Archive for the ‘Ecological’ Category

CHANS-Net

Thursday, December 11th, 2008

Towards the end of last week the MSU Environmental Science and Public Policy Program held a networking event on Coupled Human and Natural Systems (CHANS). These monthly events provide opportunities for networking around different environmental issues and last week was the turn of the area CSIS focuses on. The meeting reminded me of a couple of things I thought I would point out here.

First is the continued commitment that the National Science Foundation (NSF) is making to funding CHANS research. The third week in November will be the annual deadline for research proposals, so watch out for (particularly) tired looking professors around that time of year.

Second, I realized I haven’t highlighted on this blog one of the NSF CHANS projects currently underway at CSIS. CHANS-Net aims to develop an international network of research on CHANS to facilitate communication and collaboration among members of the CHANS research community. Central to the project is the establishment of an online meeting place for research collaboration. An early version of the website is currently in place but improvements are in the planning. I was asked for a few suggestions earlier this week and it made me realise how interested I am in the potential of the technologies that have arrived with web 2.0 (I suppose that interest is also clear right here in front of you on this blog). I hope to be able to continue to make suggestions and participate in the development of the site from afar (there’s too much to be doing elsewhere to get my hands really dirty on that project). Currently, only Principle Investigators (PIs) and Co-PIs on NSF funded CHANS projects are members of the network, but hopefully opportunities for wider participation will be available in the future. In that event, I’ll post again here.

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Anticipating Threats to Northern Hardwood Forest Biodiversity

Sunday, December 7th, 2008

Megan Matonis, one of the Masters students on the Michigan UP project, is headed to Washington D.C. for the National Council for Science and the Environment 9th National Conference on Science, Policy, and the Environment with a poster under her arm. Entitled Anticipating Threats to Northern Hardwood Forest Biodiversity with an Ecological-Economic Model the poster gives an overview of the modelling project and highlights some of the effects of deer browse and timber harvest on tree sapling and songbird diversity. Hopefully Megan will get some interesting questions and return with some new ideas about how we might use our model once it is up and running.

I haven’t posted on the blog for a little while. The main causes have been end of semester craziness and a trip to Montreal over Thanksgiving (maybe some pictures will appear on the photos page soon). More on CHANS research soon…

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Seeds and Quadtrees

Sunday, November 9th, 2008

The main reason I haven’t blogged much recently is because all my spare time has been taken up working on revisions to a paper submitted to Environmental Modelling and Software. Provisionally entitled ‘Modelling Mediterranean Landscape Succession-Disturbance Dynamics: A Landscape Fire-Succession Model’, the paper describes the biophysical component of the coupled human-natural systems model I started developing during my PhD studies. This biophysical component is a vegetation state-and-transition model combined with a cellular-automata to represent wildfire ignition and spread.

The reviewers of the paper wanted to see some changes to the seed dispersal mechanism in the model. Greene et al. compared three commonly used empirical seed dispersal functions and concluded that the log-normal distribution is generally the most suitable approximation to observed seed dispersal curves. However, dispersal functions using an exponential function have also been used. A good example is the LANDIS forest landscape simulation model that calculates the probability of seed fall (P) in a region between the effective (ED) and maximum (MD) seed distance from the seed source. For distances from the seed source (x) < ED, P = 0.95. For x > MD, P = 0.001. For all other distances P is calculated using the negative exponential distribution function is used as follows:
where b is a shape parameter.

Recently Syphard et al. modified LANDIS for use in the Mediterranean Type Environment of California. The two predominant pine species in our study area in the Mediterran Basin have different seed types: one (Pinus pinaster) has has wings and can fly large distances (~1km), but the other (Pinus pinea) does not. In this case a negative exponential distribution is most appropriate. However, research on the dispersal of acorns (from Quercus ilex) found that the distance distribution of acorns was best modeled by a log-normal distribution. I am currently experimenting with these two alternative seed dispersal distributions and comparing them with spatially random seed dispersal (dependent upon quantity but not locations of seed sources).

