Traditional Fire Knowledge in Spain

When you haven’t done something for a while it’s often best not to rush straight back in at the intensity you were at before. So here’s a nice easy blog to get me going again (not that I was blogging intensely before!).

I didn’t blog about it at the time (unsurprisingly), but back in late June 2013 I went to visit a colleague of mine in Madrid, Dr Francisco Seijo. Francisco and I met back at something I did blog about, the 2009 US-IALE conference in Snowbird. Since then we’ve been discussing how we can use the idea of coupled-human and natural systems to investigate Mediterranean landscapes.

Example of Traditional Fire Knowledge. The ‘pile-burning’ technique involves raking, piling and igniting leaves. This contrasts with ‘a manta’ broadcast burning in which leaves and ground litter are burned across larger areas. Photos by the authors of the paper.

After a brief field visit by me, an interview campaign by Francisco, collection of secondary data from other sources (aerial photography and official fire statistics) and some desk analysis, we recently published our first paper on the work. Entitled Forgetting fire: Traditional fire knowledge in two chestnut forest ecosystems of the Iberian Peninsula and its implications for European fire management policy, and published in the journal Land Use Policy, the article presents the results of our mixed-methods and interdisciplinary approach. Building on Francisco’s previous examination of ‘pre-industrial anthropogenic fire regimes’ we to to investigate differences between the fire regimes and management approaches of chestnut forest ecosystems in two municipalities in central Spain. In the paper we also discuss ideas of Traditional Ecological Knowledge (TEK), the related idea of Traditional Fire Knowledge (TFK), and discuss them in light of contemporary fire management approaches in Europe.

The full abstract is below with links to the paper. I’ll stop here now as this rate of blogging it making me quite dizzy (but hopefully I’ll be back for more soon).

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Seijo, Francisco, James DA Millington, Robert Gray, Verónica Sanz, Jorge Lozano, Francisco García-Serrano, Gabriel Sangüesa-Barreda, and Jesús Julio Camarero (2015) Forgetting fire: Traditional fire knowledge in two chestnut forest ecosystems of the Iberian Peninsula and its implications for European fire management policy. Land Use Policy 47 130-144. doi: 10.1016/j.landusepol.2015.03.006
[Online] [Pre-print]

Abstract

Human beings have used fire as an ecosystem management tool for thousands of years. In the context of the scientific and policy debate surrounding potential climate change adaptation and mitigation strategies, the importance of the impact of relatively recent state fire exclusion policies on fire regimes has been debated. To provide empirical evidence to this ongoing debate we examine the impacts of state fire exclusion policies in the chestnut forest ecosystems of two geographically neighbouring municipalities in central Spain, Casillas and Rozas de Puerto Real. Extending the concept of ‘Traditional Ecological Knowledge’ to include the use of fire as a management tool as ‘Traditional Fire Knowledge’ (TFK), we take a mixed-methods and interdisciplinary approach to argue that currently observed differences between the municipalities are useful for considering the characteristics of “pre-industrial anthropogenic fire regimes” and their impact on chestnut forest ecosystems. We do this by examining how responses from interviews and questionnaire surveys of local inhabitants about TFK in the past and present correspond to the current biophysical landscape state and recent fire activity (based on data from dendrochronological analysis, aerial photography and official fire statistics). We then discuss the broader implications of TFK decline for future fire management policies across Europe particularly in light of the published results of the EU sponsored FIRE PARADOX research project. In locations where TFK-based “pre-industrial anthropogenic fire regimes” still exist, ecosystem management strategies for adaptation and mitigation to climate change could be conceivably implemented at a minimal economic and political cost to the state by local communities that have both the TFK and the adequate social, economic and cultural incentives to use it.

