Next year’s Annual meeting of the Association of American Geographers will be in Seattle. I was considering attending but I think it might be best to let the dust settle after moving back to the UK in January. Many others will be there however, including James Porter, a colleague and friend from PhD times at King’s College, London. On his behalf, here’s the call for papers for a session he’s organising at the meeting. Deadline is 1st October, more details at the bottom.
Call for Papers The Politics of Expectations: Nature, Culture, and the Production of Space
Association of American Geographers, Annual Meeting, 12-16th April 2011, Seattle.
Session Organisers:
James Porter (King’s College London) and Samuel Randalls (University College London)
Expectations are incredibly powerful things. Whether materialized via climatic models, economic forecasts, or based on the promise of personalised medicines, expectations (and those who engineer them) play a deeply political yet often unsung role in bringing into being a particular kind of future as well as shaping a particular kind of present. Savvy actors seeking to engineer change may decide to write editorials, give press briefings, or try to normalise trust between the communities involved so as to enrol support and resources for an emerging marketplace (and consumer) they have envisioned. Such discursive as well as performative practices pre-emptively shape the social and economic context for developing technologies so that the actors involved not only develop their physical objects but also influence other people’s thinking. Rather than dismiss such efforts as exaggerated or self-serving claims, the “sociology of expectations” (cf. Brown, 2003; Hedgecoe, 2004; Law, 1994) points to the constructive, performative, and even destructive role such expectations have in today’s world where competition for funding, research impact and innovation are so intense. As many geographers researching the ‘commercialization of nature’ have noted (cf. Castree, 2003; Johnson, 2010; Lave et al., 2010; Prudham, 2005), expectations of future natures inhabit contemporary environmental management in a series of subtle and not so subtle ways for all actors.
But how are expectations created, configured, and stabilized? What, and whose, interests shape them, and in turn, whose interests do they shape? And why do some persist whilst others don’t? Such questions speak directly to the ways in which nature (and knowledge of it) is being increasingly commercialized and commodified through its interactions with science and technology. This session builds on controversies such as the climate change emails at UEA, medical trials, carbon forestry and much more to showcase how the “future” is mobilized to govern or proliferate uncertainty and justify particular mechanisms for managing environmental problems. Geographers are uniquely placed to comment on this providing theoretical depth and empirical evidence that sheds light on the commodification of nature whilst also contributing to the socio-technical analyses employed by science and technology studies scholars. We therefore invite papers addressing (though not limited to) the following questions:
Who constructs expectations and why? How / where do they get enacted (i.e. technological, sociocultural, artefacts, etc.)? And how do they get accepted, institutionalized, or perhaps resisted?
How are expectations of nature commercialized? To what extent are expectations central to processes of commercialization and does this vary depending on the specific environmental arena? Are there unnatural expectations?
Do expectations have agency? Can they be negotiated or adapted? If so, what role have geographers played in shaping past perceptions and might hope to play in the future?
What happens if a set of expectations is not successful? Why didn’t they succeed? And what lessons can we learn?
Abstracts should be sent to both James Porter (james.porter at kcl.ac.uk) and Samuel Randalls (s.randalls at ucl.ac.uk) by Friday 1st October 2010.
Across our study area we’ve found that regeneration of juvenile trees following timber harvest varies greatly. For example, from our empirical data we find that sugar maple saplings were present in over 70% of northern forest gaps but were completely absent from 96% of gaps in southern areas. Megan Matonis suggested in her thesis that this variation is related to snow depth, deer density and soil nutrient conditions. To examine the potential long-term effects of these differences in regeneration on forest structure I’ve been running our simulation model with pre-set levels of regeneration that reflect our observations, ranging from the maximum possible (given the space available in a post-harvest gap) to a complete absence of regenerating juvenile trees.
These ‘gaps’ I’m talking about are created in northern hardwood forests when individual or small groups of trees are removed in an uneven-aged timber management approach. The removal of these trees creates openings (‘gaps’) in the forest canopy allowing light into lower levels for younger trees [gaps may also be created naturally but we're focusing on those created by human activity which is the dominant driver in our study area]. When harvesting trees in this approach foresters aim to produce a forest structure with a ‘reverse-J’ distribution of tree sizes; high densities of small, young trees and low densities of larger, older trees (approximating a gamma-distribution like I found in our data previously). The idea is that through time an abundant supply of competing smaller trees will replace larger trees trees that are removed.
