework of how ecosystems function relative to a variety of controlling variables. The editors’ suggestion is that restoration practitioners are applying models that work rather than “striving to prove particular elements” (of system function or ecological theory), and that maybe this isn’t such a bad approach given pressing environmental problems.
Potentially, this is a lesson that if landscape ecologists are to provide ecosystem managers and stew
ards with timely advice they may need to need to scale-back (i.e., reduce the complexity of) their modeling aims and objectives. Alternatively, we could view this situation as an opportunity for landscape ecologists to usefully contribute to advance the field of ecological restoration. Most likely it is indicative that where practical knowledge is needed quickly, simple models using established ecological theory and modelling tools are most useful. But in time, as our theoretical understanding and representation of spatial and human-environment interactions advances, these aspects will be integrated more readily into practical applications of modelling for ecological restoration.
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This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
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Tuesday, June 30th, 2009
The abstract we submitted to the ESA Meeting was accepted a while back. Since we submitted it, Megan and I have been back in the field for some additional data collection and I’ve been doing some new analyses. Some of these new analyses are the result of my attendance at the Bayesian statistics workshop at US-IALE in Snowbird. Since then I’ve been learning more by picking the brain of a former advisor, George Perry, and doing a fair bit of reading (reading list with links at bottom). And of course, using my own data has helped a lot.
One of the main questions I’m facing, as many ecologists often do, is “which variables should be in my regression model?” This question lies at the core of model inference and assumes that it is appropriate to infer ecological process from data by searching for the single model that represents reality most accurately. However, as Link and Barker put it:
“It would be nice if there were no uncertainty about models. In such an ideal world, a single model would be available; the data analyst would be in the enviable position of having only to choose the best method for fitting model parameters based on the available data. The choice would be completely determined by the statistician’s theory, a theory which regards the model as exact depiction of the process that generated the data.
“It is clearly wrong to use the data to choose a model and then to conduct subsequent inference as though the selected model were chosen a priori: to do so is to fail to acknowledge the uncertainties present in the model selection process, and to incestuously use the data for two purposes.”
Thus, it usually more appropriate to undertake a process of multi-model inference and search for the ‘best’ possible model (given current data) rather than a single ‘true’ model. I’ve been looking into the use of Bayesian Model Averaging to address this issue. Bayesian approaches take prior knowledge (i.e., a probability distribution) and data about a system and combine them with a model to produce posterior knowledge (i.e., another probability distribution). This approach differs from the frequentist approach to statistics which calculates probabilities based on the idea of a (hypothetical) long-run of outcomes from a sequence of repeated experiments.
For example, estimating the parameters of a linear regression model using a Bayesian approach differs from a frequentist ordinary least squares (OLS) approach in two ways:
i) a Bayesian approach considers the parameter to be a random variable that might take a range of values each with a given probability, rather than being fixed with unknown probability,
ii) a Bayesian approach conditions the parameter estimate probability on the sample data at hand and not as the result of a set of multiple hypothetical independent samples (as the OLS approach does).
If there is little prior information available about the phenomena being modelled, ‘uninformative priors’ (e.g., a normal distribution with a relatively large variance about a mean of zero) can be used. In this case, the parameter estimates produced by the Bayesian linear regression will be very similar to those produced by regular OLS regression. The difference is in the error estimates and what they represent; a 95% confidence interval produced by a Bayesian analysis specifies that there is a 95% chance that the true value is within that interval given the data analyzed, whereas a 95% confidence interval from a frequentist (OLS) approach implies that if (hypothetical) data were sampled a large number of times, the parameter estimate for those samples would lie within that interval 95% of those times.
There has been debate recently in ecological circles about the merits of Bayesian versus frequentist approaches. Whilst some have strongly advocated the use of Bayesian approaches (e.g., McCarthy 2007), others have suggested a more pluralistic approach (e.g., Stephens et al. 2005). One of the main concerns with the approach of frequentist statistics is related to a broader criticism of the abuse and misuse of the P-value. For example, in linear regression models P-values are often used to examine the hypothesis that the slope of a regression line is not equal to zero (by rejecting the null hypothesis that is equal to zero). Because the slope of a regression line on a two-dimensional plot indicates the rate of change of one measure with respect to the other, a non-zero slope indicates that as one measure changes, so does the other. Consequently it is often inferred that a processes represented by one measure had an effect, or caused, the change in the other). However, as Ben Bolker points out in his excellent book:
“…common sense tells us that the null hypothesis must be false, because [the slope] can’t be exactly zero [due to the inherent variation and error in our data] — which makes the p value into a statement about whether we have enough data to detect a non-zero slope, rather than about whether the slope is actually different from zero.”
