|
Archive for the ‘Forests’ Category
Tuesday, November 17th, 2009
It’s taken a while but finally the model that I came to Michigan State to develop is producing what seems to be sensible output. Just recently we’ve brought all the analyses on the data that were collected in the field into a coherent whole. We’ll use this integrated model to investigate best approaches for forest and wildlife management to ensure ecological and economic sustainability. This post is a quick overview of what we’ve got at the moment and where we might take it. The image below provides a simplified view of the relationship of the primary components the model considers (a more detailed diagram is here).
The main model components I’ve been working on are the deer distribution, forest gap regeneration and tree growth and harvest sub-models. Right now we’re still in the model testing and verification stage but soon we hope to be able start putting it to use. Here’s a flow chart representing the current sequence of model execution (click for larger image):
As I’ve posted several times about the deer distribution modelling (here, here, and here for example) and because the integration of FVS with our analyses is more a technical than scientific issue, I’ll focus on the forest gap regeneration sub-model.
Most of the forest gap regeneration analyses used the data Megan Matonis collected during her two summers in the field (i.e., forest). During her fieldwork Megan measured gap and tree regeneration attributes such as gap size, soil and moisture regime, time since harvest, deer density, and sapling heights, density and species composition. Megan is writing up her thesis right now but we’ve also managed to find time to do some extra analyses on her data for the gap regeneration sub-model. Here’s the flow chart representing the model sequence to estimate initial regeneration in gaps created by a selection harvest in a forest stand (click for larger image):
In our gap regeneration sub-model we take a probabilistic approach to estimate the number and species of the first trees to reach 7m (this is the height at which we pass the trees to FVS to grow). The interesting equations for this are Eqs. 6 – 9 as they are responsible for estimating regeneration stocking (i.e. number of trees that regenerate) and the species composition of the regenerating trees. Through time the effects of the results of these equations will drive future forest composition and structure and the amount of standing timber available for harvest.
The probability that any trees regenerate in a gap is modelled using a generalized linear mixed model with a stand-level random intercept drawn from a normal distribution. The probability is a function of canopy gap area and deer browse category (high or low; calculated as a function of deer density in the stand).
If there are some regenerating trees in the gap, we use a logistic regression to calculate the probability that the gap contains as many (or more) trees as could fit in the gap when all the trees are 7m (and is therefore ‘fully stocked’). The probability is a function of canopy openness (calculated as a function of canopy gap area), soil moisture and nutrient conditions and deer density. If the gap is not fully stocked we sample the number of trees using from a uniform distribution.
Finally, we assign each tree to a species by estimating the relative species composition of the gap. We do this by assuming there are four possible species mixes (derived from our empirical data) and we use a logistic regression to calculate the probability that the gap has each of these four mixes. The probability of each mix is a function of soil moisture and nutrient conditions, canopy gap area, and stand-level basal area of Sugar Maple Ironwood. Currently we have parameterised the model to represent five species (Sugar Maple, Red Maple, White Ash, Black Cherry and Ironwood).
As the flow chart suggests, there is a little more to it than these three equations alone but hopefully this gives you a general idea about how we’ve approached this and what the important variables are (look out for publications in the future with all the gory details). For example, at subsequent time-steps in the simulation model we grow the regenerating trees until they reach 7m and also represent the coalescence of the canopy gaps. I haven’t integrated the economic sub-model into the program yet but that’s the next step.