The main thing that has kept me occupied the last couple of weeks has been the implementation of these approaches in a manner that is computationally feasible. I need to run and test my model over several hundred (annual) timesteps for a landscape grid of data ~1,000,000 pixels. Keeping computation time down so that model execution does not take hundreds of hours is clearly important if sufficient model executions are to be run to ensure some form of statistical testing is possible. A brute-force iteration method was clearly not the best approach.

One of my co-authors suggested I look into the use of Quadtrees. Quadtrees are a tree data structure that are often used to partition a two dimensional space by recursively subdividing regions into quadrants (nodes). A region Quadtree partitions a region of interest into four equal quadrants. Each of these quadrants is subdivided into four subquadrants, each of which is subdivided and so on to the finest level of spatial resolution required. The University of Maryland have a nice Java applet example that helps illustrate the concept.

For our seed dispersal purposes, a region quadtree with n levels of may be used to represent an landscape of 2n × 2n pixels, where each pixel is assigned a value of 0 or 1 depending upon whether it contains a seed source of the given type or not. The distance of all landscape pixels to a seed source can then be quickly calculated using this data structure – staring at the top level we work our way down the tree querying whether each quadrant contains a pixel(s) that is a seed source. In this way, large areas of the grid can be discounted as not containing a seed source, thereby speeding the distance calculation.

Now that I have my QuadTree structure in place model execution time is much reduced and a reasonable number of model executions should be possible over the next month or so of model testing, calibration and use. My next steps are concerned with pinning down the appropriate values for ED and MD in the seed dispersal functions. This process of parameterization will take into account values previously used by similar models in similar situations (e.g. Syphard et al.) and empirical research and data on species found within our study area (e.g. Pons and Pausas). The key thing to keep in mind with these latter studies is their focus on the distribution of individual seeds from individual trees – the spatial resolution of my model is 30m (i.e. each pixel is 30m square). Some translation of values for individuals versus aggregated representation of individuals (in pixels) will likely be required. Hopefully, you’ll see the results in print early next year.

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Regional partitioning for wildfire regime characterization

Saturday, August 2nd, 2008

Fighting wildfires is a strategic operation. In fire-prone areas of the world, such as California and the Mediterranean Basin, it is important that managers allocate and position fire trucks, water bombers and human fire-fighters in locations that minimize the response time to reach new fires. Not only is this important to reduce risk to human lives and livelihoods, the financial demands of fighting a prolonged campaign against multiple fires demands that resources be used as economically as possible.

Characterizing the wildfire regime of an area (the frequency, timing and magnitude of all fires) can be very useful for this sort of planning. If an area burns more frequently, or with greater intensity, on average, fire-fighting resources might be better placed in or near these areas. The relationship between the frequency of fires and the area they burn is one the characteristics that is particularly interesting from this perspective.

As I’ve written about previously with colleagues, although it is well accepted that the frequency-area distribution of wildfires is ‘heavy-tailed’ (i.e. there are many, many more small fires than large fires), the exact nature of this distribution is still debated. One of the distributions that is frequently used is the power-law distribution. Along with my former advisors Bruce Malamud and George Perry, I examined how this characteristic of the wildfire regime, the power-law frequency-area distribution, varied for different regions across the continental USA (see Malamud et al. 2005). Starting with previously defined ‘ecoregions’ (area with characterized by similar vegetation, climate and topography) we mapped how the frequency-area relationship varied in space, finding a systematic change from east to west across the country.

More recently, Paolo Fiorucci and colleagues (Fiorucci et al. 2008) have taken a slightly different approach. Rather than starting with pre-defined spatial regions and calculating the frequency-area distribution of all the fires in each region, they have devised a method that splits a large area into smaller regions based on the wildfire data for the entire area. Thus, they use the data to define the spatial differentiation of regions with similar wildfire regime characteristics a posteriori rather than imposing the spatial differentiation a priori.