This is not to say there’s isn’t a place for null hypothesis testing using P-values in the frequentist approach. As Stephens et al. argue, “marginalizing the use of null-hypothesis testing, ecologists risk rejecting a powerful, informative and well-established analytical tool.” To the pragmatist, using whatever (statistical) tool available seems eminently more sensible than placing all one’s eggs in one basket. The important point is to try to make sure that the hypotheses one tests with P-values are ecologically meaningful.
Back to Bayesian Model Averaging (BMA). BMA provides a method to account for uncertainty in model structure by calculating (approximate) posterior probabilities for each possible model (i.e., combination of variables) that could be constructed from a set of independent variables (see Adrian Raftery’s webpage for details and examples of BMA implementation). The ‘model set’ is all possible combinations of variables (equal to 2n models, where n is the number of variables in the set). The important thing to remember with these probabilities is that it is the probability that the model is the best one from the model set considered – the probability of other models with variables not measured or included in the model set obviously can’t be calculated.
The advantage over other model selection procedures like stepwise regression is that the output provides a measure of the performance of many models, rather than simply providing the single ‘best’ model. For example, here’s a figure I derived from the output BMA provides:
The figure shows BMA results for the five models with highest posterior probability of being the best candidate model from a hypothetical mo
del set. The probability that each model is the best in the model set is shown at top for each model – Model 1 has almost 23% chance that it is the best model given the data available. Dark blocks indicate the corresponding variable (row) is included in a given model – so Model 1 contains variables A and B, whereas Model 2 contains Variable A only. Posterior probabilities of variables being included in the best model (in the model set) are shown to the right of the blocks – as we might expect given that Variable A is present in the five most probable models it has the highest chance of being included in the best model. Click for a larger image.
BMA also provides a posterior probability for each variable being included in the best candidate model. One of the cool things about the variable posterior probability is that it can be used to produce a weighted mean value from all the models for each variable parameter estimate, each with their own Bayesian confidence interval. The weight for each parameter estimate is the probability that variable is present in the ‘best’ model. Thus, the ‘average’ model accounts for uncertainty in variable selection in the best candidate model in the individual parameter estimates.
I’ve been using these approaches to investigate the potential factors influencing local winter white-tailed deer density in in managed forests of Michigan’s Upper Peninsula. One of the most frequently used, and freely available, software packages for Bayesian statistics is WinBUGS. However, because I like to use R I’ve been exploring the packages available in that statistical language environment. Specifically, the BRugs package makes use of many OpenBUGS components (you actually provide R with a script in WinBUGS format to run) and the BMA package provides functionality for model averaging. We’re in the final stages of writing a manuscript incorporating these analyses – once it’s a bit more polished (and submitted) I’ll provide an abstract.
Reading List
Model Inference: Burnham and Anderson 2002, Stephens et al. 2005, Link and Barker 2006, Stephens et al. 2007
Introduction to Bayesian Statistics: Bolker 2007 [webpage with chapter pre-prints and exercises here], McCarthy 2007
Discussion of BMA methods: Hoeting et al. 1999, Adrian Raftery’s Webpage
Examples of BMA application: Wintle et al. 2003, Thomson et al. 2007
Criticisms of Stepwise Regression: James and McCulloch 1990, Whittingham et al. 2006
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, MichiganUP, Modelling, Statistical | 1 Comment »
Monday, May 25th, 2009
It can be hard not to abandon hope for a sustainable future when you read about our rapidly growing global population and the hopes of those in the developing world (growing the fastest) to lead more ‘western’ lifestyles. For ‘western’, read ‘consumptive’. Last year Jared Diamond came up with new numbers to make us feel even more hopeless; economically more developed countries are consuming resources and producing waste 32 times faster than less developed countries. That means, Diamond estimates, if everyone on earth were to eat as much meat, drive their cars as far and use electricity as prodigiously as Europeans, Americans and Japanese currently do it would be as if the human population had suddenly ballooned to 72 billion.
In an editorial in the latest issue of Conservation Biology, R. Edward Grumbine and Jianchu Xu use Diamond’s example when discussing the rise of China as a global economic power and consumer and the potential implications for conservation, the environment and the climate debate:
“China’s rapid economic rise has not helped conservation much. The country faces severe environmental challenges as the largest human population in history builds highways, factories, and housing to fully join the modern industrial world. The PRC [People's Republic of China], however, remains relatively poor. Per capita income in 2007 was a mere one-fifth of the U.S. average; a typical American teenager has more discretionary income than the total annual salary of the average Chinese citizen.
