Millington, J.D.A., Walters, M.B., Matonis, M.S., Laurent, E.J., Hall, K.R. and Liu, J. (2011) Combined long-term effects of variable tree regeneration and timber management on forest songbirds and timber production Forest Ecology and Management 262 718-729 doi: 10.1016/j.foreco.2011.05.002

Millington, J.D.A. and Perry, G.L.W. (2011) Multi-model inference in biogeography Geography Compass 5(7) 448-530 doi: 10.1111/j.1749-8198.2011.00433.x

Millington, J.D.A., Demeritt, D. and Romero-Calcerrada, R. (2011) Participatory evaluation of agent-based land use models Journal of Land Use Science 6(2-3) 195-210 doi:10.1080/1747423X.2011.558595

Millington, J.D.A. et al. (2011) Combined long-term effects of variable tree regeneration and timber management on forest songbirds and timber production Forest Ecology and Management 262 718-729
Abstract
The structure of forest stands is an important determinant of habitat use by songbirds, including species of conservation concern. In this paper, we investigate the combined long-term impacts of variable tree regeneration and timber management on stand structure, songbird occupancy probabilities, and timber production in northern hardwood forests. We develop species-specific relationships between bird species occupancy and forest stand structure for canopy-dependent black-throated green warbler (Dendroica virens), eastern wood-pewee (Contopus virens), least flycatcher (Empidonax minimus) and rose-breasted grosbeak (Pheucticus ludovicianus) from field data collected in northern hardwood forests of Michigan’s Upper Peninsula. We integrate these bird-forest structure relationships with a forest simulation model that couples a forest-gap tree regeneration submodel developed from our field data with the US Forest Service Forest Vegetation Simulator (Ontario variant). Our bird occupancy models are better than null models for all species, and indicate species-specific responses to management-related forest structure variables. When simulated over a century, higher overall tree regeneration densities and greater proportions of commercially high value, deer browse-preferred, canopy tree Acer saccharum (sugar maple) than low-value, browse-avoided subcanopy tree Ostrya virginiana (ironwood) ensure conditions allowing larger harvests of merchantable timber and had greater impacts on bird occupancy probability change. Compared to full regeneration, no regeneration over 100 years reduces merchantable timber volumes by up to 25% and drives differences in bird occupancy probability change of up to 30%. We also find that harvest prescriptions can be tailored to affect both timber removal volumes and bird occupancy probability simultaneously, but only when regeneration is adequate. When regeneration is poor (e.g., 25% or less of trees succeed in regenerating), timber harvest prescriptions have a greater relative influence on bird species occupancy probabilities than on the volume of merchantable timber harvested. However, regeneration density and composition, particularly the density of Acer saccharum regenerating, have the greatest long-term effects on canopy bird occupancy probability. Our results imply that forest and wildlife managers need to work together to ensure tree regeneration density and composition are adequate for both timber production and the maintenance of habitat for avian species over the long-term. Where tree regeneration is currently poor (e.g., due to deer herbivory), forest and wildlife managers should pay particularly close attention to the long-term impacts of timber harvest prescriptions on bird species.

Millington, J.D.A. and Perry, G.L.W. (2011) Multi-model inference in biogeography Geography Compass 5(7) 448-530
Abstract
Multi-model inference (MMI) aims to contribute to the production of scientific knowledge by simultaneously comparing the evidence data provide for multiple hypotheses, each represented as a model. With roots in the method of ‘multiple working hypotheses’, MMI techniques have been advocated as an alternative to null-hypothesis significance testing. In this paper, we review two complementary MMI techniques – model selection and model averaging – and highlight examples of their use by biogeographers. Model selection provides a means to simultaneously compare multiple models to evaluate how well each is supported by data, and potentially to identify the best supported model(s). When model selection indicates no clear ‘best’ model, model averaging is useful to account for parameter uncertainty. Both techniques can be implemented in information-theoretic and Bayesian frameworks and we outline the debate about interpretations of the different approaches. We summarise recommendations for avoiding philosophical and methodological pitfalls, and suggest when each technique is best used. We advocate a pragmatic approach to MMI, one that emphasises the ‘thoughtful, science-based, a priori’ modelling that others have argued is vital to ensure valid scientific inference.