Fiorucci and his colleagues apply their method to a dataset of 6,201 fires (each with an area greater than 0.01 sq km) that burned between 1987 and 2004 in the Liguria region of Italy (5400 sq km). They show that estimates of a measure of the wildfire frequency-area relationship (in this case the power-law distribution) of a given area varies significantly depending on how regions within that area are partitioned spatially. Furthermore, they found differences in spatial patterns of the frequency-area relationship between climatic seasons.

Using both a priori (the approach of Malamud et al. 2005) and a posteriori (the approach of Fiorucci et al. 2008) spatial delineation of wildfire regime areas, whilst simultaneously considering patterns in the processes believed to be driving wildfire regimes (such as climate, vegetation and topography), will lead to better understanding of wildfire regimes. That is, future research in this area will be well advised to look at the problem of wildfire regime characterization from both perspectives – data-driven and process-driven. The approach developed by Fiorucci et al. also provide much promise for a more rigorous, data-driven, approach to make decisions about the allocation and positioning of wildfire fire-fighting resources.

Citation and Abstract
Fiorucci, P., F. Gaetani, and R. Minciardi (2008) Regional partitioning for wildfire regime characterization, Journal of Geophysical Research, 113, F02013
doi:10.1029/2007JF000771

Wildfire regime characterization is an important issue for wildfire managers especially in densely populated areas where fires threaten communities and property. The ability to partition a region by articulating differences in timing, frequency, and intensity of the phenomena among different zones allows wildfire managers to allocate and position resources in order to minimize wildfire risk. Here we investigate “wildfire regimes” in areas where the ecoregions are difficult to identify because of their variability and human impact. Several studies have asserted that wildfire frequency-area relationships follow a power law distribution. However, this power law distribution, or any heavy-tailed distribution, may represent a set of wildfires over a certain region only because of the data aggregation process. We present an aggregation procedure for the selection of homogeneous zones for wildfire characterization and test the procedure using a case study in Liguria on the northwest coast of Italy. The results show that the estimation of the power law parameters provides significantly different results depending on the way the area is partitioned into its various components. These finds also show that it is possible to discriminate between different wildfire regimes characterizing different zones. The proposed procedure has significant implications for the identification of ecoregion variability, putting it in a more mathematical basis.

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US-IALE 2009: Coupling Humans and Complex Ecological Landscapes

Thursday, July 31st, 2008

Coupling Humans and Complex Ecological Landscapes is the theme of the 2009 annual conference of US-IALE (U.S. Regional Association, International Association for Landscape Ecology). The conference will be held in Snowbird, Utah, from April 12-16, 2009. Proposals for symposia and workshops are due September 15, 2008; and abstracts are due November 17, 2008.

Several types of financial support for attending and presenting at the conference are available:

(1) the “Sponsored Student Travel Awards Program” of local sponsors (USGS, Utah State University, and Utah Department of Natural Resources),

(2) US-IALE’s ‘Foreign Scholar Travel Award‘ Program,

(3) the ‘NASA-MSU Professional Enhancement Awards Program‘ (supported by NASA and Michigan State University), and

(4) the ‘CHANS Fellows Program’ of the new International Network of Research on Coupled Human and Natural Systems (CHANS-Net, supported by NSF, see background papers in Science and Ambio).

US-IALE conferences are particularly students-friendly, with two popular programs — Lunch with Mentors and NASA-MSU dinner, and a new program — We’ll “Pick Up The Tab!”.

More information about the conference is available from the web site.

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Creating a Genuine Science of Sustainability

Saturday, June 21st, 2008

Previously, I wrote about Orrin Pilkey and Linda Pilkey-Jarvis’ book, Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future. In a recent issue of the journal Futures, Jerome Ravetz reviews their book alongside David Waltner-Toews’ The Chickens Fight Back: Pandemic Panics and Deadly Diseases That Jump From Animals to Humans. Ravetz himself points out that the subject matter and approaches of the books are rather different, but suggests that “Read together, they provide insights about what needs to be done for the creation of a genuine science of sustainability”.