Representing this approach in our model (using FVS keywords [.pdf]) requires quite a bit of code, but working through the example provided by Don Vandendriesche [.pdf] helped. This approach requires the model user to specify a residual basal area (the area occupied by trees) and the ratio between the number of trees in successive size classes (the q-factor).
To examine my initial results (and to help debugging during the whole modelling process) I used R to plot size-class distributions for tree densities and basal area. As is the norm I used size-classes defined by the diameter-at-breast-height of the trees (5 cm or about 2 inches). Then I combined plots for simulated years into animated .gif files to see how the distributions changed through time for different regeneration levels. Here are a couple of examples (click for larger versions):
By the end of these 200-year simulations the same stand has a very different forest structure. In the top example regeneration is sufficient to replace trees removed during harvest, growing into larger size-classes as more resources (light and space) become available. But in the bottom example we see the consequences of when no new trees grow to replace the the removed trees – by the mid-21st century there are no trees in the smaller size-classes and timber harvesting has to become less frequent to meet timber removal goals (and remain viable).
I’m continuing to analyse the model output in a more quantitative manner and assessing the impacts of these potential changes in forest structure on bird habitat (specifically the probability that different species will be present in a forest stand). All together this should make a nice manuscript and provide some interesting information for the foresters working in these northern hardwood forests.
The Leverhulme Trust makes awards in support of research and education with special emphasis on original and significant research that aims to remove barriers between traditional disciplines. Their Early Career Fellowships are awarded across all disciplines and in 2010 approximately 70 were expected to be awarded to individuals to hold at universities in the UK. Given the emphasis on original, significant and cross-disciplinary research made by the Trust I looked for something that matched my research skills in coupled human and natural systems modelling but that pushed work in that area in a new direction. I thought back to the ideas about model narratives I have previously explored with David O’Sullivan and George Perry (but have not worked on since then) and Bill Cronon’s plenary address at the Royal Geographical Society in 2006 on the need for ‘sustainable narratives’. With that in mind, and given the UK Forestry and Climate change report I had been reading, I decided to make a pitch for a project that would explore how narratives from the use of models could help individuals identify how local actions transcend scales to mitigate global climate change in the context of the anticipated woodland planting that will be ongoing in the UK in future years. It proved to be a successful pitch!
I’m sure I will blog plenty more about the project in the future, so for now I will just leave you with the proposal rationale (below). I’m looking forward to getting to work on this when I get back to London, but before that there’s plenty more things to get done on the Michigan forest landscape ecological-economic modelling.
Model narratives for climate change mitigation The abstract, vast, and systemic narratives that dominate the issue of global climate change do little to illustrate to individuals and groups how their actions might contribute to mitigate the effects of what is often framed as a global problem (Cronon 2006). Ways to improve the ability of individuals and groups to identify how their local actions transcend scales to mitigate global climate change are needed. In this research I will explore how narratives produced from computer simulation models that represent individuals’ actions can provide people with insights into how their behaviour affects system properties at a larger scale. Although the narrative properties of simulation models have been highlighted (O’Sullivan 2004), the use of models to develop localised narratives of climate change which emphasise individual agency has yet to be explored. Confronting individuals with these narratives will also help researchers reveal important underlying, and possibly implicitly held, assumptions that influence choices and behaviour.
This research will address the following general questions:
How can computer simulation models be better used to reveal to individuals how their local actions can contribute to global environmental issues such as Climate Change Mitigation (CCM)?
What are the narrative properties of simulation models and how can they be exploited to help individuals find meaning about their actions as they relate to global climate change?
By using simulation tools to spur reflection what can we learn about the factors influencing individuals’ choices and behaviour with regards CCM options?
Answering these questions will require a uniquely interdisciplinary research approach that spans the physical sciences, social sciences and humanities. Such ground-breaking, boundary-crossing work is necessary if we are to re-connect the physical sciences with the publics they intend to benefit and find solutions to large-scale and pressing environmental problems. For example, one of the key findings from a recent report by the National Assessment of UK Forestry and Climate Change Steering Group (Read et al. 2009) was that “[t]he extent to which the potential for additional [greenhouse gas] emissions abatement through tree planting is realized … will be determined in large part by economic forces and society’s attitudes rather than by scientific and technical issues alone” (p.xvii). The report also argued the need “to better understand and consider the role of different influences affecting choices and behaviour. Without the appropriate emotional, cultural or psychological disposition, information will make no difference.” (p.210). Narratives based on scientific understanding which portray how individuals can make a difference to large-scale, diffuse environmental issues will be important for fostering such a disposition. Simulation models – quantitative representations of reality which provide a means to logically examine how high-level and large-scale patterns are generated by lower-level and smaller-scale processes and events – have the potential to contribute to the construction of these narratives.