Despite the importance of biodiversity issues, we want to draw attention to less-discussed environmental concerns that involve China at regional and global scales and which will likely transform life for all of us over the rest of the 21st century.”
Focusing on their discussion about issues related to climate change, Grumbine and Xu point out;
“Even if the European Union and the United States magically reduced their greenhouse gas emissions to zero while you are reading this sentence, China’s current pace by itself may keep global emissions rising through 2020.
China should not be blamed for the world’s runaway greenhouse gas emissions; the United States never even ratified the Kyoto Protocol. And we emphasize that China’s development dream is not a vision exclusive to the PRC. Beyond the Middle Kingdom, there are at least 1.2 billion people desiring cars, a decent house attached to a sewer system, potable water, and a fair measure of education and health care.”
The consequences of Chinese, and other poorer nations, realising their hopes of economic development?
“China and the rest of the less-developed world are driving wealthy countries toward a global reckoning with the fossil-fuel-powered, high-consumption, industrial way of life.
… The Tyndall Centre for Climate Change Research in the United Kingdom has estimated that some 23% of China’s total emissions result from net exports to the developed world. The Earth’s atmosphere bears a message: we are all in this together. China and climate change have collapsed us and them into we.”
Grumbine and Xu reckon China is poised to assume a leadership role in solving our international environmental problems despite, or maybe as a consequence of, its rapidly growing population and ecological footprint. The US government also seems to now recognise that we’re all in this together. In February, US Secretary of State Hillary Clinton set out to discuss these issues during her visit to China, and it appears her path may have been previously beaten (behind closed doors) during the preceding administration. In vowing to “restore science to its rightful place” President Obama named Nobel Prize laureate Steven Chu as his Energy Secretary. However, it seems that despite wanting to put science first, domestic political opposition to emissions cuts and to changes in the US energy mix are hindering these moves. Chu said recently to the BBC;
“As someone very concerned about climate I want to be as aggressive as possible but I also want to get started. And if we say we want something much more aggressive on the early timescales that would draw considerable opposition and that would delay the process for several years. … But if I am going to say we need to do much, much better I am afraid the US won’t get started.”
However, Chu went on to discuss his aims for a “massive programme of efficiency for commercial buildings”, vastly improved cost-effectiveness of solar energy, and an interconnected wind power grid. The Obama climate change bill is making progress, but the slow movement on energy policy because of domestic resistance to change has potential global consequences. If the economically more developed countries of the world cannot show that their populations are willing and able to change their lifestyles to be less consumptive, negotiations with developing countries will be hindered.
Pressure from lower levels of government will help push things along. Last week 178 Michigan scientists (including myself) signed a letter to the Michigan Congressional delegation calling for actions to achieve strong and effective federal climate change solutions policies. And scientists can (and need) to do more than just write letters and do their basic (physical) research in their laboratories and at their computers. Reiterating his commitment to science in an address to the National Academy of Science, President Obama asked scientists and academics to engage in society to inspire and enable people “to be makers of things, not just consumers of things”.
A paper by David Pimentel and colleagues, entitled Energy efficiency and conservation for individual Americans, provides some solid numbers and ideas about how we as individual citizens in the economically more developed world can modify our residential energy use, reduce the impact of personal transport, and make informed decisions about what we eat. I’ve listed some of their more interesting suggestions for a sustainable lifestyle below. These are rational and effective ways we can change our lifestyles to live more sustainably and show that we are willing to share the responsibility of mitigating the human impact on the global environment. If we don’t want to be left with mere hope for a sustainable future, we need to show how others in the world can realise their hopes of development whilst conserving energy, water and our other natural resources.
Residential Energy Conservation
- Improve and upgrade
windows – 25% of residential heating and cooling energy is lost directly through single pane windows
- Plant trees – deciduous on south to shade the house in the summer and allow full-sun in the winter, evergreen trees to the north can act as a wind-break
- Use the microwave – it’s the most efficient way to steam, boil, and bake vegetables
- Power-down your computer when it’s not in use – “computers should be turned off if the unit will be left for 2 hours or more and if left for 30 min the machine should be set in standby mode”
Pimentel and colleagues suggest that implementing these, and other, measures around the home would save around 5,600 kWh/year, resulting in savings of about $390/year on home energy costs.