Ravetz (along with Silvio Funtowicz) has developed the idea of ‘post-normal’ science – a new approach to replace the reductionist, analytic worldview of ‘normal’ science. Post-normal science is a “systemic, synthetic and humanistic” approach, useful in cases where “facts are uncertain, values in dispute, stakes high and decisions urgent”. I used some of these ideas to experiment with some alternative model assessment criteria for the socio-ecological simulation model I developed during my PhD studies. Ravetz’s perspectives toward modelling, and science in general, shone through quite clearly in his review:

“On the philosophical side, the corruption of computer models can be understood as the consequence of a false metaphysics. Following on from the prophetic teachings of Galileo and Descartes, we have been taught to believe that Science is the sole and certain path to truth. And this Science is mathematical, using quantitative data and abstract reasonings. Such a science is not merely necessary for achieving genuine knowledge (an arguable position) but is also sufficient. We are all victims of the fantasy that once we have numerical data and mathematical argument (or computer programs), truth will inevitably follow. The evil consequences of this philosophy are quite familiar in neo-classical economics where partly true banalities about markets are dressed up in the language of the differential calculus to produce justifications for every sort of expropriation of the weak and vulnerable. ‘What you can’t count, doesn’t count’ sums it all up neatly. In the present case, the rule of models extends over nearly all the policy-relevant sciences, including those ostensibly devoted to the protection of the health of people and the environment.

We badly need an effective critical philosophy of mathematical science. … Now science has replaced religion as the foundation of our established order, and in it mathematical science reigns supreme. Systematic philosophical criticism is hard to find. (The late Imre Lakatos did pioneering work in the criticism of the dogmatism of ‘modern’ abstract mathematics but did not focus on the obscurities at the foundations of mathematical thinking.) Up to now, mathematical freethinking is mainly confined to the craftsmen, with their jokes of the ‘Murphy’s Law’ sort, best expressed in the acronym GIGO (Garbage In, Garbage Out). And where criticism is absent, corruption of all sorts, both deliberate and unaware, is bound to follow. Pseudo-mathematical reasonings about the unthinkable helped to bring us to the brink of nuclear annihilation a half-century ago. The GIGO sciences of computer models may well distract us now from a sane approach to coping with the many environmental problems we now face. The Pilkeys have done us a great service in providing cogent examples of the situation, and indicating some practical ways forward.”

Thus, Ravetz finds a little more value in the Useless Arithmetic book than I did. But equally, he highlights that the Pilkeys offer few, rather vague, solutions and instead turns to Waltner-Toews’ book for inspiration for the future:

Pilkey’s analysis of the corruptions of misconceived reductionist science shows us the depth of the problem. Waltner-Toews’ narrative about ourselves in our natural context (not always benign!) indicates the way to a solution.”

Using the outbreak of avian flu as an example of how to tackle complex environmental in the ‘risk society’ in which we now live, Waltner-Toews:

“… makes it very plain that we will never ‘conquer’ disease. Considering just a single sort of disease, the ‘zoonoses’ (deriving from animals), he becomes a raconteur of bio-social-cultural medicine …

What everyone learned, or should have learned, from the avian flu episode is that disease is a very complex entity. Judging from TV adverts for antiseptics, we still believe that the natural state of things is to be germ-free, and all we need to do is to find the germs and kill them. In certain limiting cases, this is a useful approximation to the truth, as in the case of infections of hospitals. But even there complexity intrudes … “

Complexity which demands an alternative perspective that moves beyond the next stage of ‘normal’ science to a post-normal science (to play on Kuhn’s vocabulary of paradigm shifts):

“That old simple ‘kill the germ’ theory may now be derided by medical authorities as something for the uneducated public and their media. But the practice of environmental medicine has not caught up with these new insights.

The complexity of zoonoses reflects the character of our interaction with all those myriads of other species. … the creatures putting us at risk are not always large enough to be fenced off and kept at a safe distance. … We can do all sorts of things to control our interactions with them, but one thing is impossible: to stamp them out, or even to kill the bad ones and keep the good ones.