As I mentioned in a tweet earlier this week, Prof. Ken Frank was ‘visiting’ CSIS this week. Ken studies organizational change and innovation using, amongst other methods, Social Network Analysis (SNA). SNA examines how the structure of ties between people affects individuals’ behaviour, at how social network structure and composition influences the social norms of a group, and how resources (for example, of information) flow through a social network. This week Ken organised a couple of seminars on the use of SNA to investigate natural resource decision-making (for example, in small-scale fisheries) and I joined a workshop he ran on how we actually go about doing SNA, learning about software like p2 and KliqueFinder. Ken showed us the two main models; the selection model and the influence model. The former addresses network formation and examines individuals’ networks and how they chose it. The latter examines how individuals are influenced by the people in their network and the consequences for their behaviour. As an example of how SNA might be used, take a look at this executive summary [pdf] of the thesis of a recent graduate students from MSU Fisheries and Wildlife.
On Friday, after having been introduced through the week to what SNA is, I got to chat with Ken about how it might relate to the agricultural decision-making modelling I did during my PhD. In my agent-based model I used a spatial neighbourhood rule to represent the influence of social norms (i.e. whether a farmer is ‘traditional’ or ‘commercial’ in my categories). However, the social network of farmers is not solely determined by spatial relationshps – farmers have kinship ties and might meet other individuals at the market or in the local cerveceria. We discussed how I might be able to use SNA to better represent the influences of other farmers on an indiviuals’ decision-making in my model. I don’t have the network data needed to do this right now but it’s something to think about for the future.
If I’d been more aware of SNA previously I may have incorporated some discussion of it into the book chapter I re-wrote recently for Environmental Modelling. In that chapter I focused on the increasing importance of behavioural economics for investigating and modelling the relationships between human activity and the environment. SNA is certainy something to add to the toolbox and seems to be on the rise in natural resources research. Something else I missed whilst working on re-writing that that chapter was the importance of behavioural economics to David Cameron‘s ‘Big Society’ idea. He seems to be aware of the lessons we’ve started learning from things like social network analysis and behavioural economics – now he’s in charge maybe we’ll start seeing some direct application of those lessons to UK public policy.
Recently I was asked to write a review of the current state-of-the-art of model selection and Bayesian approaches to modelling in biogeography for the Geography Compass journal. The intended audience for the paper will be interested but non-expert, and the paper will “…summarize important research developments in a scholarly way but for a non-specialist audience”. With this in mind, the structure I expect I will aim for will look something like this:
i) Introduction to the general issue of model inference (i.e., “What is the best model to use?”). This section will likely discuss the modelling philosophy espoused by Burnham and Anderson and also highlight some of the criticisms of null-hypothesis testing using p-values. Then I might lead into possible alternatives (to standard p-value testing) such as:
ii) AIC approaches (to find the ‘best approximating model’)
I also expect I will try to offer some practical hint and tips, possibly using boxes with example R code (maybe for the examples in iv). Other published resources I’ll draw on will likely include the excellent books by Ben Bolker and Michael McCarthy. As things progress I may post more, and I’ll be sure to post again when the paper is available to read in full.
Birds have been given short shrift in my posts blog posts about the Michigan UP ecological-economic modelling project. It’s not that we have forgotten about them, it’s just that before we got to incoporating them into our modelling there were other things to deal with first. Now that we’ve made progress on modelling deer distribution it’s time to turn our attention to how we can represent the potential impacts of forest management on bird habitat so that we might better understand the tradeoffs that will need to be negotiated to achieve both economic and ecological sustainability.
Ovenbird (Seiurus aurocapillus)
One of the things we want to do is link our bird-vegetation modelling with Laila Racevskis‘ assessment of the economic value of bird species she did during her PhD research. Laila assessed local residents’ willingess-to-pay for ensuring the conservation of several bird species of concern in our study area. If we can use our model to examine the effects of different timber management plans (each yielding different timber volumes) on the number of bird species present in an area we can use Laila’s data to examine the economic tradeoffs between different management approaches. The first thing we need to do to achieve this is be able to estimate how many bird species would be present in a given forest stand.
