Archive for the ‘Publications’ Category

Answering forest management questions

Sunday, January 8th, 2012

Although I’ve been working on new ideas since leaving Michigan and returning to London about a year ago, there’s still lots to do to examining alternative forest management strategies.

Several years ago we set out to develop a simulation model that could be used to investigate the effects of interactions between timber harvest and deer browse disturbances on economic productivity and wildlife habitat. We’ve already published several papers on this work, but just before Christmas we submitted a manuscript to Ecological Modelling entitled ‘Modelling for forest management synergies and trade-offs: Tree regeneration, timber and wildlife’. In the manuscript we report error analyses of the full simulation model (which uses the USFS Forest Vegeation Simulator) and use the model to investigate scenarios of different timber and deer management actions. Our results indicate that greater harvest of commercially low-value ironwood and lower deer densities significantly increase sugar maple regeneration success over the long term.

I expect we’ll also report some of these results at the Fourth Forest Vegetation Simulator (FVS) Conference to be held in April this year in Fort Collins, CO. Our abstract, entitled ‘Investigating combined long-term effects of variable tree regeneration and timber management on forest wildlife and timber production using FVS’, has been accepted for oral presentation. It would be great to be there myself to present the paper and discuss things with other FVS experts, but I’m not sure if that will be possible. If it’s not, Megan Matonis will present as, handily, she’s currently doing her PhD in that neck of the woods at Colorado State University.

In the meantime, Megan and I are in the process of finishing off a different manuscript describing the mesic conifer planting experiment we did in Michigan. In that experiment we planted seedlings of white pine (Pinus strobus), hemlock (Tsuga canadensis), and white spruce (Picea glauca) in northern hardwood stands with variable deer densities and then monitored browse on the seedlings over two years. We found that damage to pine and hemlock seedlings was inversely related to increasing snow depth, and our data suggest a positive relationship between hemlock browse and deer density. These results suggest that hemlock restoration efforts will not be successful without protection from deer. Hopefully we’ll submit the manuscript, possibly to the Northern Journal of Applied Forestry, in the next month or so.

All of this work has been pursued with management in mind, so it was nice this week to receive a call from Bob Doepker, a manager at the Michigan Department of Natural Resources with whom we worked to co-ordinate data collection and establish key research questions. Bob had some questions about the details and implications of our previous findings for deer habitat, tree regeneration and how they should be managed. It was good to catch up, and no doubt our ongoing work will continue to contribute to contemporary management understanding and planning.

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Agent-based models – because they’re worth it?

Thursday, December 8th, 2011

So term is drawing to an end. There’s lots been going on since I last posted here and I’ll write a full update of that over the Christmas break. I’ll just highlight here quickly that the agent-based modelling book I contributed to has now been published.

Agent-Based Models of Geographical Systems, is editied by Alison Heppenstall, Andrew Crooks, Linda See and Mike Batty and presents a comprehensive collection of papers on the background, theory, technical issues and applications of agent-based modelling (ABM) in geographical systems. David O’Sullivan, George Perry, John Wainwright and I put together a paper entitled ‘Agent-based models – because they’re worth it?’ that falls into the ‘Principles and Concepts of Agent-Based Modelling’ section of the book. To give an idea of what the paper is about, here’s the opening paragraph:

“In this chapter we critically examine the usefulness of agent-based models (ABMs) in geography. Such an examination is important be-cause although ABMs offer some advantages when considered purely as faithful representations of their subject matter, agent-based approaches place much greater demands on computational resources, and on the model-builder in their requirements for explicit and well-grounded theories of the drivers of social, economic and cultural activity. Rather than assume that these features ensure that ABMs are self-evidently a good thing – an obviously superior representation in all cases – we take the contrary view, and attempt to identify the circumstances in which the additional effort that taking an agent-based approach requires can be justified. This justification is important as such models are also typically demanding of detailed data both for input parameters and evaluation and so raise other questions about their position within a broader research agenda.”

In the paper we ask:

  • Are modellers agent-based because they should be or because they can be?
  • What are agents? And what do they do?
  • So when do agents make a difference?

To summarise our response to this last question we argue;

“Where agents’ preferences and (spatial) situations differ widely, and where agents’ decisions substantially alter the decision-making con-texts for other agents, there is likely to be a good case for exploring the usefulness of an agent-based approach. This argument focuses attention on three model features: heterogeneity of the decision-making context of agents, the importance of interaction effects, and the overall size and organization of the system.”