Personal Transport
- Drive slower – “A reduction in speed from 104 kmph (65 mph) to 86 kmph (55 mph) will reduce fuel consumption 19% (UrbanPlanet). For a 104 km trip, only an additional 11 min would be required if one traveled at 86 kmph. This extra 11 min would repay the person nearly $1.86 in fuel saving, or repay the person $10/h.”
- Inflate your car tires properly – this will decrease the fuel consumption by up to 3%
- Get rid of that junk in your trunk – “each 45 kg (100 pounds) of additional load in the car will reduce fuel mileage about 1%”
- Ride your bike – bicycling uses 25 kcal/km (34 kcal/mile)compared with 938 kcal/km (1,510 kcal/mile) for a mid-sized car
In summary: “[c]urrently, the average American uses about 1,900 l (500 gallons) of fuel/year in personal transport in contrast to the average person in the United Kingdom who consumes 1,700 l (450 gallons) (Renner 2003). If Americans implement the suggestions listed above [and others I haven't listed] over a 10-year period, it would be possible to reduce fuel oil consumption between 10% and 20% from the current 20 quads of vehicle fuel [approximately 600 billion l or about 16 billion gallons of fuel] consumed in the U.S.”
Food system
The authors highlight several ways in which farmers and policy-makers can aggressively pursue sustainable agricultural practices. They are less precise about what individuals’ can do but offer some general ideas:
- Eat local products – reduces transport energy costs [and find out where you should buy your wine from here]
- Eat less (especially less meat) – read more about meat and the environment here
- “Select aluminum and steel packaging over glass or plastic, for energy conservation. For the same reasons, however, plastic and especially recyclable plastic should be selected instead of glass and/or paper.”
Pimentel et al. summarise: “[w]ell-directed, serious conservation strategies influenced by individuals with supportive state and federal leadership and policies will have an enormous positive impact on transitioning to a sustainable energy future for the United States.”
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Sunday, May 10th, 2009
Coupled Human and Natural Systems (CHANS) research is all about relationships – that seemed to be one of the main conclusions of the Challenges and Opportunities in Research on Complexity of Coupled Human and Natural Systems workshop at the US-IALE meeting in Snowbird, UT. The processes of identifying relationships between system elements and fostering them between researchers are key to realizing successful CHANS research. The workshop followed-up on a symposium in which principle investigators from several NSF-funded CNH projects presented their work, and was an opportunity to ask questions that went unasked during that symposium. The workshop was also the kick-off event for the CHANS-Net website.
In my notes below I have not identified individual workshop participants, both because I may have mis-interpreted their actual opinions or thoughts, but also because in some cases I can’t identify from my notes who said what. The workshop started with a panel discussion (the panel composed of the symposium speakers) followed by break-out groups to continue the discussion.
The first question from the audience asked how the panel approaches the dichotomy between abstract and contextualised research. Just as many dichotomies are false, it seems this one is also not always appropriate. For example, one response was that just because we can explain some characteristic about a specific place does not mean we didn’t use any theory whilst arriving at that explanation. Talking to local people can generate interesting, if contextualised, questions and one panel member highlighted the usefulness of ‘stakeholder steering groups’ (composed of local decision-makers and actors) to identify diverse opinions and direct research in ways that may not have happened otherwise. Another suggestion was that communication tools (such as role-playing, hypothetical scenarios, model output, etc.) are useful as a starting point for discussion, even if the theory underlying those tools is not discussed. To summarise the responses to this question I’ll paraphrase one of the panel members; ‘it was Louis Pasteur that said the question is not about whether the science is abstract or applied, but whether it is good science or bad science’.
A subsequent question along similar lines touched on the interplay of theory and practice; “what happens when your research proposal does not match ‘messy reality’? How do you explain why you ended up doing what you did do [to the people that accepted your proposal]?”. No original research goes entirely to plan – as some famous scientist once said; ‘if we knew what we were doing, it wouldn’t be called research’. In reality, there is always ‘wriggle-room’ in resolving this issue – if you start with a broad question it is easier to stay with a research theme even if the details get modified. Similarly, it is useful to make sure your research question is more important than the place where you will address that question. One panel member described how a research project they worked on needed to change the country in which is was situated. By focusing on the general research question they were able to negotiate this seemingly insurmountable problem. Other respondents from the panel got into more ‘messy’ details about the execution of such research. For example, in a project that involved both social and physical scientists there was initially confusion about how the two different types of scientists perceived and undertook measurements. A useful suggestion was to read your colleagues synthesis/review papers from other disciplines or backgrounds. Through commitment and patience in working together, an objective should be to identify a common language between researchers that can then push the research goals forward. Again, the importance of relationships was stressed.