Right now the plan is to estimate the presence of songbird species of concern in forest stands by using the data Ed Laurent collected during his PhD research at MSU. To this end I’ve been doing some reading on the latest occupancy modelling approaches and reviewing the literature on its application to birds in managed forests. Probably the most popular current approach was developed recently by Darryl Mackenzie and colleagues – it allows the the estimation of whether a site is occupied by a given species or not when we know that our detection is imperfect (i.e. when we know we have false negative observations in our bird presence data). The publication of some nice overviews of this approach (e.g. Mackenzie 2006) plus the development of software to perform the analyses are likely to be at the root of this popularity.
The basic idea of the approach is that if we are able to make multiple observations at a site (and if we assume that bird populations and habitat do not change between these observations) we can use the probability of each bird observation history at a site across all the sites to form a model likelihood. This likelihood can then be used to estimate the parameters using any likelihood-based estimation procedure. Covariates can be used to model both the probability of observation and detection (i.e. we can account for factors that may have hindered bird observation such a wind strength or the time of day). I won’t go into further detail here because there’s an excellent online book that will lead you through the modelling process, and you can download the software and try it yourself.
Two recent papers have used this approach to investigate bird species presence given different forest conditions. DeWan et al. 2009 used Mackenzie’s occupancy modelling approach to examine impacts of urbanization on forest birds in New York State (they do a good job of explaining how they apply Mackenzie’s approach to their data and study area). DeWan considered landscape variables such as perimeter-area ratios of habitat patches and proximity to urban area to create occupancy models for 9 birds species at ~100 sites. They found that accounting for imperfect bird detection was important and that habitat patch “perimeter-area ratio had the most consistent influence on both detection probability and occupancy” (p989).
In a slightly different approach Smith et al. 2008 estimated site occupancy of the black-throated blue warbler (Dendroica caerulescens) and ovenbird (Seiurus aurocapillus) in 20 northern hardwood-conifer forest stands in Vermont. At each bird observation site they had also collected stand structure variables including basal area, understory density and tree diameters (in contrast to DeWan et al who only considered landscape-level variables). Smith et al. write their results “demonstrate that stand-level forest structure can be used to predict the occurrence of forest songbirds in northern hardwood-conifer forests” (p43) and “suggest that the role of stand-level vegetation may have been underestimated in the past” (p36).
Our approach will take the best aspects from both these studies; the large sample size of DeWan et al. with the consideration of stand-level variables like Smith et al. More on this again soon I expect.
I hoped it would be quicker than previous papers, but the review process of the ‘Mind, the Gap’ manuscript I worked on with John Wainwright hasn’t been particularly fast. I guess that’s just how it goes with special issues. I’ll discuss some of the topics we touch on in the paper in a future post. For now here’s the abstract – look out for the full paper on the ESPL website in the next couple of months.
Mind, the Gap in Landscape-Evolution Modelling John Wainwright and James Millington Earth Surface Processes and Landforms (Forthcoming)
Abstract Despite an increasing recognition that human activity is currently the dominant force modifying geomorphic landscapes, and that this activity has been increasing through the Holocene, there has been little integrative work to evaluate human interactions with geomorphic processes. We argue that agent-based models (ABMs) are a useful tool for overcoming the limitations of existing, highly empirical approaches. In particular, they allow the integration of decision-making into process-based models and provide a heuristic way of evaluating the compatibility of knowledge gained from a wide range of sources, both within and outwith the discipline of geomorphology. The application of ABMs to geomorphology is demonstrated from two different perspectives. The SPASIMv1 (Special Protection Area SIMulator version 1) model is used to evaluate the potential impacts of land-use change – particularly in relation to wildfire and subsequent soil conditions – over a decadal timescale from the present day to the mid-21st century. It focuses on the representation of farmers with traditional versus commercial perspectives in central Spain, and highlights the importance of land-tenure structure and historical contingencies of individuals’ decision making. CYBEROSION, on the other hand, considers changes in erosion and deposition over the scale of at least centuries. It represents both wild and domesticated animals and humans as model agents, and investigates the interactions of them in the context of early agriculturalists in southern France in a prehistoric context. We evaluate the advantages and disadvantages of the ABM approach, and consider some of the major challenges. These challenges include potential process scale mis-matches, differences in perspective between investigators from different disciplines, and issues regarding model evaluation, analysis and interpretation. If the challenges can be overcome, this fully-integrated approach will provide geomorphology a means to conceptualize soundly the study of human-landscape interactions.