Key words

Fire exclusion policies; traditional ecological knowledge; traditional fire knowledge; Chestnut forest ecosystems; FIRE PARADOX

 

#ialeuk2014 – Urban landscape ecology: Science, policy and practice

Something else to keep me busy this year is the organisation of the Annual Conference of the International Association for Landscape Ecology (UK). We’ll be hosting the conference at King’s in central London on 1-3 September 2014. We will be having two days of presentations on science, policy, planning and practice, networking events and workshops. We’re still planning them, but we’re hoping that fieldtrips on the final day will include visits to the Thames Barrier and surrounding area and to the top of the Shard, Western Europe’s tallest building.

The theme of the conference this year is ‘Urban landscape ecology: science, policy and practice’. We are keen to hear from researchers, policymakers, and practitioners developing new evidence, policies, strategies, plans or projects on the ground that relate to the landscape ecology of urban and peri-urban areas. The call for abstracts has just gone out; please submit abstracts (300 words) for presentations and posters to conference2014@iale.org.uk by 28 February 2014. We expect selected papers will compose an edited volume on current key issues in urban landscape ecology. The full call for abstracts is copied below.

We’ll be updating the conference website regularly throughout the year as conference planning continues, so keep checking back at: http://iale.org.uk/conference2014 Further details of the conference programme and how to register will be available there soon. We’ll be using the hashtag #ialeuk2014 so please use this on social media. And any questions or queries, don’t hesitate to get in touch via conference2014@iale.org.uk

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Call for Abstracts – Urban landscape ecology: Science, policy and practice

Cities are growing rapidly. Across Europe, more than 70 per cent of people already live in urban areas, including 80 per cent of the UK population. The growth of cities poses ever-increasing challenges for the natural environment on which they impact and depend, not only within their boundaries but also in surrounding peri-urban areas. Landscape ecology – the study of interactions across space and time between the structure and function of physical, biological and cultural components of landscapes – has a pivotal role to play in identifying sustainable solutions.

This conference will consider how concepts from landscape ecology can inform the maintenance and restoration of healthy, properly functioning natural environments across urban and peri-urban landscapes, as the foundation of sustained economic growth, prospering communities and personal wellbeing.

Conference themes are likely to include: ecological connectivity of terrestrial and aquatic environments; ecosystem services, including regulation of air quality, urban heat, and water quality and quantity, as well as cultural services; planning for change; and landscape-scale management of terrestrial and aquatic ecosystems.

We are keen to hear from researchers, policymakers, and practitioners developing new evidence, policies, strategies, plans or projects on the ground that relate to the landscape ecology of urban and peri-urban areas.

Please submit abstracts (300 words) for presentations and posters to conference2014@iale.org.uk by 28 February 2014. Selected papers will compose an edited volume on current key issues in urban landscape ecology.

There will be two days of presentations on science, policy, planning and practice, networking events and workshops. We are hoping that fieldtrips on the final day will include visits to the Thames Barrier and surrounding area and to the top of the Shard, Western Europe’s tallest building, from where we can consider connectivity across London and beyond.

Further details of the conference programme and how to register will be available soon.

General enquiries: conference2014@iale.org.uk
Website: http://iale.org.uk/conference2014
Social media: #ialeuk2014

When is a pattern a pattern?

This week I received my copy of ‘Patterns of Land Degradation in Drylands: Understanding Self-Organised Ecogeomorphic Systems’ which I contributed to after participating at a workshop in Potsdam, Germany. It’s a well produced book and as I was flicking through it I came across one of the pieces I wrote. Rather than just leave it hidden away on pages 60 – 61 in the book I thought I’d reproduce it here. It’s less than 500 words and to the point. Just right for a blog post.

