Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
Multistate No.
Program Code
Project Start Date
Mar 1, 2017
Project End Date
Feb 29, 2020
Grant Year
Project Director
Atallah, S. S.
Recipient Organization
Performing Department
Natural Resources & the Enviro
Non Technical Summary
Effective management of invasive plants is critical for the long-term ecological health of forest ecosystems and the economic vitality of communities. Increasingly, the management of forest exotic plant invasions is recognized as a joint socio-economic and scientific challenge. Thirty-five percent of U.S. forests are owned by more than 10 million individuals and families with different goals and motivations and landownership fragmentation is expected to increase. These landowners' individual invasive management decisions over time and across a forested landscape can either facilitate or impede society's ability to manage invasions and secure the provision of forest ecosystem services. The long-term goal of our proposed project is to determine to what extent private forest landownership patterns that are characteristic of the Northern and Eastern US act as drivers of a bio-invasion at the landscape level. For feasibility, this project will focus on the northeastern region of the U.S., and a specific forest invasive shrub, glossy buckthorn, which invades forest understory thereby impacting both market (MES) and nonmarket ecosystem services (NMES) in forests. The approach consists of GIS-based, statistical, spatial ecological modeling, landowner qualitative and quantitative surveys, and computational bio-economic modeling landowner invasive plant management. Specifically, this project will (1) map risk of glossy buckthorn invasion, (2) estimate costs and benefits of available and novel management strategies, (3) conduct focus groups and surveys among landowners within the invasion hot spot areas in the Northeast to estimate their perceived bio-invasion impacts and willingness to adopt available management strategies, (4) develop spatial bio-economic models to understand whether and how landowner characteristics affect negative spillovers in forest plant invasion management.
Animal Health Component
Research Effort Categories

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
Goals / Objectives
The long-term goal of our proposed project is to determine to what extent characteristic forest landownership patterns in the Northern U.S. act as drivers of a bio-invasion at the landscape level. This can then inform forest invasive plant management strategies that address the tension between individual landowner and landscape level objectives, damages, and constraints. The specific supporting objectives are as follows:Objective 1: Map ecological invasion risk of glossy buckthorn. We will synthesize existing vegetation datasets within the study region in a hierarchical Bayesian modeling framework to create a regional vegetation map and estimate the ecological invasion risk of glossy buckthorn, the representative invasive plant we choose for this proposal.Objective 2: Estimate costs and benefits of available and novel management strategies. We will build on data from ongoing field experiments to develop a partial budget for controlling glossy buckthorn in private forests using direct methods (chemical and mechanical). We will also conduct new experiments to assess the cost effectiveness of two novel, indirect glossy buckthorn strategies (herbaceous vegetation and soil compaction).Objective 3: Characterize landowner heterogeneity. We will use the bio-invasion risk map from Objective 1 as well as control strategies from Objective 2 to conduct focus groups and design contingent valuation surveys. Through this mixed-methods approach, we will characterize manager heterogeneity in forest ownership objectives, perceived impacts on MES and NMES, management horizon, current management strategies, bio-invasion management costs, and willingness to adopt alternative strategies.Objective 4: Develop bioeconomic models of bio-invasion and management at the individual land parcel and landscape levels. In the first sub-objective, we will combine cellular automata models with two-player (Nash 1953) and three-player (Haller 1986) games to construct representative, game-theoretic models of invasion management within and across land parcels, under non-cooperative and cooperative settings. Through these models, we will assess the relationship between landowner heterogeneity, the generation of spatial externalities, and individual and aggregate landowner payoffs. The second sub-objective will consist of using landowner heterogeneity survey findings (Objective 2) along with strategic behavior from the game theoretic models (first sub-objective 4) to build an agent-based model (ABM). This ABM will be overlaid on the invasion risk map (Objective 1) to produce a map that incorporates both ecological and human determinants of invasion risk. The third sub-objective consists of conducting extensive sensitivity analyses to identify ecological and socio-economic model parameters that have the highest marginal impact on model outcomes, testing whether heterogeneity in these parameters among landowners has a detrimental effect on the aggregate landowner welfare, and evaluating policy scenarios that might address these externalities.The results of our proposed project will assist private forest landowners, forestry professionals, and policy makers in making decisions that improve the long-term ecological and economic sustainability of forest ecosystems and the provisioning of MES and NMES in the northeastern U.S. This project can potentially increase the efficiency of public conservation program funds (Butler et al., 2014) and private landowner expenditures as a result of new knowledge of the ecological and socio-economic determinants of invasive plant spatial bio-invasion risk. The ecological and economic benefits of making decisions over space and time, based on ecological and socio-economic risk maps for invasive plants, can be leveraged in other bio-invasion systems in other northern and northeastern U.S. states (see Box 1 and Box 2). Understanding the landowner-level determinants of spatial externalities generated by private control decisions will help design policy recommendations that can reduce social welfare losses caused by the divergence in landownership objectives and constraints among neighboring landowners. Thus, our proposed project addresses the goal of 'sustainable agriculture' by identifying forest invasion management strategies and policy incentives for private forest landowners. Specifically, our project will contribute to minimizing bio-invasion risk that jeopardizes the provision of forest MES and NMES, enhancing the forest resource base upon which ecosystem services vital to agricultural production and the broader society are produced, sustaining the economic viability of forestry, and enhancing the quality of life for forest landowners, rural communities, and the broader society that directly or indirectly benefits from at-risk ecosystem services and faces increasing budget constraints to sustain them.