So what can we use the model for? One question we might use the model to address is, ‘how does change in the deer population influence northern hardwood regeneration, timber revenue and deer hunting value?’ For example, in one set of initial model runs I varied the deer population to test how it affects regeneration success (defined as the number of trees that regenerate as a percentage of the maximum possible). Here’s a plot that shows how regeneration success decreases with increasing deer population (as we would expect given the model structure):
Because we are linking the ecological sub-models with economic analyses we can look at how these differences will play out through time to examine potential tradeoffs between ecological and economic values. For example, because we know (from our analyses) how the spatial arrangement of forest characteristics influences deer distribution we can estimate how different forest management approaches in different locations influences regeneration through time. The idea is that if we can reduce deer numbers in a given area immediately after timber harvest we can give trees a chance to survive and grow above the reach of deer – moving deer spatially does not necessarily mean reducing the total population (which would reduce hunting opportunities, an important part of the local economy). The outcomes may look something like this:
We plan to use our model to examine scenarios like this quantitatively. But first, I need to finish testing the model…
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, Modelling | Comments Off
Thursday, October 15th, 2009
When testing and using simulation models we often need to use synthetic data. This might be because we want to examine the effects of different initial conditions on our model output, or simply because we have insufficient data to examine a system at the scale we would like to. The ecological-economic modelling project I’m currently working on is in both these situations, and over the last week or two I’ve been working on generating synthetic tree-level data so that we can initialize our model of forest stand change for testing and scenario development. Here’s a brief overview of how I’ve approached the task of producing a ‘treelist generator’ from the empirical data we have for over 60,000 trees in Northern Hardwood stands across Upper Michigan.
One of the key measures we can use to characterise forest stands is basal area (BA). We can assume that for each stand we generate a treelist for there is some ‘target BA’ that we are aiming to produce. As well as hitting a target BA, we also need to make sure that the tree diameter-at-breast-height (DBH) size-class distribution and species composition are representative of the stands in our empirical data. Therefore, our the first step is to look at the diameter size-class distribution of the stands we want to emulate. We can do this by plotting histograms of the frequency of trees of different diameter for each stand. In the empirical data we see two characteristic distributions (Fig 1).
Fig 1. Example stand tree count histograms
The distribution on the left has very many more trees in the smaller size classes as a result of stands self-thinning (as larger trees compete for finite resources). The second distribution, in which the smallest size classes are under-represented and larger size classes have relatively more trees, does not fit so well with the theoretical, self-thinning DBH size-class distribution. Stands with a distribution like this have probably been influenced by other factors (for example deer browse on the smaller trees). However, it turns out that both these DBH size-class distributions can be pretty well described by the gamma probability distribution (Fig 2).
Fig 2. Example stand gamma probability distributions for data in Fig 1
The gamma distribution has two parameters, a shape parameter we will call alpha and a scale parameter we will call beta. Interestingly, in the stands I examined (dominated by Sugar Maple and Ironwood) there are two different linear relationships between the parameters. The relationship between alpha and beta for 80% of stands represents the ‘self-thinning’ distribution, and the other 20% represent distributions in which small DBH classes are under-represented. We use these relationships – along with the fact that the range of values of alpha for all stands has a log-normal distribution – to generate characteristic DBH size-class distributions;
- sample a value of alpha from a normal distribution (subsequently reconvert using 10alpha),
- for the two different relationships use Bayesian linear regression to find mean and 95% credible intervals for the slope and intercept of a regression line between alpha and beta,
- use the value of alpha with the regression parameters to produce a value of beta.
So now for each stand we have a target basal area, and parameters for the DBH size class distribution. The next step is to add trees to the stand with diameters specified by the probability distribution. Each time we add a tree, basal area is added to the stand. The basal area for a tree is calculated by:
TreeBA = TreeDensity * (0.005454* diameter2)
[Tree density can be calculated for each tree because we know the sampling strategy used to collect empirical data on our timber cruise, whether on a fixed area plot, n-tree or with a prism].
Once we get within 1% of our target BA we stop adding trees to the stand [we'll satisfy ourselves with a 1% accuracy because the size of tree that we allocate each time is sampled from a probability distribution and so we it is unlikely we will be able to hit our target exactly]. The trees in our (synthetic) stand should now (theoretically) have the appropriate DBH size-class distribution and basal area.