Waltner-Toews is quite clear about the message, and about the sort of science that will be required, not merely for coexisting with zoonoses but also for sustainable living in general. Playing the philological game, he reminds us that the ancient Indo-European world for earth, dgghem, gave us, along with ‘humus’, all of ‘human’, ‘humane’ and ‘humble’. As he says, community by community, there is a new global vision emerging whose beauty and complexity and mystery we can now explore thanks to all our scientific tools.”

This global vision is a post-normal vision. It applies to far more than just avian flu – from coastal erosion and the disposal of toxic or radioactive waste (as the Pilekys discuss for example) to climate change. This post-normal vision focuses on uncertainty, value loading, and a plurality of legitimate perspectives that demands an “extended peer community” to evaluate the knowledge generated and decisions proposed.

In all fairness, it would not be easy to devise a conventional science-based curriculum in which Waltner-Toews’ insights could be effectively conveyed. For his vision of zoonoses is one of complexity, intimacy and contingency. To grasp it, one needs to have imagination, breadth of vision and humility, not qualities fostered in standard academic training. … “

This post-normal science won’t be easy and won’t be learned or fostered entirely within the esoteric confines of an ivory tower. Science, with its logical rigour, is important. It is still the best game in town. But the knowledge produced by ‘normal’ science is provisional and its march toward truth is seemingly Sisyphean when confronted faced with the immediacy of complex contemporary environmental problems. To contribute to the production a sustainable future, a genuine science of sustainability would do well to adopt a more post-normal stance toward its subject.

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Model Types for Ecological Modelling

Friday, June 13th, 2008

Sven Erik Jørgensen introduces a recent issue of Ecological Modelling that presents selected papers from the International Conference on Ecological Modelling in Yamaguchi, Japan (28 August – 1 September 2006). The paper provides an overview of the model types available for ecological modelling, briefly highlighting the shift from a dominance of bio-geo-chemical dynamic models and population dynamics models in the 1970s toward the application of a wider spectrum of models. The emergence of new model types has come as a response to questions such as:

  • How can we describe the spatial distribution which is often crucial to understand ecosystem reactions?
  • How do we model middle number systems?
  • How do we model hetergenous populations and databases (e.g. observations from many different ecosystems)?
  • How do we model ecosystems, when our knowledge is mainly based on a number of rules/properties/propositions?

Jørgensen suggests there are at least 10 types of model currently available for modelling ecological systems (purely mathematical and statistical aside):

  1. (Bio-geo-chemical and bio-energetics), dynamic models
  2. Static models
  3. Population dynamic models
  4. Structurally dynamic models
  5. Fuzzy models
  6. Artificial neural networks
  7. Individual-based models and cellular automata
  8. Spatial models
  9. Ecotoxicological models
  10. Stochastic models
  11. Hybrid models

Of these, my particular interest is in spatial models, individual-based models and cellular automata models (with a passing interest in population models). This is largely because of my background in geography and landscape ecology, but also because of the heterogeneity in patterns, processes and behaviour often exhibited in socio-ecological systems.

Jørgensen offers a short description of each type, before listing their advantages and disadvantages. Here are a couple with my comments in italics:

Individual-Based Models (IBMs)and Cellular Automata (CA)
First, counter to Jørgensen, I would argue that CA models should be placed with the ‘spatial models’ – the ability of CA to represent space for me outweighs their potential to represent (limited) heterogeneity between cells. This aside, their grouping does make sense when we consider that these models can be relatively easily combined to represent individuals’ interactions across space and with a heterogeneous environment (via the CA).

Advantages

  • Are able to account for individuality – agreed, especially for IBMs
  • Are able to account for adaptation within the spectrum of properties – yes
  • Software is available; although the choice is more limited than by bio-geo-chemical dynamic models – but excellent free modelling environments such as NetLogo make this type of modelling widely available
  • Spatial distribution can be covered – yes

Disadvantages

  • If many properties are considered, the models get very complex – and may require the adoption and development of new techniques to present/analyse/interpret output (e.g. POM, narratives)
  • Can be used to cover the individuality of populations; but they cannot cover mass and energy transfer based on the conservation principle – I see no reason why the principle of energy and mass conservation could not be achieved by models of these types
  • Require many data to calibrate and validate the models – yes, this often the case, and in some cases (again) may require new approaches and types of data to calibrate and evaluate models