Hopefully people will find this, and the rest of the book useful! You can check out the full table of contents here.

Citation
O’Sullivan, D., J.D.A. Millington, G.L.W. Perry, J. Wainwright (2010) Agent-based models – because they’re worth it? p.109 – 123 In: Heppenstall, A.J., A.T. Crooks, L.M. See, M. Batty (Eds.) Agent-Based Models of Geographical Systems, Springer. DOI: 10.1007/978-90-481-8927-4_6

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Summer 2011 Papers

Thursday, July 7th, 2011

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.

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Bayesian Modelling in Biogeography

Monday, April 26th, 2010

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’)

iii) Bayesian approaches (including Bayesian Model Averaging, as I’ve discussed on this blog previously)

iv) Some applied examples (including my deer density modelling for example)

vi) A brief summary

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.

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'Mind, the Gap' paper in press

Wednesday, February 3rd, 2010

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.

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Holiday Publications!

Saturday, December 19th, 2009

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.

Keywords: wildlife distribution; landscape characteristics; managed forest; ungulate herbivory; northern hardwood; lowland conifer; white-tailed deer

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ESA 2009 Deer Density Presentation on Nature Precedings

Saturday, September 5th, 2009

The presentation I made at ESA 2009 is now online at Nature Precedings, a platform for sharing new and preliminary findings globally. The presentation itself is embedded below, or visit Nature Precedings for more details (including the abstract).

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Incendio en un Paisaje Mediterráneo

Thursday, August 6th, 2009

Our recent paper describing and testing the Mediterranean Landscape Fire Succession Model I developed during my PhD has caught the eye of some folks in Spain. sinc (Servicio de Informacion y Noticias Cientificas), a Spanish scientific news website <a href="
http://plataformasinc.es/index.php/esl/Noticias/Un-modelo-predice-la-evolucion-del-paisaje-mediterraneo-tras-los-incendios” class=”regular”, target=”_blank”>has posted details of the paper (in Spanish) – hopefully it will generate some interest in our work and that some find it useful for their own.

Update 18th August 2009
Several other websites have picked up on the sinc summary and re-published an English version:

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New Models for Ecosystems Dynamics and Restoration

Friday, July 10th, 2009

Recently I’ve been working on a review of the latest contribution to The Science and Practice of Ecological Restoration book series, entitled New Models for Ecosystems Dynamics and Restoration (edited by Hobbs and Suding). Here’s an outline of what I’ve been reading and thinking about – the formal review will appear in print in Landscape Ecology sometime in the future.

The Society for Ecological Restoration defines ecological restoration as an “intentional activity that initiates or accelerates the recovery of an ecosystem with respect to its health, integrity and sustainability”. Restoration ecology is a relatively young academic field of study that addresses problems faced by land managers and other restoration practitioners. Young et al. suggest that models of succession, community assembly and state transitions are an important component of ecological restoration, and that seed and recruitment limitation, soil processes and diversity-function relationships are also important.

The ‘new’ models referenced in the title of the book are ‘threshold’ or ‘regime shift’ ecosystem models. These models are ‘new’, the editors argue, in the sense that they contrast gradual continual models and stochastic models. Gradual continuous models are described as those that assume post-disturbance ecosystem recovery follows a continuous, gradual trajectory and are associated with classical, Clementsian theory that assumes steady, uni-directional change towards some single equilibrium state. Stochastic models assume exogenous drivers dominate the behavior of ecosystems to the extent that non-equilibrium and unstable systems states are the norm. Threshold models assume there are multiple (in contrast to the Clementsian view) stable (in contrast to the stochastic view) ecosystem states and represent changes from one relatively distinct system state to another as the result of small changes in environmental (driving) conditions. Thresholds and regime shifts are important to consider in restoration ecology as there may be thresholds in system states beyond which recovery to the previous (healthy) state is not possible.

Two types of threshold model are considered in New Models;

i) state-and-transition (S-T) models that represent multiple (often qualitative) stable states and the potential transitional relationships between those states (including the rates of transition), and

ii) alternative stable state (ASS) models which are a subset of S-T models and generally represent systems with fewer states and faster transitions (flips) between the alternative states.