With a number of trees in now in our synthetic stand, each with a DBH value, the next step is to assign each tree to a species so that the stand has a representative species composition. For now, the two species we are primarily interested in are Sugar Maple and Ironwood. However, we will also allow trees in our stands to be Red Maple, White Ash, Black Cherry or ‘other’ (these are the next most common species in stands dominated by Sugar Maple and Ironwood). First we estimate the proportion of the trees in each species. In stands with Sugar Maple and Ironwood deer selectively browse Sugar Maple, allowing Ironwood a competitive advantage. Correspondingly, in the empirical data we observe a strong linear and inverse relationship between the abundance of Sugar Maple and Ironwood (Fig 3).
Fig 3. Relationship between stand Sugar Maple and Ironwood abundance
To assign species proportions we first estimate the proportion of Sugar Maple from the empirical data. Next, using the strong inverse relationship above we estimate the corresponding proportion of Ironwood (sampled using normal distribution with mean and standard deviation from from Bayesian linear regression). The remaining species proportions are assigned according to the frequency of their presence in the empirical data.
Now we use these proportions to assign a species to individual trees. Because physiology varies between species, the probability that a tree is of a given size also varies between species. For example, Ironwood very seldom reach DBH greater than 25 cm and the vast majority (almost 99% in our data) are smaller than 7.6 cm (3 inches) in diameter. Consequently, first we assign the appropriate number Ironwood to trees according to their empirical size-class distribution, before then assigning all other trees to the remaining species (using a uniform distribution).
The final step in generating our treelist is to assign each tree a height and a canopy ratio. We do this using empirical relationships between diameter and height
for each species that are available in the literature (e.g. Pacala et al. 1994). And we’re done!
In the model I’m developing, these stands can be assigned a spatial location either using a pre-existing empirical map or using a synthetic land cover map with known characteristics (generated for example using the modified random clusters method, as the SIMMAP 2.0 software does). In either case we can now run the model multiple times to investigate the dynamics and consequences of different initial conditions. More on that in the 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 Forests, MichiganUP, Modelling, Statistical | Comments Off
Wednesday, August 19th, 2009
Experimentation can be tricky for landscape ecologists, especially if we’re considering landscapes at the human scale (it’s a bit easier at the beetle scale [pdf]). The logistic constraints of studies at large spatial and temporal scales mean we frequently use models and modelling. However, every-now-and-then certain events afford us the opportunity for a ‘natural experiment’ – situations that are not controlled by an experimenter but approximate controlled experimental conditions. In her opening plenary at ESA 2009, Prof. Monica Turner used one such natural experiment – the Yellowstone fires of 1988 – as an exemple to discuss how disturbance affects landscape dynamics and ecosystem processes. Although this is a great example for landscapes with limited human activity, it is not such a useful tool for considering human-dominated landscapes.
 Landsat satellite image of the Yellowstone fires on 23rd August 1988. The image is approximately 50 miles (80 km) across and shows light from the green, short-wave infrared, and near infrared bands of the spectrum. The fires glow bright pink, recently burned land is dark red, and smoke is light blue.
Before getting into the details, one of the first things Turner did was to define disturbance (drawing largely on Pickett and White) and an idea that she views as critical to landscape dynamics – the shifting mosaic steady state. The shifting mosaic steady state, as described by Borman and Likens, is a product of the processes of vegetation disturbance and succession. Although these processes mean that vegetation will change through time at individual points, when measured over a larger area the proportion of the landscape in each seral stage (of succession) remains relatively constant. Consequently, over large areas and long time intervals the landscape can be considered to be in equilibrium (but this isn’t necessarily always the case).
Other key ideas Turner emphasised were:
- disturbance is a key component in ecosystems across many scales,
- disturbance regimes are changing rapidly but the effects are difficult to predict,
- disturbance and heterogeneity have reciprocal effects.