An issue that came up both in response to this theory versus practice question, and frequently throughout the workshop, was the importance of good project management. One panel member suggested that an individual needs to be designated with the task of keeping the project on timeline, and that this person may need to take tough decisions (e.g. to drop researchers from the project) if deadlines or standards are not met. Finally, changing research can be a healthy thing – there will be frequent opportunities to extend research in new directions because new questions will arise as understanding develops. We shouldn’t be afraid to pursue those new directions.
One participant wanted to talk about fields that remain under-represented in CHANS current projects. They asked; “what about landscape architects and other ‘professional’ individuals?” A variety of missing experts and knowledge were suggested: the built environment, technology, environmental psychology, historians, political scientists, and communications experts (cartographers, public relations consultants, etc.) amongst others. The need for greater engagement and strengthening of relationships with political scientists seemed to be particularly important to several participants: under what conditions does a policy succeed or fail? How do we achieve good governance of the systems being studied? The US EPA (for example) are making decisions all the time – how are CHANS researchers engaging and influencing them?
Another workshop participant suggested that the presentations in the symposium had highlighted several different ways to conceive the relationship of humans with their environment, from ‘invaders’ to ‘managers’ to ‘components’. “How do we cross the boundaries between these different conceptualizations?” The first respondent suggested that researchers tend to pick a perspective (on the relationship between humans and their environment) and stick with it throughout their research – a better approach might be to consider different perspectives within the same project. However, the discussion quickly moved on to address the entire concept of ‘coupled’ human-natural systems. Several panel respondents voiced concerns about the coupling metaphor – one suggested that (human-natural) systems are not coupled, rather there is just one system. Another highlighted how the US perspective [remember this was the US-IALE meeting] on the human-nature relationship is rather unique – Europeans arrived with ideas of wilderness, protection and exploitation which differ from those in other places. Many of our ideas about how humans are related to their environment, one panel member suggested, likely stem from the Judeo-Christian philosophy which states that man was given dominion over nature. During the development of that philosophy humans got separated [in their minds?] from ecosystems and a difference soon emerged between a perspective in which humans rightly dominate nature versus one in which humans are viewed as being part of nature [which might be more consistent with Eastern religions such as Taoism or Buddhism].
To conclude the panel discussion someone asked; “what direction does CHANS research need to go in?” I thought the most interesting response was that CHANS research should be about easing transitions between different environmental conditions, and not trying to stop those transitions. The speaker suggested that CHANS research needs to focus on
the sustainability of communities in the face of environmental transitions, adopting a perspective closely aligned with the view that humans are a part of nature rather than a controller of nature. A second respondent (possibly a geographer) identified the problem of scale. Whilst pretty much every presentation in the symposium contained a ‘spider diagram’ depicting a system as arrows linking boxes of elements, scale didn’t figure much. Yet, the respondent argued, all the systems presented were to some degree scale-dependent (but note there are cases where scale-invariant behaviour is manifest [.pdf]).
The workshop then broke up into groups to discuss some the issues outlined above in more detail. Correspondingly, there was plenty of feedback when the groups re-convened. Put in the most simple terms, our group decided that there are four things that characterize CHANS research:
- It is hard (e.g. issues of coupling systems, scaling, policy work, management, interdisciplinarity, and many more)
- It’s all about relationships (both in the systems of study and between the researchers studying those systems)
- Face-to-face interaction is key (between researchers themselves, and between researchers and other stakeholders – policy makers, managers and importantly the people in the systems and places being studied)
- It takes time (because of all of the above)
This last point was emphasized in several places; it takes time to generate links between disciplines. And it can be frustrating. For CHANS research to be successful, one of the key steps is to identify individuals that are willing to make the same leap across a disciplinary divide that you want to. CHANS researchers aren’t alone in having these kinds of discussions right now, and there are lessons to be learned from many different groups investigating the web of human-environment relationships. That’s where the workshop ended in Utah, but no doubt the discussion about relationships will continue – possibly in forums like that offered by CHANS-Net.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
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Tuesday, May 5th, 2009
Right now I should be back in East Lansing after a week of fieldwork in our Michigan Upper Peninsula (the UP) study area. We’ve been in the UP this last week to finish up on our mesic conifer planting and white-tailed deer density fieldwork that I’ve written about previously. However, an incident with a deer has delayed us (see the bottom of this post) so I’m doing some data entry and writing in Marquette while our Jeep is repaired.
