All seminars take place on Tuesdays at 3pm in the Cottrell Building,
room 4B96 (Except where stated otherwise).
|29th Aug - Room 2B38
||Dr. Shadi Atallah
University of New Hampshire
|The bio-economics of managing invasive plant externalities in forests with heterogeneous landowner preferences
Forest invasive plants can cause market (MES) and non-market ecosystem service (NMES) losses to private forest landowners. Because the bio-invasion creates spatial-dynamic ecological-economic linkages among landowners, bio-invasion control is a weaker-link public good and is likely to be underprovided. We hypothesize that heterogeneity in forest landowner preferences is a major determinant of bio-invasion spatial externalities. To test this hypothesis, we develop a spatial-dynamic model of bio-invasion and control with two agents that value differently the MES and NMES produced by the forest. Landowners choose control strategies and ignore the impact of their decisions on their neighbors. In the absence of long-distance dispersal, they both control the bio-invasion regardless of their preferences. In the presence of long-distance dispersal, a central planner controls the bio-invasion as well. However, when landowners have heterogeneous preferences, the MES landowner implements bio-invasion control, but the NMES landowner does not, creating a wedge between the central planner and decentralized management solutions. We compare uniform and non-uniform payments for ecosystem services (PES) and find that a PES to the NMES landowner is enough to mitigate the externality whereas a non-uniform PES is costlier and leads to a non-additional participation of the MES landowner.
NB: This seminar will be in Room 2B38.
||Dr. Brad Duthie
University of Stirling
|GMSE: a general tool for management strategy evaluation
Management strategy evaluation (MSE) is a powerful tool for simulating all key aspects of natural resource management under conditions of uncertainty. Here I present the R package GMSE, which generalises MSE using a game-theoretic approach to simulate adaptive decision-making management scenarios between stakeholders with competing objectives under complex social-ecological interactions and uncertainty. GMSE is agent-based and spatially explicit, and incorporates a high degree of realism through mechanistic modelling of links and feedbacks among stakeholders and with the ecosystem. I demonstrate how GMSE simulates a social-ecological system using the example of a waterfowl population in an agricultural landscape that is adaptively managed; simulated waterfowl exploit agricultural land, causing conflict between conservation interests and the interest of food producers maximising their crop yield. The R package GMSE is open source under GNU Public License; source code and documents are freely available on GitHub.
||Dr. Craig Tennenhouse (Joint work with Dr. Carrie Byron)
University of New England, Maine, USA
|Commonality in structure among food web networks
A simple graph theoretic analysis of wet food webs, demonstrating the use of graph theory as a modeling technique in the applied sciences. We will examine real-world networks from geographically diverse ecological networks for common substructures using graph theory metrics and computational tools, and compare the results to randomized food webs. We will also look at landscape connectivity as another avenue of joint graph theory-ecology research.
||Dr. Paul Johnson
Institute of Biodiversity, Animal Health, and Comparative Medicine
University of Glasgow
|Modelling zoonosis transmission along livestock movement networks in Northern Tanzania
Zoonotic infectious diseases such as brucellosis, Q fever and Rift Valley Fever (RVF) cause substantial harm to people and livestock in sub-Saharan Africa. In order to predict how disease incidence will change — whether in response to control measures or changes in social, economic and environmental factors — we need to understand the processes that spread and maintain the causative pathogens at a range of scales from local (e.g. between neighbouring villages) to regional. I will describe our ongoing efforts to model long-range (10—500 km) transmission of zoonoses in Northern Tanzania among livestock (cattle, sheep and goats) populations. I will focus on the first stage of this process, in which we built a model of livestock movement using very patchy data gathered from archived paper livestock movement records. I will also briefly present another of my research interests, extending basic statistics such as power and R-squared to a very widely used class of regression models, generalised linear mixed-effects models (GLMMs).
||Dr. Scott Denholm
Animal & Veterinary Sciences Research Group
||Dr. Paul McMenemy
University of Stirling