Landscape Dynamics
In contrast to what you might expect, very large disturbances generally increase landscape heterogeneity. For example, the 1988 Yellowstone fires burned 1/3 of the park in all forest types and ages but burn severity varied spatially. Turner highlighted that environmental thresholds may determine whether landscape pattern constrains fire spread. For instance, in very dry years spatial pattern will likely have less effect than years where rainfall has produced greater spatial variation in fuel conditions.
Turner and her colleagues have also found that burn severity, patch size and geographic location affected early succession in the years following the Yellowstone fires. Lodgepole pine regeneration varied enormously across the burned landscape because of the spatial variation in serotiny and burn severity. Subsequently, the size, shape and configuration of disturbed patches influenced succession trajectories. Turner also highlighted that succession is generally more predictable in small patches, when disturbances are infrequent, and when disturbance severity/intensity is low (and vice versa).
Ecosystem Processes
One of the questions landscape ecologists have been using the Yellowstone fires to examine is; do post-disturbance patterns affect ecosystem processes? Net Primary Production varies a lot with tree density (e.g., density of lodgepole pine following fire) and the post-fire patterns of tree density have produced a landscape mosaic of ecosystem process rates. For example, Kashian and colleagues found spatial legacy effects of the post-fire mosaic can last for centuries. Furthermore, this spatial variation in ecosystem process rates is greater than temporal variation and the fires produced a mosaic of different functional trajectories (a ‘functional mosaic’).
Another point Turner was keen to make was that the Yellowstone fires were not the result of fire suppression as is commonly attributed, but instead they were driven by climate (particularly hot and dry conditions). Later in the presentation she used the ecosystem process examples above to argue that the Yellowstone fires were not an ecological disaster and that the ecosystem has proven resilient. However, she stressed that fire will continue to be an important disturbance and that the fire regimes is likely to change rapidly if climate does. For example, Turner highlighted the study by Westerling and colleagues that showed that increased fire activity in the western US in recent decades is a result of increasing temperatures, earlier spring snowmelt and subsequent increases in vegetation moisture deficit. If climate change projections of warming are realised, by 2100 the climate of 1988 (which was extreme) could become the norm and events like the Yellowstone fires will be much more frequent. For example, using a spatio-temporal state-space diagram (seebelow), Turner and colleagues [pdf] found that fires in Yellowstone during the 15 years previous to 1988 had relatively little impact on landscape dynamics (shown in green in the lower left of the diagram). However, the extent of the 1988 fires pushed the disturbance regime up into an area of the state-space characteristic of the shifting-mosaic steady state (shown in red).
 The spatio-temporal state-space diagram used by Turner and colleagues [pdf] to describe potential landscape disturbance dynamics. On the horizontal x-axis is the ratio of disturbance extent (area) to the landscape area and on the vertical y-axis is the ratio of disturbance interval (time) to recovery interval. Landscapes in the upper left of the diagram will appear to an observer as relatively constant in time with little disturbance impact; those in the lower right are dominated by disturbance.
Remaining Questions
Turner finished her presentation by highlighting what she sees as
key questions for studying disturbance and landscape dynamics in a changing world:
- How will disturbance interact with one another?
- How will disturbances interact with other drivers?
- What conditions will cause qualitative shifts in disturbance regimes (like that shown in the diagram above)?
It was comforting to hear that a leader in the field identified these points as important as many of them relate closely to what I’ve been working on thinking about. For example, the integrated ecological-economic forest modelling project I’m working on here in Michigan explicitly considers the interaction of two disturbances – human timber harvest and deer herbivory. The work I initiated during my PhD relates to the second question – how does human land use/cover change interact and drive changes in the wildfire regime of a landscape in central Spain? And recently, I reviewed a new book on threshold modelling in ecological restoration for Landscape Ecology.