“In science, patterns are observations of any non-random structure. In ecology for example, a pattern has long been understood as the “structure which results from the distributions of organisms in, or from, their interactions with their environments” (Hutchinson 1953, p.3, also see Watt 1947, Greig-Smith 1979). However, when identifying patterns in nature, scientists more precisely mean the identification of patterns in data about nature. Important considerations for identifying patterns, therefore, are the means by which data were collected, and most importantly the scales of measurement used to collect data. In particular, two components of scale – grain and extent – are important in determining whether a pattern is identified. Grain is the resolution of measurement (i.e. the smallest unit of measurement at which objects or states can be distinguished), whereas extent is the full scope of observation or total range over which measurements are made. As examples, different spatial patterns will be detectable in maps of vegetation configuration in semi-arid areas depending on the grain and extent of the maps (e.g. compare Figures 3 and 6 in Barbier et al. 2006), and different temporal patterns will be detectable in storm hydrographs depending on the resolution and duration of measurement (e.g. compare drainage for 10 minute intervals with full 80 minute duration, and observed drainage with simulated drainage, in Figure 5 of Mueller et al. 2007). In other circumstances, observed structures may be described as being ‘scale-free’. These structures lack a characteristic length scale and have the same properties across any grain and extent of measurement (e.g. power-law distributions of vegetation patch sizes; Kéfi et al. 2007). These scale-free structures can also be considered to be patterns.

Because patterns are non-random, they have the potential to provide information. In natural science this information is usually understood as being about the processes that caused the pattern. Thus, identifying patterns is useful because they can be used to investigate processes (Levin 1992). Processes are typically assumed to act at a different scale from the patterns they produce, with patterns either emerging from processes at smaller scales (‘bottom-up’ processes) or imposed by constraints at larger scales (‘top-down’ processes). It is also important to consider the reciprocal effects of patterns on processes (Turner 1989). For example, the field of landscape ecology has placed an emphasis on the quantification of spatial pattern using pattern metrics (e.g. McGarigal 2006) and shown how the history of previous ecological processes can increase the strength and extent of spatial pattern (Peterson 2002). The ‘pattern-oriented modelling’ (POM) approach has been developed to use models to help decode the information present in patterns to better understand processes (Wiegand et al. 2003, Grimm et al. 2005). The POM approach iteratively compares empirical and model-output patterns at multiple scales and levels of organization and for multiple models to identify most appropriate models. Approaches like POM, which place pattern at the centre of scientific investigation, are vital for improving understanding about physical processes.”

References

  • Barbier N, Couteron P, Lejoly J et al (2006) Self-organized vegetation patterning as a fingerprint of climate and human impact on semi-arid ecosystems. J of Ecol 94:537-547
    Greig-Smith P (1979) Pattern in vegetation. J of Ecol 67: 755-779
  • Grimm V, Revilla E, Berger U et al. (2005) Pattern-oriented modeling of agent-based complex systems: Lessons from ecology. Science 310:987-991
  • Hutchinson GE (1953) The concept of pattern in ecology. Proc of the Acad of Nat Sci of Philadelphia 105:1-12
  • Kéfi S, Rietkerk M, Alados, CL et al (2007) Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449:213-217
  • Levin SA (1992) The problem of pattern and scale in ecology: The Robert H. MacArthur award lecture. Ecology 73(6):1943-1967
  • McGarigal K (2006) Landscape pattern metrics. In: El-Shaarawi AH and Piegorsch WW (eds) Encyclopedia of Environmetrics. Wiley: Chichester, UK
  • Mueller EN, Wainright J, Parsons, AJ (2007) Impact of connectivity on the modeling of overland flow within semiarid shrubland environments. Water Res Res 43:W09412
  • Peterson GD (2002) Contagious disturbance, ecological memory, and the emergence of landscape pattern. Ecosystems 5:329-338
  • Turner MG (1989) Landscape ecology: The effect of pattern on process. Ann Rev of Ecol and Syst 20:171-197
  • Watt, AS (1947) Pattern and process in the plant community. J of Ecol 35:1-22
  • Wiegand T, Jeltsch F, Hanski I et al (2003) Using pattern-oriented modeling for revealing hidden information: A key for reconciling ecological theory and application. Oikos 100:209-222

Citation
Jeltsch, F., Millington, J.D.A., et al. (2014) Resilience, self-organization, complexity and pattern formation In: Mueller, E.N., Wainwright, J., Parsons, A.J. and Turnbull, L. (Eds.) Patterns of Land Degradation in Drylands. Springer, pp. 55-84.