In previous posts about the fieldwork we’ve done in the UP, I have included photos from forest stands containing deciduous hardwood species such as Sugar Maple or American Beech. Generally, it’s understood that white-tailed deer browse juveniles trees in hardwood stands during the daytime in the winter, but shelter overnight in nearby lowland conifer stands. One of the aspects of our project is to identify some quantitative relationships for this behaviour, and so we’ve often had take measurements in the cedar swamps adjacent to northern hardwood stands.
As you can see from the picture above, the density of cedar swamps can make tree measurements a bit tricky. A standard measure of forest stand density (or stocking) is ‘stand basal area’ – a measure of the area occupied by tree stems (i.e. trunks) in a given area. The northern hardwood stands in our study area can have a stand basal area of anywhere between 60 and 100 square feet per acre. Cedar swamps are much more densely populated, with stand basal area values of 280 to 350 square feet per acre. An example of the transition between these stand types is shown in the picture below (click for a larger image).
The high density of the cedar swamps combined with continual cover provided by the evergreen canopy (generally) make winter snow depths lower and winter air temperatures higher compared with the deciduous hardwood stands. The soggy conditions underfoot make surveying cedar swamps even trickier – one has to hop from tree-root island to tree-root island over puddles whilst trying not to impale oneself on the lower branches. Even with care given enough time you’re guaranteed scratches and wet boots.
We’ve completed our fieldwork for now and are just waiting for our Jeep to be fixed after we hit a deer on our last day of work. With so many deer in the area and the high number of miles we drive around our study area, it was only a matter time before we hit one. We were on a major highway and the deer came out of nowhere. We’ve often spooked deer driving on tracks through the forest – it seems to me that when they’re startled they just bolt in whatever direction they happen to be facing at the time. Even if that means running across the road in front of your vehicle. As you can see below, it left quite a dent in the radiator. But Megan did a good job of keeping us on the road and thankfully the only casualty was the deer.
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Tuesday, March 24th, 2009
It took a while (first submitted late February 2008) but the manuscript I submitted with colleagues to Environmental Modelling and Software has now been accepted for publication. The paper describes the bio-physical component of the integrated socio-ecological simulation model I developed during my PhD. I don’t envision it changing it much so the abstract is copied below. When it’s in print I’ll holler again…
Modelling Mediterranean Landscape Succession-Disturbance Dynamics: A Landscape Fire-Succession Model
James D.A. Millington, John Wainwright, George L.W. Perry, Raul Romero-Calcerrada and Bruce D. Malamud
Abstract
We present a spatially explicit Landscape Fire Succession Model (LFSM) developed to represent Mediterranean Basin landscapes and capable of integrating modules and functions that explicitly represent human activity. Plant functional types are used to represent spatial and temporal competition for resources (water and light) in a rule-based modelling framework. Vegetation dynamics are represented using a rule-based community-level modelling approach that considers multiple succession pathways and vegetation ‘climax’ states. Wildfire behaviour is represented using a cellular automata model of fire spread that accounts for land-cover flammability, slope, wind and vegetation moisture. Results show that wildfire spread parameters have the greatest influence on two aspects of the model: land cover change and the wildfire regime. Such sensitivity highlights the importance of accurately parameterising this type of grid-based model for representing landscape-level processes. We use a ‘pattern-oriented modelling’ approach in conjunction with wildfire power-law frequency-area scaling exponent beta to calibrate the model. Parameters describing the role of soil moisture on vegetation dynamics are also found to significantly influence land-cover change. Recent improvements in understanding the role of soil moisture and wildfire fuel loads at the landscape-level will drive advances in Mediterranean LFSMs.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, Modelling, MyPhD, Publications, Wildfire | Comments Off
Friday, March 6th, 2009
Last Friday I was aiming to go to a seminar by Dr Michael Nelson entitled An Unprecedented Challenge: Environmental Ethics and Global Climate Change. Unfortunately time flies when you’re coding [our ecological-economic forest simulation model] and I missed it.
But I found a few bits and pieces on the MSU website that I assume are related. Like his recent article Abandon Hope in The Ecologist (written with <a href="
http://www.conservationethics.org/CEG/personnel.html” class=”regular” target=”_blank”>John Vucetich), and this associated MSU interview in which he outlines his argument:
[youtube=http://www.youtube.com/watch?v=D8-ZIbohRHM&hl=en&fs=1]
Even if they aren’t quite what was discussed on Friday, it’s still interesting stuff. Nelson’s argument is that if the only reason we have to live sustainably is the hope that environmental disaster will be averted, it’s unlikely that we’ll actually avert those disasters. Why? Because hope is a pretty weak argument when confronted by a continual news stream about how unsustainable western societies are and because many messages suggest disaster is inevitable.