Much of Turner’s presentation and discussion applied to American landscapes with limited human activity. This not surprising of course, given the context of the presentation (at the Ecological Society of America) and the location of her study areas (all in the USA). But although natural experiments like the 1988 Yellowstone fires may be useful as an analogue to understand processes and dynamics in similar systems, it is also interesting (and important) to think about how other systems potentially differ from this examplar. For example, the Yellowstone fires natural experiment has little to say about disturbance in human-dominated landscapes that are prevalent in many areas of the world (such as the Mediterranean Basin). In the future, research and models of landscape succession-disturbance dynamics will need to focus as much attention on human drivers of change as environmental drivers.
Turner concluded her plenary by emphasising that ecologists must increase their efforts to understand and anticipate the effects of changing disturbance regimes. This is important not only in the context of climate as driver of change, but also because of the influence of a growing human population.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Forests, Landscapes, Wildfire | Comments Off
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.
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
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
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…
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, Publications | Comments Off
Friday, April 25th, 2008
I’m back in the UP for more fieldwork. Last time I was up here was right before the start of hunting season last year. Since then a hard winter has passed and is now just being replaced by spring. There’s still snow on the ground in the northern areas of our study area, but it’s melting fast. Over the next couple of weeks we’ll be doing deer pellet counts (as a proxy for numbers of deer) to supplement previous data and to try to get a better gauge on how snowfall affects the spatial distribution of deer during the winter. We need to do these as soon after the snow melts before ground level vegetation re-grows and obscures the pellets. We’re also going to count pellets in the stands where we planted tree seedlings last fall. Then we’ll compare the estimated deer numbers in the stands with the browse on the seedlings we planted (if there’s anything left of them at all!) to try to get a more precise handle on how deer density relates to browse impact of different species.
So that’s my next few weeks – counting deer poo in the UP forests. I doubt I’ll be online much so this might be the last blog for a week or two. I’ll take some photos and maybe post them when I’m back in Lansing.
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 | Comments Off
Wednesday, April 2nd, 2008
The call for papers for the XIII World Forestry Congress is now open. To be held in Buenos Aires, Argentina, in October 2009 the congress will address “the sustainable development of forests from a global and integral perspective”. Authors are invited to submit papers and posters expressing new ideas and providing information on experiences, theoretical models and interesting initiatives. Papers will be published in the Congress Proceedings and on the Congress’ official website.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Economic, Forests | Comments Off
Tuesday, March 25th, 2008
There’s a new forest landscape model classification and review out there, recently published in Forest Ecology and Management by Hong He. The paper assumes greater familiarity with the topic of forest and disturbance modelling than the paper I recently published with my former advisor, George Perry, and discussion focuses largely on models primarily developed for the study of temperate forest systems in the USA (e.g., JABOWA, SORTIE, LANDIS, ZELIG – exceptions include MAQUIS and FORMOSAIC).
Distinction between deterministic models and stochastic models
He suggests that, generally, ecological models fall in two seemingly exclusive categories, deterministic models and stochastic models, and that either category of model can use physical or empirical approaches, or a combination of both (see figure). However, the classification He presents in the paper is developed according to how models represent
- spatial processes,
- temporal processes,
- site-level succession, and
- the intended use of the model.
Models are classified by succession based on whether the model uses succession pathways (i.e., a Markov state-and-transition approach), vital attributes (as I utilised in my PhD modelling), or by coupling landscape models with more detailed stand-level vegetation succession models. The fourth classification criteria above highlights that there are numerous applications of forest landscape models, and that design is strongly related to the desired applications. He suggests applications of forest landscape models generally fall into one of three categories:
- spatiotemporal patterns of model objects,
- sensitivities of model object to input parameters, and
- comparisons of model simulation scenarios.