Spatial Models
Advantages

  • Cover spatial distribution, that is often of importance in ecology – yes, particularly Landscape Ecology, an entire discipline that has arisen since the 1970s and ’80s
  • The results can be presented in many informative ways, for instance GIS – GIS is a means to organise and analyse data as well as present data

Disadvantages

  • Require usually a huge database, giving information about the spatial distribution – this can certainly give rise to the issue of ‘model but no data’ and increases the costs of performing ecological research by adding space to time. We have found that our large (~4,000 sq km) Upper Michigan study area demands high time and resources needed for data collection.
  • Calibration and validation are difficult and time-consuming – maybe more so than non-spatial models, but probably not as much as some individual-based models
  • A very complex model is usually needed to give a proper description of the spatial patterns – not necessarily. A model should be only as complex as the patterns and processes it seeks to examine and the inclusion of space does not imply patterns or processes any more complex than a system with less variables or interactions that is non-spatial.

This isn’t a bad review of the types of ecological modelling being done. However, more incisive and useful insight could have been made with respect to landscape ecology and those models that are now beginning to attempt to account for human activity in ecological systems. [And it definitely could have been better written.] Maybe I’ll stop criticising sometime and write one myself eh?

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Aldo Leopold Legacy Center – The 'Greenest Building in the US'

Sunday, June 1st, 2008

One of the fieldtrips we took during the US-IALE conference in Madison was to Aldo Leopold’s shack and the Aldo Leopold Legacy Center. Aldo Leopold is considered by many to be the ‘father’ of wildlife management. His significant and lasting mark is his book, A Sand County Almanac. I’ll look at the book in later post, but here I’ll talk briefly about what we saw on our excursion from Madison.

After graduating from the Yale Forest School in 1909, Aldo Leopold spent time working in Arizona and New Mexico before moving to Madison, Wisconsin, in 1924. In 1933 he published the first wildlife management textbook and accepted a new chair in game management at the University of Wisconsin – a first for both the university and the nation.

In 1935, Leopold and his family initiated their own ecological restoration experiment on a washed-out sand farm of 120 acres along the Wisconsin River near Baraboo, Wisconsin. Planting thousands of pine trees, restoring prairies, and documenting the ensuing changes in the flora and fauna informed and inspired Leopold. Many of his writings in the initial parts of A Sand County Almanac – the history of the local region as told through the rings of an oak tree, evening shows of sky dancing woodcock, fishing the Alder Fork, hunting ruffled-grouse in smoky gold tamarack – were penned in ‘the shack’ (above) on his farm which we stopped by at on a wet, grey day after visiting The Aldo Leopold Legacy Center (below).

In sharp contrast to ‘the shack’ the Legacy Center feels solid and dry. But consistent with the Land Ethic message of the writing that was done in the old dilapidated building, the new building ‘sustains the health, wildness, and productivity of the land, locally and globally‘. The Legacy Center has received Platinum Leadership in Energy and Environment Design (LEED) Certification from the U.S. Green Building Council and is currently the ‘greenest building in the U.S.’.

The Legacy Center is an example of how we can use energy more efficiently and construct building with a limited impact on our environment. Through energy efficiency, renewable energy, the Legacy Center is the first carbon neutral building certified by LEED — annual operations account for no net gain in carbon dioxide emissions.

The Legacy Center is also a net zero energy building, using 70 percent less energy than a building built just to code and meeting all of its energy needs on site using tools like a roof-mounted solar array and a ‘thermal flux zone’ to reduce heat flow between interior rooms and the outdoors. Many of the structural columns, beams, and trusses, as well as interior panelling and finish work, are from the pine trees Leopold planted himself on his farm between 1935-1948.