It seems much of this argument stems from Nelson’s dissatisfaction with books like Jared Diamond’s Collapse which spends the vast majority of 500 pages discussing the demise of previous societies and what could go wrong now, then finishing with a 5 page section entitled Reasons for Hope.
Nelson’s dissatisfaction reminds me of William Cronon’s argument against the Grand Narratives of global environmental problems that I wrote about previously.
Cronon argued that global, ‘prophetic’ narratives are politically and socially inadequate because they don’t offer the possibility of individual or group action to address global problems. Such ‘big’ issues are hard for individuals to feel like they can do anything about.
Part of Cronon’s solution was the identification of ‘smaller’ (more focused) stories that individuals will be better able to empathise with. However, Cronon also played the hope card – suggesting that these more focused narratives offer individuals more hope than the global narratives.
Focusing on smaller issues closer to home may help – doesn’t hope become a stronger argument when the problems faced are less complex and the solutions are seemingly closer at hand? But Nelson seems to be suggesting that (as any ardent sports fan will tell you) it’s the hope that kills you.
“Instead of hope we need to provide young people with reasons to live sustainably that are rational and effective. We need to equate sustainable living, not so much with hope for a better future, but with basic virtues such as sharing and caring, which we already recognize as good in and of themselves, and not because of their measured consequences.”
Nelson’s is an ethical argument – that living sustainably should be portrayed as the ‘the right thing to do’, and that we should do it regardless of the consequences.
But then the question arises: how do we live sustainably? How do I know what the right thing to do is? Given a choice (on what printer paper to buy, for example) what decision to I make if I want to be sustainable? In order to make this choice we immediately need to start measuring the future consequences of our decisions. The future is an inherent part of the sustainability concept – it is about maintaining system processes or function into the future. So when we make our lifestyle decisions now, guided as they might be by the virtue of ‘doing the right thing’, we still need to have some idea about how we want the future to be, and which actions are more likely to get us there.
Nelson may be right – blind hope in a better future may prove counter productive given the current stream of global, prophetic, doomsaying narratives. But equally, just saying ‘do the right thing’ may be equally confusing for many people. Nelson isn’t arguing that this is all we should do, of course – he also suggests there is a “desperate need for environmental educators, writers, journalists and other leaders to work these [virtuous] ideas into their efforts”. It would be a good thing if living sustainably was more widely understood as ‘doing the right thing’. But this virtue will remain largely irrelevant if we don’t also work out how individuals and societies can live sustainably.
So what’s the result of all this thinking? It seems we should be focusing less on on doomsaying prophetic narratives (boiling seas bleaching coral reefs on continents thousands of miles away, stories of global warming when there’s a foot of snow outside, and so on) and more on what the individual person or group can do now, themselves, practically. In conjunction with the argument of acting virtuously with respect to sustainability, this focus may provide people with ‘rational and effective’ reasons, leaving them feeling more optimistic about the future and empowered to lead sustainable lives.
Update – 6th March
Okay, how about a couple of quick examples to go with that rhetoric? The cover story of this month’s National Geographic Magazine is a good one – Peter Miller looks at how we can start making energy savings (reducing CO2 emissions) around our own homes. And of course, I should have already pointed out the BBC’s Ethical Man as he works out how to keep his environmental impact to a minimum. Currently he’s attemting to traverse the USA without flying or driving. The ethics of Ethical Man are more implied than stated explicitly, but it’s another example of the sort of reporting is discussed above – showing how individuals can act now rather than merely hoping for a better future.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, Environmental, Political, Social, Sustainability | Comments Off
Friday, February 27th, 2009
February 2009 seems to be the month of abstracts. Here’s another we just submitted to the 94th Ecological Society of America Annual Meeting, the theme of which is Ecological Knowledge and a Global Sustainable Society.