For example, S-T models are often used to represent vegetation and land cover dynamics (as I did in the LFSM I developed to examine Mediterranean landscape dynamics), whereas ASS models are more frequently used for aquatic systems (e.g. lake ecosystems) and chemical/nutrient dynamics.

New Models focuses on use of these models in ecological restoration and provides an excellent introduction to key concepts and approaches in this field. Two of the six background chapters in this introduction address models and inference, two introduce transition theory and dynamics in lake and terrestrial ecosystems (respectively), and two discuss issues in social-ecological and rangeland systems. These background chapters are clear and concise, providing accessible and cogent introductions to the systems concepts that arise in the later case studies. The case studies present research and practical examples of threshold models in a range of ecosystems types – from arid, grassland, woodland and savanna ecosystems, though forest and wetland ecosystems, to ‘production landscapes’ (e.g. restoration following mining activities). Although the case study chapters are interesting examples of the current state of the use and practice of threshold modeling for ecological restoration, from my perspective there are certain issues that are insufficiently addressed. Notably, there is limited explicit consideration of spatial interactions or feedbacks between social and ecological systems.

For example, in their background chapter King and Whisenant highlight that many previous studies of thresholds in social-ecological systems have investigated an ecological system driven by a social system, ignoring feedbacks to the social components. Explicitly representing the links between social and ecological components in models does remain a daunting task, and many of the case studies continue in the same vein as the ‘uni-directional’ models King and Whisenant hint at (and I’ve discussed previously). The editors themselves highlight that detailed consideration of social systems is beyond the scope of the book and that such issues are addressed elsewhere (including in other volumes of the Ecological Restoration book series – Aronson et al.). However, representing human-environment feedbacks is becoming increasingly vital to ensure appropriate understanding of many environmental systems and their omission here may prove unsatisfactory to some.

A second shortcoming of the book, from the perspective of a landscape ecologist, is the general lack of consideration for spatial pattern and scaling and their influences on the processes considered in the case studies. In their background chapter on resilience theory and rangelands, Bestelmeyer et al. do highlight the importance of a landscape perspective and considering land as being a ‘state mosaic’, but only a single case study really picks up on these concepts in earnest (Cale and Willoughby). Other case studies do indirectly consider spatial feedbacks and landscape context, but explicit representation of relationships between spatial patterns and ecosystems processes is lacking.

However, these criticisms do need to be considered in light of the objectives of New Models. At the outset, the editors state that the book aims to collectively evaluate threshold modeling approaches as applied to ecological restoration – to examine when and where these models have been used, what evidence is used to derive and apply them, and how effective they are for guiding management. In their synthesis chapter the editors highlight that the models presented in the book have been used heuristically with little testing of their assumptions and ask; “Does this indicate an obvious gap between ecological theory and restoration practice?” For example, in their chapter on conceptual models for Australian wetlands, Sim et al. argue that the primary value of threshold models is to provide a conceptual framework of how ecosystems function relative to a variety of controlling variables. The editors’ suggestion is that restoration practitioners are applying models that work rather than “striving to prove particular elements” (of system function or ecological theory), and that maybe this isn’t such a bad approach given pressing environmental problems.

Potentially, this is a lesson that if landscape ecologists are to provide ecosystem managers and stew
ards with timely advice they may need to need to scale-back (i.e., reduce the complexity of) their modeling aims and objectives. Alternatively, we could view this situation as an opportunity for landscape ecologists to usefully contribute to advance the field of ecological restoration. Most likely it is indicative that where practical knowledge is needed quickly, simple models using established ecological theory and modelling tools are most useful. But in time, as our theoretical understanding and representation of spatial and human-environment interactions advances, these aspects will be integrated more readily into practical applications of modelling for ecological restoration.

Buy at Amazon

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Millington et al. 2009 – A landscape fire-succession model

Tuesday, June 9th, 2009

As promised, I’m hollering:

Millington, J.D.A., Wainwright, J., Perry, G.L.W., Romero-Calcerrada, R. and Malamud, B.D. (2009) Modelling Mediterranean landscape succession-disturbance dynamics: A landscape fire-succession model Environmental Modelling and Software 24 1196-1208

http://dx.doi.org/10.1016/j.envsoft.2009.03.013

Email me if you would like a reprint: jamesdamillington at gmail.com

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