Update January 2010: This paper is now online with doi 10.1016/j.foreco.2009.12.020.
I received some good news this morning as I prepared to head back to the UK for the holidays. The paper I started writing back in January examining the white-tailed deer distribution in our managed forest landscape (the analysis for which inspired posts on Bayesian and ensemble modelling) has been accepted for publication and is ‘In Press’! I’ve copied the abstract below.
Another piece of publications news I received a while back is that the paper I co-authored with Raul Romero-Calcerrada and others modelling socioeconomic data to understand patterns of human-caused wildfire ignition risk has now officially been published in Ecological Modelling.
Happy Holidays everyone!
Effects of local and regional landscape characteristics on wildlife distribution across managed forests (In Press) Millington, Walters, Matonis, and Liu Forest Ecology and Management
Abstract Understanding impacts of local and regional landscape characteristics on spatial distributions of wildlife species is vital for achieving ecological and economic sustainability of forested landscapes. This understanding is important because wildlife species such as white-tailed deer (Odocoileus virginianus) have the potential to affect forest dynamics differently across space. Here, we quantify the effects of local and regional landscape characteristics on the spatial distribution of white-tailed deer, produce maps of estimated deer density using these quantified relationships, provide measures of uncertainty for these maps to aid interpretation, and show how this information can be used to guide co-management of deer and forests. Specifically, we use ordinary least squares and Bayesian regression methods to model the spatial distribution of white-tailed deer in northern hardwood stands during the winter in the managed hardwood-conifer forests of the central Upper Peninsula of Michigan, USA. Our results show that deer density is higher nearer lowland conifer stands and in areas where northern hardwood trees have small mean diameter-at-breast-height. Other factors related with deer density include mean northern hardwood basal area (negative relationship), proportion of lowland conifer forest cover (positive relationship), and mean daily snow depth (negative relationship).The modeling methods we present provide a means to identify locations in forest landscapes where wildlife and forest managers may most effectively co-ordinate their actions.
A while back I wrote about how it takes all sorts to make a world and why we need to account for those different sorts in our models of it. One of the things that I highlighted in that post was the need for mainstream economics to acknowledge and use more of the findings from behavioural economists.
One of the examples I used in the draft of the book chapter I have been writing for the second edition of Wainwright and Mulligan’s Environmental Modelling was the paper by Tversky and Kahneman, The Framing of Decisions and the Psychology of Choice. They showed how the way in which a problem is framed can influence human decision-making and causes problems for rational choice theory. In one experiment Tversky and Kahneman asked people if they would buy a $10 ticket on arriving at the theatre when finding themselves in two different situations:
i) they find they have lost $10 on the way to the theatre, ii) they find they have lost their pre-paid $10 ticket.
In both situations the person has lost the value of the ticket ($10) and under neoclassical economic assumptions should behave the same when deciding whether to buy a ticket when arriving at the theatre. However, Tversky and Kahneman found that people were more likely to buy a ticket in the first situation (88%) than buying a (replacement) ticket in the second (46%). They suggest this behaviour is due to human ‘psychological accounting’, in which we mentally allocate resources to different purposes. In this case people are less willing to spend money again on something they have already allocated to their ‘entertainment account’ than if they have lost money which they allocate to their ‘general expenses account’.
More recently, Galinsky and colleagues examined how someone else’s irrational thought processes can influence our own decision-making. In their study they asked college students to take over decision-making for a fictitious person they had never met (the students were unaware the person was fictitious).
In one experiment, the volunteers watched the following scenario play out via text on a computer screen: the fictitious decision-maker tried to outbid another person for a prize of 356 points, which equaled $4.45 in real money. The decision-maker started out with 360 points, and every time the other bidder upped the ante by 40 points, the decision-maker followed suit. Volunteers were told that once the decision-maker bid over 356 points, he or she would begin to lose some of the $12 payment for participating in the study.
When the fictitious decision-maker neared this threshold, the volunteers were asked to take over bidding. Objectively, the volunteers should have realized that – like the person who makes a bad investment in a ‘fixer-upper’ – the decision-maker would keep throwing good money after bad. But the volunteers who felt an identification with the fictitious player (i.e., those told by the researchers that they shared the same month of birth or year in school) made almost 60% more bids and were more likely to lose money than those who didn’t feel a connection.
Are we really surprised that neoclassical economic models often fall down? Accounting for seemingly irrational human behaviour may make the representation of human decision-making more difficult, but increasingly it seems irrational not to do so.
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…