Local winter white-tailed deer density: Effects of forest cover pattern, stand structure, and snow in a managed forest landscape
James D. A. Millington, Michael B. Walters, Megan S. Matonis and Jianguo Liu
Michigan State University
Background/Question/Methods
White-tailed deer (Odocoileus virginianus) are a ‘keystone herbivore’ with the potential to cause tree regeneration failure and greatly affect vegetation dynamics, stand structure and ecological function of forests across eastern North America. In northern mixed conifer-hardwood forests, local winter-time deer populations are dependent on habitat characterized by patterns of forest cover that provide shelter from snow and cold temperatures (lowland conifer stands) in close proximity to winter food (deciduous hardwood stands). Stand structure may also influence winter spatial deer distribution. Consequently, modification of forest cover patterns and stand structure by timber harvesting may affect local spatial deer distributions, with potential ecological and economic consequences. Here, we ask if forest cover pattern and stand structure, and their interactions with snow depth, can explain winter deer density in the managed forests of the central Upper Peninsula of Michigan, USA. For each local winter deer density estimate (from fecal pellet counts) we calculate stand-level characteristics for surrounding ‘landscapes of influence’ of radius 200 m and 380 m. For these data, and modeled snow depth estimates, we use multivariate techniques to produce predictive models and to identify the most important factors driving local deer densities across our 400,000 ha study area.
Results/Conclusions
Distance to the nearest conifer stand consistently explains the most variance in univariate regression models. Deer densities are highest near lowland conifer stands in areas where the proportion of hardwood forest-cover is high but the mean tree diameter-at-breast-height is low. Multiple regression models including these factors explain up to 38% of variance in deer density and have up to a 68% chance of correctly ranking a site’s deer density (relative to other sites within our study area). We are unable to conclusively show that snow depth has a significant impact on winter deer density, but our data suggest that more detailed investigation into the combined effect of distance to lowland conifer and snow depth may prove fruitful. Our results quantify clear effects of stand structure and forest cover composition on the winter spatial distribution of white-tailed deer. We briefly discuss how these results can be used in an ecological-economic simulation model of a managed forest for tree regeneration risk assessment. Use of these results, and the simulation model, will help identify management practices that can decrease deer impacts and ensure the ecological and economic sustainability of forests in which deer browse is proving problematic for tree regeneration.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, Environmental, Landscapes, MichiganUP | Comments Off
Wednesday, January 28th, 2009
On Monday several other members of the EE model research team and I met with foresters from Plum Creek and AFM to give them an overview of what we’ve been working on over the past year or so. Megan (Forestry Master’s student) and I gave them the lowdown on what we’ve been doing with regards fieldwork and analysis of the resulting data, Susan (Natural Resources Master’s student) spoke briefly about her work looking at factors influencing the prices of timber sales, and Mike (Forestry Prof.) was on hand to help paint the overall picture.
The foresters we spoke with were interested in our progress to date and asked for more details on tree species-specific patterns we find in our regeneration data so that they might work to continue the sustainability of their forest stands. Megan and are I are likely taking a trip to the study area again in late April to revisit a few sites from last spring and summer, so we’ll visit again then.
To get from one meeting to the other we drove through our study area. We wanted to see if we could find evidence of winter deer browse and generally get a feel for how the forests (and our study stands) look during the winter. We didn’t catch any deer in the act of browsing but, as the top picture below shows, we did see tracks and there were plenty of stunted maple saplings poking just above the snow nearby.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, Forests, MichiganUP, Photography | Comments Off
Saturday, January 10th, 2009
First week back in CSIS after the holiday and I got cracking with the winter white-tailed deer density paper we’re working. Understanding the winter spatial distribution of deer are important for the wider simulation modelling project we’re working on as the model needs to be able to estimate deer densities at each model timestep. We need to do this so that we might represent the impacts of deer on tree regeneration following timber harvest in the simulation model. The work the paper will present is using data from several sources:
- data we collected this summer regarding forest stand composition and structure,
- similar data kindly shared with us by the Michigan DNR,
- estimates of deer density derived from deer pellet counts we also made this year,
- other environmental data such as snow depth data from SNODAS.
Here’s my first stab at the opening paragraph (which will no doubt change before publication):
Spatial distributions of wildlife species in forest landscapes are known to be influenced by forest-cover composition and pattern. The influence of forest stand structure on the spatial distribution of wildlife is less well understood. However, understanding the spatial distribution of herbivorous ungulate species that modify vegetation regeneration dynamics is vital for forest managers entrusted with the goal of ensuring both ecological and economic sustainability of their forests. Feedbacks between timber harvest, landscape pattern, stand structure, and herbivore population density may lead to spatial variation in tree regeneration success. In this paper we explore how forest stand structure and landscape pattern, and their interactions with other environmental factors, can be used to predict and understand the winter spatial distribution of white-tailed deer (Odocoileus virginianus) during in the managed forests of the central Upper Peninsula (U.P.) of Michigan, USA.
I’ll update the status of the paper here periodically.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Ecological, MichiganUP, Modelling, Publications | Comments Off
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