Heartwood Forest #kclfield Activities

Just this week our first year undergraduates had their fieldweek, with lots of geography-related activities across London. For the physical geography activities we headed up to Heartwood Forest, the largest new native forest in England (near Sandridge in Hertfordshire). A nice video from last year’s trip is below.

As you can see from the video, currently much of the ‘forest’ looks more like fields than what would be considered forest in Michigan, but the 600,000 trees being planted by volunteers will grow over the coming years to change that. There are three existing ancient woodlands (covering 45 acres) in the entire 858 acre (347 ha) area the Woodland Trust have acquired. Much of this area was previously agricultural land – the planted trees and newly created meadows will connect the existing woodland.

So there’s going to be some big ecological changes over the coming years as landscape changes. To keep track of changes in vegetation and animal populations volunteers from the Herefordshire Natural History Society (HNHS) have set up a monitoring group that regularly collect data on the growth of new trees, plants, mammals, birds and butterflies.

Similarly, some of the activities our first year undergrads undertook were to do ecological surveys of understory and overstory vegetation. Our students also did hillslope surveys and soil moisture monitoring, measured vertical wind speed profiles (to see how wind speed changes with height from the ground), explored the use of helium balloons and thermal cameras to make aerial photographs and other observations, and learned how to use global positioning system (GPS) units. The students seemed to enjoy the day at Heartwood, and the entire fieldweek for that matter, as you can see from their activities on Twitter (we use the #kclfield hastag to associate tweets with our field activities).


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Over the coming years we hope to expand our students’ field activities at Heartwood to our third year undergraduate and taught Master’s students. In particular, we hope that dissertation research by these students will be able to contribute to the efforts of the Heartwood monitoring group, to collect data and investigate questions of ecological interest. For example, to analyse presence/absence data for mammals like woodmice, we might use statistical modelling techniques like those I used to examine neotropical bird populations in Michigan.

It’s going to be very interesting watching and studying the ecological changes as Heartwood really does become a forest over the years. Keep track of the changes by visiting the forest yourself or via the HSNS website, the Heartwood blog and right here on this blog.

Writing: Landscape Ecology and Land Degradation

July was a busy month of writing. Unfortunately, it wasn’t busy writing on this blog and I failed on my New Year’s resolution to make at least one blog post each calendar month this year.

The writing I was doing was for my contribution to a new Landscape Ecology textbook I’m co-authoring with Dr Rob Francis. I’ve written and contributed to individual chapters for edited books previously (the latest highlighted below), but a whole book is a larger challenge. In particular, it’s been a useful experience thinking about how to structure the presentation of the ideas we want to address, which order they come in, what goes in each chapter, and so forth. I’ve mainly been working on the chapters on scale and disturbance, but have also been thinking about material for the heterogeneity and landscape evolution chapters. I’ve been learning a lot, revisiting old notes (including from my undergraduate lectures with Dr Perry!) and reviewing the content of others’ books. It’s been good thinking about some of the broader issues – such as the shifting-mosaic steady state and diversity-disturbance relationships – as it helps to frame more focused questions and work I’ve been thinking about and doing (including my ongoing research using Mediterranean disturbance-succession simulation modelling). When I get the chance (in amongst other things) I’ll post more here about the progression of the book, it’s aims and how it will fit in with teaching we have planned.

Just this week another book I have been involved with has become available online. Patterns of Land Degradation in Drylands: Understanding Self-Organised Ecogeomorphic Systems is the edited volume that summarises and develops the discussions we had at a workshop in Potsdam, Germany in the summer of 2010. The workshop and writing of the book, led by Eva Mueller, John Wainwright, Tony Parsons and Laura Turnbull, examine processes at the interface of ecology and geomorphology that are associated with land degradation in drylands. I contributed to the book chapters on the current state of the art in studying land degradation in drylands, on resilience, self-organization, complexity and pattern formation, and on pattern-process interrelationships and the role of ecogeomorphology. The book is the first on ecogeomorphology of drylands and contains four case studies from drylands in Europe, Africa, Australia and North America that highlight recent advances in ecogeomorphic research. It’s available online now and will be out in print soon.