This really is a building that embodies Leopold’s Land Ethic – both conceptually through the principles used when designing the building, and physically by using material from Leopold’s own ecological restoration experiment. The Legacy Center contains the offices of the Leopold Foundation, has a small shop and ‘museum’ about Leopold, and can be hired for meetings. The building itself is what is really the attraction – and hopeully there will be more like this appearing more frequently elsewhere. Unless you’re passing by or really want to make a pilgrimage to gain an insight into the area where Leopold’s vision unfolded, there’s really no need to go out of your way to visit. Take a virtual tour instead to save energy and carbon and make the building even greener.

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Columbia University Press Sale

Wednesday, May 21st, 2008


Columbia University Press currently has a sale on. They have savings of up to 80% on more than 1,000 titles from several fields of study. I was particularly interested in their books in the Environmental Studies and Ecology section and purchased several:

Previously on this blog I reviewed another book they have on sale, Useless Arithmetic: Why Environmental Scientists Can’t Predict the Future by Orrin H. Pilkey and Linda Pilkey-Jarvis.

When I get round to reading this new batch I’ll review some of these also (at first glance the Wiens et al. book looks particularly useful for any Landscape Ecologist – student, teacher or researcher). You’ve got up until May 31st to order yours.

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Michigan UP Seedling Experiment

Friday, May 16th, 2008

I’ve been back from our study area in Michigan’s Upper Peninsula for over a week so it’s about time I posted something about what we were doing up there.

One of the main issues we will study with our integrated ecological-economic landscape model is the impact of whitetail deer (Odocoileus virginianus) herbivory on tree regeneration following cutting. Last November we spent a week planting 2 year-old seedlings in Northern Hardwood forest gaps created by selective timber harvest (like the one in the photo below).

Our plan was to return this spring to examine the impacts of deer browse on these seedlings. In particular, we wanted to examine how herbivory varies across space due to changes in deer population densities (in turn driven by factors such as snow depth).

To this end we selected almost 40 forest sites that would hopefully capture some spatial variation in snowfall and that had recently been selectively harvested. At each site we selected 10 gaps produced by timber harvest in which to plant our seedlings.

In each gap we planted six trees of each of three species: White Spruce (Picea glauca), White Pine (Pinus strobus) and Eastern Hemlock (Tsuga canadensis). We chose these coniferous species as these are examples of the mesic confer species the Michigan DNR are trying to restore across our study area, and because we expected a range of herbivory across these species.

At each site we would also undertake deer pellet counts in the spring to estimate the number of deer in the vicinity of the site during the winter (during which time the browse we were measuring would have occurred).

On returning to the study sites a couple of weeks ago we set about looking for the trees we had planted to measure herbivory and count deer pellets. In some cases, finding the trees we planted was easier said than done. We tried to get our field crews to plant the trees in straight lines with equal spacing between each tree. In general, this was done well but on occasion the line could only be described as crooked at best. Micro-topography, fallen tree trunks and limbs, and slash from previous cutting all contributed to hamper the planned planting system. However, we did pretty well and found well over 90% of the trees.

We haven’t begun analyzing our data as yet, but some anecdotal observations stand out. First, the deer preferentially browsed Hemlock over the other species, often removing virtually all non-woody biomass as shown by the ‘before and after’ examples below (NB – these photographs are not of the same tree and this is not a true before/after comparison).

In some cases, the deer not only removed all non-woody biomass but also pulled the tree out of the ground (as shown below).

In contrast, White Pine was browsed to a much lesser extent and White Spruce was virtually untouched (as shown below).

Having a species that was unaffected by deer (i.e. spruce) often made our job of finding the other trees much easier. Finding heavily browsed Hemlock that no longer had any green vegetation was often tricky against a background of forest floor litter.

The next step now is to start looking at this variation in browse through a more quantitative lens. Then we can start examining how browse and deer densities vary across space and how these variables are related to one another and other factors (such as snow depth and distance to conifer stands).

All-in-all the two weeks of work went pretty well. There were some issues with water-logged roads (due to snow melt) meaning we couldn’t get to one or two of the sites we planted at, but generally the weather was pretty good (it only rained heavily one day). I’ll write more once we have done more analysis and stop here with a shot I took at sunrise as I left for home.

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