After developing and presenting the classification, the paper goes on to discuss two dilemmas facing those using forest landscape models. The first is the validation of model results, which has been discussed on numerous occasion elsewhere (including this blog). The discussion on circular reasoning is more novel however, (and related in some ways to what I have written with regards models of human agents):
“It is often difficult to separate expected results from emergent results. A caution against circular reasoning is the caveat often encountered in this situation, where researchers discuss biological or environmental forcing (causes) of their modeled results, whereas the forcing (causes) is actually built in the model formulation to derive such results. It should be pointed out that most model simulations do not lead to new understanding of the modeled processes themselves. The primary and subsequent results simply reflect the relationships used in building the models, which in turn reflect current understanding of the processes. The findings of these models are simply the spatiotemporal variations of the spatial process (discussed in Section 5.1), not the mechanisms that drive the potential changes of the spatial process. Emergent results are generally those resulted from the interactions and feedbacks of model objects.”
The paper concludes by summarizing likely development of forest landscape modelling in the future:
- Model development will move from the foci of theoretical and exploratory purposes to the foci of strategic and tactical purposes with increasing model realism, responding to the needs of forest management and planning.
- Multiple spatial and temporal resolutions will be implemented for different processes
- Standardized module components may emerge as handy utilities that are ready to be plugged into other models. Since component-based models provide non-developers or end users with access to model components, a component-based model can be more rigorously tested, evaluated, and modified than before, and thus, model development processes can be driven not solely by original developers, but by the broader scientific community
- Synchronization of multiple ecological processes can be made possible with multiple computer processors. This will help deal with the limitation that ecological processes are simulated in a sequential order as determined by the executable program.
- Model memorization will be improved so that a forest landscape model not only memorizes vegetation, disturbance, and management status at the current and previous model iteration, but also the entire temporal sequence. This would allow more effective studies of legacies of forested landscapes responding to various disturbance and management activities.
Here’s the full paper citation and abstract:
He (2008) Forest landscape models: Definitions, characterization, and classification Forest Ecology and Management 254 (3) Pages 484-498
Abstract
Previous model classification efforts have led to a broad group of models from site-scale (non-spatial) gap models to continental-scale biogeographical models due to a lack of definition of landscape models. Such classifications become inefficient to compare approaches and techniques that are specifically associated with forest landscape modeling. This paper provides definitions of key terminologies commonly used in forest landscape modeling to classify forest landscape models. It presents a set of qualitative criteria for model classification. These criteria represent model definitions and key model implementation decisions, including the temporal resolution, number of spatial processes simulated, and approaches to simulate site-level succession. Four approaches of simulating site level succession are summarized: (1) no site-level succession (spatial processes as surrogates), (2) successional pathway, (3) vital attribute, and (4) model coupling. Computational load for the first three approaches is calculated using the Big O Notation, a standard method. Classification criteria are organized in a hierarchical order that creates a dichotomous tree with each end node representing a group of models with similar traits. The classified models fall into various groups ranging from theoretical and empirical to strategic and tactical. The paper summarizes the applications of forest landscape models into three categories: (1) spatiotemporal patterns of model obj
ects, (2) sensitivities of model object to input parameters, and (3) scenario analyses. Finally, the paper discusses two dilemmas related to the use of forest landscape models: result validation and circular reasoning.
Keywords Forest landscape models; Spatially explicit; Spatially interactive; Definitions; Model characterization; Model classification
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, Modelling | 1 Comment »
Friday, March 14th, 2008
This week’s edition of Nature devotes an editorial, a special report and an interview to the subject of tropical rainforests and their deforestation. The articles highlight both the proximate causes and underlying driving forces of tropical deforestation, and the importance of human activity as an agent of change (via fire for example), in these socio-ecological systems.