Forest gap regeneration modelling

Last week the second of two papers describing our forest tree regeneration, growth, and harvest simulation model was published in Ecological Modelling. These two papers initially started out as a single manuscript, but on the recommendation of a reviewer and the editor at Ecological Modelling we split that manuscript into two. That history explains why this second paper to be published focuses on a component of the integrated model we presented a couple of months ago.

There’s a nice overview of the work these two papers contribute to on the MSU Center for System Integration and Sustainability (CSIS) website, and abstracts and citations for both papers are copied at the bottom of this blog post. Here I’ll go into a little bit more detail on the approach to our modelling:

“The model simulates the initial height of the tallest saplings 10 years following gap creation (potentially either advanced regeneration or gap colonizers), and grows them until they are at least 7 m in height when they are passed to FVS for continued simulation. Our approach does not aim to produce a thorough mechanistic model of regeneration dynamics, but rather is one that is sufficiently mechanistically-based to allow us to reliably predict regeneration for trees most likely to recruit to canopy positions from readily-collectable field data.”

In the model we assume that each forest gap contains space for a given number of 7m tall trees. For each of these spaces in a gap, we estimate the probability that it is in one of four states 10 years after harvest:

  1. occupied by a 2m or taller sugar maple tree (SM)
  2. occupied by a 2m or taller ironwood tree (IW)
  3. occupied by a 2m or taller tree of another species (OT)
  4. not occupied by a tree 2m or taller (i.e., empty, ET)

To estimate the probabilities of these states for each of the gap spaces, given different environmental conditions, we use regression modelling for composition data:

“The gap-level probability for each of the four gap-space states (i.e., composition probabilities) is estimated by a regression model for composition data (Aitchison, 1982 and Aitchison, 1986). Our raw composition data are a vector for each of our empirical gaps specifying the proportion of all saplings with height >2 m that were sugar maple, ironwood, or other species (i.e., SM, IW, and OT). If the total number of trees with height >2 m is denoted by t, the proportion of empty spaces (ET) equals zero if t > n, otherwise ET = (n − t)/n. These raw composition data provide information on the ratios of the components (i.e., gap-space states). The use of standard statistical methods with raw composition data can lead to spurious correlation effects, in part due to the absence of an interpretable covariance structure (Aitchison, 1986). However, transforming composition data, for example by taking logarithms of ratios (log-ratios), enables a mapping of the data onto the whole of real space and the use of standard unconstrained multivariate analyses (Aitchison and Egozcue, 2005). We transformed our composition data with a centred log-ratio transform using the ‘aComp’ scale in the ‘compositions’ package (van den Boogaart and Tolosana-Delgado, 2008) in R (R Development Core Team, 2009). These transformed data were then ready for use in a standard multivariate regression model. A centred log-ratio transform is appropriate in our case as our composition data are proportions (not amounts) and the difference between components is relative (not absolute). The ‘aComp’ transformation uses the centred log-ratio scalar product (Aitchison, 2001) and worked examples of the transformation computation can be found in Tolosana-Delgado et al. (2005).”

One of the things I’d like to highlight here is that the R script I wrote to do this modelling is available online as supplementary material to the paper. You can view the R script here and the data we ran it for here.

If you look at the R script you can see that for each gap, proportions of gap-spaces in the four states predicted by the regression model are interpreted as the probability that gap-space is in the corresponding state. With these probabilities we predict the state of each gap space by comparing a random value between 0 and 1 to the cumulative probabilities for each state estimated for the gap. Table 1 in the paper shows an example of this.

With this model setup we ran the model for scenarios of different soil conditions, deer densities, canopy openness and Ironwood basal area (the environmental factors in the model that influence regeneration). The results for these scenarios are shown in the figure below.