Millington et al. (2011) Participatory evaluation of agent-based land use models Journal of Land Use Science 6(2-3) 195-210
Abstract
A key issue facing contemporary agent-based land-use models (ABLUMs) is model evaluation. In this article, we outline some of the epistemological problems facing the evaluation of ABLUMs, including the definition of boundaries for modelling open systems. In light of these issues and given the characteristics of ABLUMs, participatory model evaluation by local stakeholders may be a preferable avenue to pursue. We present a case study of participatory model evaluation for an agent-based model designed to examine the impacts of land-use/cover change on wildfire regimes for a region of Spain. Although model output was endorsed by interviewees as credible, several alterations to model structure were suggested. Of broader interest, we found that some interviewees conflated model structure with scenario boundary conditions. If an interactive participatory modelling approach is not possible, an emphasis on ensuring that stakeholders understand the distinction between model structure and scenario boundary conditions will be particularly important.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

There’s some more by the same guy here, and an awesome one of the recent Icelandic volcanic eruptions here.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

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.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

Recently, however, the case been made for increasing UK forest and woodland cover as a form of climate change mitigation. This summer the UK Low Carbon Transition Plan identified woodland creation as a cost-effective way of mitigating climate change and recognised the importance of supporting tree-planting initiatives. More recently, the National Assessment of UK Forestry and Climate Change Steering Group has provided its response to the IPCC Fourth Assessment Report and argues there is a clear need for more woodlands.

One of the main findings of this initial assessment was that an increase in woodland area of 23,000 ha per year over the next 40 years could abate 10% of UK 2050 greenhouse gas emissions. With echos of the recommendation from the Stern Review on the Economics of Climate Change to ‘Act now or pay later’, the key message from this assessment is ‘Plant now and use sustainably’. The long maturation times of forest systems means that it may take take 50–100 years for actions to pay off.

Being such a long-standing investment it’s vital that the benefits of planted woodlands and forests are not outweighed by negative impacts on biodiversity, food security, landscape and water supply. From this stand-point there is much to be done and the assessment recommends that “further scientific and socio-economic analysis is required to enable the UK to achieve the full [climate] adaptation and mitigation potential of forestry” and that “clear, robust, research programmes will be needed to underpin the changes of forestry policy and practice which are required to meet the new and challenging circumstances”.

A question that immediately springs to my mind is where these woodlands should be placed to maximise their carbon sequestration payoff while minimising negative impacts on other aspects of the landscape. For example, if arable agricultural land is to be converted, how will biodiversity be affected by the removal of hedgerows? What would this conversion of agricultural land mean for local economies? Which species will benefit in terms of habitat connectivity and which will lose out? Addressing questions like these will be important as forest policy moves toward returning UK forest cover area near levels seen elsewhere in Europe.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

LANDMOD 2010
LANDMOD 2010 will be held at SupAgro in Montpellier, France, February 3rd to 5th 2010.

The 2010 international conference on integrative landscape modelling will gather leading scientists in each of the main disciplines dealing with ecosystems and landscape simulation and management, complex dynamic modelling and assessment of vulnerability, resilience and adaptation of agro- and eco-systems under human influence.

The main objectives of the conference are:

• To discuss the objectives, priorities and expectations when modelling the functioning of landscapes;
• To share experience about landscape modelling and to identify major existing conceptual and technological gaps;
• To release a ‘state of the art’ about landscape modelling and simulation;
• To start building an international network on integrative ecosystems and landscape modelling.

Deadlines
October 31st : deadline for submission of extended abstracts
November, 30th: notification of acceptation of talks and posters
December, 31st : deadline for registration and payment

Website: http://www.umr-lisah.fr/rtra-projects/landmod2010

US-IALE 2010
The 25th annual meeting of US-IALE (US Regional Association, International Association for Landscape Ecology) will be held in Athens, Georgia, from April 5-9, 2010. One of the unique aspects of the 25th annual meeting is to reflect upon progress made in the past 25 years and to chart an even more productive course for landscape ecology over the next quarter century. The meeting will include special sessions at which past presidents of US-IALE and other leading landscape ecologists will provide retrospectives on and perspectives for landscape ecology.