The editorial considers the economics of rainforest destruction, with regards to global carbon emissions. It suggests that deforestation must be integrated into international carbon markets, to reward those countries that have been able to control the removal of forest land (such as India and Costa Rica). Appropriate accounting of tropical rainforest carbon budgets is required however, and the authors point to the importance of carbon budget modelling and the monitoring of (via satellite imagery for example) change in rainforest areas over large spatial extents. Putting an economic price on ‘ecosystem services’ is key to this issue, and the editorial concludes:
One of the oddly positive effects of global warming is that it has given the world the opportunity to build a more comprehensive and inclusive economic model by forcing all of us to grapple with our impact on the natural environment. We are entering a phase in which new ideas can be developed, tested, refined and rejected as necessary. If we find just one that can beat the conventional economic measure of gross domestic product, and can quantify some of the basic services provided by rainforests and other natural ecosystems, it will more than pay for itself.
The special report focuses on the efforts of the Brazilian government to curb the rate of deforestation in the their Amazonian forests. The Brazilian police force is blockading roads, conducting aerial surveys and inspecting agricultural and logging operations, to monitor human activities on the ground. Brazilian scientists meanwhile are monitoring the situation from space, and have developed methodologies and techniques that are leading the way globally in the remote monitoring of forests. The Brazilian government is a keen advocate of the sort of economic approaches to the issues of rainforest destruction highlighted in the editorial outlined above, and sees this rigorous monitoring as key to be able to show how much carbon they can save by preventing deforestation.
Halting the removal of forest cannot simply be left to carbon trading alone, however, and local initiatives need to be pursued. To ensure the forest’s existence is sustainable, local communities need to be able make money for themselves without chopping down the trees – if they can do this it will be their in their interests NOT to remove forest. But developing this incentive has not been straightforward. For example, some researchers have have suggested that as commodity prices for crops such as soya beans have increased (possibly due to increased demand for corn-based ethanol in the US) deforestation has increased as a result. Although the price of soya beans may be a contributing factor to rainforest removal, Ruth DeFries (who will be visiting CSIS and MSU next week as part of the Rachel Carson Distinguished Lecture Series) suggests that it is not the main driver. Morton et al. found that during for the period 2001-04, conversion of forest to agriculture peaked in 2003. This situation makes it clear that there are both proximate causes and underlying driving forces of tropical deforestation. The Nature special report suggests:
If the international community is serious about tackling deforestation, it will probably need to use a hybrid approach: helping national governments such as Brazil to fund traditional policies for enforcement and monitoring and enabling communities to experiment with a market-based approach.
But how long do policy-makers have to discuss this and get these measures in place? One set of research suggests 55% of the Amazon rainforest could be removed over the next two decades, and the complexity of the rainforest system means that a ‘tipping point’ (i.e., an abrupt transition) beyond which the system might not recover (i.e., reforestation would not be possible). The Nature interview with Carlos Nobre highlights this issue – the interactions of climate change with soil moisture and the potential for fire indicate that the there is risk of rapid ‘savannization’ in the eastern to southeastern Amazon as the regional climate changes. When asked what the next big question scientists need to address in the Amazon is, Nobre replies that the role of human-caused fire will be key:
Fire is such a radical transformation in a tropical forest ecosystem that biodiversity loss is accelerated tremendously — by orders of magnitude. If you just do selective logging and let the area recover naturally, perhaps in 20–30 years only a botanist will be able to tell that a forest has been logged. If you have a sequence of vegetation fires going through that area, forget it. It won’t recover any more.
As I’ve previously discussed, considering the feedbacks and interactions between systems is important when examining landscape vulnerabilities to fire. Along with colleagues I have examined the potential effects of changing human activity on wildfire regimes in Spain (recently we had this paper published in Ecosystems and you can see more wildfire work here). However, the integrated study of socio-economic and ecological systems is still very much in its infancy. And the processes of landscape change in the northern Mediterranean Basin and the Amazonian rainforest are very different; practically inverse (increases in forest in the former and decreases in the latter). As always, plenty more work needs to be done on these subjects, and with the potential presence of ‘tipping points’, now is an important time to be doing it.
This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.
Posted in Academic, Ecological, Economic, Environmental, Forests, Landscapes, Political, Sustainability, Wildfire | Comments Off
| |