Hopefully this gives you an idea about how the model works. The paper has all the details of course, so check that out. If you’d like a copy of the paper(s) or have any questions just get in touch (email or @jamesmillington on twitter)

Millington, J.D.A., Walters, M.B., Matonis, M.S. and Liu, J. (2013) Filling the gap: A compositional gap regeneration model for managed northern hardwood forests Ecological Modelling 253 17–27
doi: 10.1016/j.ecolmodel.2012.12.033
Regeneration of trees in canopy gaps created by timber harvest is vital for the sustainability of many managed forests. In northern hardwood forests of the Great Lakes region of North America, regeneration density and composition are highly variable because of multiple drivers that include browsing by herbivores, seed availability, and physical characteristics of forest gaps and stands. The long-term consequences of variability in regeneration for economic productivity and wildlife habitat are uncertain. To better understand and evaluate drivers and long-term consequences of regeneration variability, simulation models that combine statistical models of regeneration with established forest growth and yield models are useful. We present the structure, parameterization, testing and use of a stochastic, regression-based compositional forest gap regeneration model developed with the express purpose of being integrated with the US Forest Service forest growth and yield model ‘Forest Vegetation Simulator’ (FVS) to form an integrated simulation model. The innovative structure of our regeneration model represents only those trees regenerating in gaps with the best chance of subsequently growing into the canopy (i.e., the tallest). Using a multi-model inference (MMI) approach and field data collected from the Upper Peninsula of Michigan we find that ‘habitat type’ (a proxy for soil moisture and nutrients), deer density, canopy openness and basal area of mature ironwood (Ostrya virginiana) in the vicinity of a gap drive regeneration abundance and composition. The best model from our MMI approach indicates that where deer densities are high, ironwood appears to gain a competitive advantage over sugar maple (Acer saccharum) and that habitat type is an important predictor of overall regeneration success. Using sensitivity analyses we show that this regeneration model is sufficiently robust for use with FVS to simulate forest dynamics over long time periods (i.e., 200 years).

Millington, J.D.A., Walters, M.B., Matonis, M.S. and Liu, J. (2013) Modelling for forest management synergies and trade-offs: Northern hardwood tree regeneration, timber and deer Ecological Modelling 248 103–112
doi: 10.1016/j.ecolmodel.2012.09.019
In many managed forests, tree regeneration density and composition following timber harvest are highly variable. This variability is due to multiple environmental drivers – including browsing by herbivores such as deer, seed availability and physical characteristics of forest gaps and stands – many of which can be influenced by forest management. Identifying management actions that produce regeneration abundance and composition appropriate for the long-term sustainability of multiple forest values (e.g., timber, wildlife) is a difficult task. However, this task can be aided by simulation tools that improve understanding and enable evaluation of synergies and trade-offs between management actions for different resources. We present a forest tree regeneration, growth, and harvest simulation model developed with the express purpose of assisting managers to evaluate the impacts of timber and deer management on tree regeneration and forest dynamics in northern hardwood forests over long time periods under different scenarios. The model couples regeneration and deer density sub-models developed from empirical data with the Ontario variant of the US Forest Service individual-based forest growth model, Forest Vegetation Simulator. Our error analyses show that model output is robust given uncertainty in the sub-models. We investigate scenarios for timber and deer management actions in northern hardwood stands for 200 years. Results indicate that higher levels of mature ironwood (Ostrya virginiana) removal and lower deer densities significantly increase sugar maple (Acer saccharum) regeneration success rates. Furthermore, our results show that although deer densities have an immediate and consistent negative impact on forest regeneration and timber through time, the non-removal of mature ironwood trees has cumulative negative impacts due to feedbacks on competition between ironwood and sugar maple. These results demonstrate the utility of the simulation model to managers for examining long-term impacts, synergies and trade-offs of multiple forest management actions.

Summer 2011 Papers

Since I last posted, THREE of the papers I’ve mentioned here previously have become available online! Here are their details, abstracts are below. Email me if you can’t get hold of them yourself.

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.