Approximately 20 NASA-MSU Awards and 10 CHANS Fellowships will be available to support students, postdoctoral associates, junior faculty and other junior researchers to attend the meeting.

Deadlines
October 15, 2009: Proposals for symposia and workshops
December 15, 2009: Abstracts for oral and poster presentations
December 15, 2009: NASA-MSU Awards Applications
December 15, 2009: CHANS Fellowship Applications

Website: http://www.usiale.org/athens2010/

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

A joint seminar between the Royal Bath and West of England Society and RGS with IBG, “with the aim of examining some of the problems and solutions relating to planning for future sustainable land use in rural areas. Using the West Country as a model, eminent speakers will cover such topics as climate change, population increase, pollution concerns, security of food, energy and economy, biodiversity and scientific developments.”

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

The ancient trees of Petrified Forest National Monument – preserved as quartz crystal moulds of trees buried by sediments before they decomposed – are over 200 million years old.

At that time, in the Late Triassic, northeastern Arizona was located near the equator resulting in a tropical climate and vegetation. The climate and landscape couldn’t be much more different now and the sheer scale of change (both time and location) are hard to comprehend looking out over the desert sunset.

The physical size of the Grand Canyon isn’t much easier to comprehend, even when you’re stood at the very edge of the southern rim.

By the time the Colorado river had begun carving the canyon a mere 17 million years ago, the processes leading to its formation had already been at work for around 2,000 million years (the lowest sediments at the bottom of the Inner Gorge date to around that time). Sunset here is no less timeless than in the Petrified Forest.

Compared with the forest and the gorge the Barringer Crater was created in the blink of an eye. But the 300,000 ton meteor that hit earth 50,000 years ago had probably being travelling on that collision course for a much longer time.

That these awesome features remain – so huge in time and space – reminds us how fleeting our biological landscapes are.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

Landsat satellite image of the Yellowstone fires on 23^rd 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.

Local winter white-tailed deer density: Effects of forest cover pattern, stand structure, and snow in a managed forest landscape
James D. A. Millington, Michael B. Walters, Megan S. Matonis and Jianguo Liu
Michigan State University

Background/Question/Methods
White-tailed deer (Odocoileus virginianus) are a ‘keystone herbivore’ with the potential to cause tree regeneration failure and greatly affect vegetation dynamics, stand structure and ecological function of forests across eastern North America. In northern mixed conifer-hardwood forests, local winter-time deer populations are dependent on habitat characterized by patterns of forest cover that provide shelter from snow and cold temperatures (lowland conifer stands) in close proximity to winter food (deciduous hardwood stands). Stand structure may also influence winter spatial deer distribution. Consequently, modification of forest cover patterns and stand structure by timber harvesting may affect local spatial deer distributions, with potential ecological and economic consequences. Here, we ask if forest cover pattern and stand structure, and their interactions with snow depth, can explain winter deer density in the managed forests of the central Upper Peninsula of Michigan, USA. For each local winter deer density estimate (from fecal pellet counts) we calculate stand-level characteristics for surrounding ‘landscapes of influence’ of radius 200 m and 380 m. For these data, and modeled snow depth estimates, we use multivariate techniques to produce predictive models and to identify the most important factors driving local deer densities across our 400,000 ha study area.

Results/Conclusions
Distance to the nearest conifer stand consistently explains the most variance in univariate regression models. Deer densities are highest near lowland conifer stands in areas where the proportion of hardwood forest-cover is high but the mean tree diameter-at-breast-height is low. Multiple regression models including these factors explain up to 38% of variance in deer density and have up to a 68% chance of correctly ranking a site’s deer density (relative to other sites within our study area). We are unable to conclusively show that snow depth has a significant impact on winter deer density, but our data suggest that more detailed investigation into the combined effect of distance to lowland conifer and snow depth may prove fruitful. Our results quantify clear effects of stand structure and forest cover composition on the winter spatial distribution of white-tailed deer. We briefly discuss how these results can be used in an ecological-economic simulation model of a managed forest for tree regeneration risk assessment. Use of these results, and the simulation model, will help identify management practices that can decrease deer impacts and ensure the ecological and economic sustainability of forests in which deer browse is proving problematic for tree regeneration.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.

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.

This work by James D.A. Millington is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States License.