Project Methods
Proposed research activities and methods Objective 1: Map ecological invasion risk of glossy buckthorn.Vegetation Modeling. We will use a model-based approach that leverages nationally available datasets with high quality state-level data.A hierarchical Bayesian model will be developed to link national scale NLCD data (broad forest categories, 30 m resolution, Homer et al. 2015), FIA forest composition data (four clustered 1808 ft2 plots per 6,000 acres, FIA 2014), and N.H. vegetation type data (30 m resolution, Justice et al. 2002) at 1-acre resolution (to reflect private land ownership scales). Given the spatially-nested resolutions of available data and the larger analysis scale, we will rely on spatially-explicit compositional data models (Leininger et al., 2013) to classify forest cover into four types (white pine, other evergreen, hardwoods, and mixed forest) along with open vegetated habitats. Within the compositional model framework, we will use latent variable modeling to incorporate the three vegetation data sources available.The prediction area will extend to Massachusetts and southern Maine to expand the spatial coverage of the invasion risk map. Uncertainty in compositional component estimates will be quantified and mapped.Species Distribution Modeling. Glossy buckthorn invasion risk will be estimated using a cellular automaton model that relies on the local population growth of glossy buckthorn, seed SDD, and random LDD (following Merow et al., 2011).Sensitivity of model results to parameter estimates will be explored across replicate model runs, and uncertainty in predictions due to parameter selection and stochastic processes will be quantified. We will also use the portfolio-theoretic approach described in Yemshanov et al. (2012; 2013a, b, c; 2015) to map and describe invasion risk and its associated uncertainty, incorporating and illustrating the consequences of risk attitudes for invasive management (Fig. 1).Objective 2: Estimate the costs and benefits of current and novel management strategies.Partial budget. We will calculate the net present value and internal rate of return (Atallah et al., 2011; Ricketts et al., 2015) of a pine forest stand under each of the four control strategies, and a strategy of no control, over a timeframe of 30 years.Ecological field experiment. Using a field experiment at the University of New Hampshire (UNH) Kingman Farm, we will test the effectiveness of the two novel, indirect control methods in controlling buckthorn establishment and growth. We will establish three treatments. Units will be 2 m2 plots with 12 replicates per treatment. We will collect control effectiveness data, measured by the reduction in buckthorn growth, together with data on the relationship between buckthorn and pine density and growth (Lee et al., 2016, in review) and timber prices to assess the reduction in timber damage under each strategy.Objective 3: Characterize landowner heterogeneity in objectives, willingness to control, bio-invasion damages, and bio-invasion management costs. Focus groups. The focus group interviews aim to understand "social adoptability," which is instrumental to determining the feasibility of a resilient ecological system from the human perspective (Redman et al., 2004). We will collect data on landowners' awareness and perceptions of the different MES and NMES from their land and the impacts of glossy buckthorn invasion (Knudson, 1991; Nowak, 1987; Rogers, 1983), as well as on the perceptions and desirability of different systems of glossy buckthorn management strategies (Nowak, 1987). In addition to exploring landowner attitudes toward buckthorn management, the focus groups will also generate important background data for designing the landowner survey and provide a convenient setting to conduct economic experiments with participants as well as meet the requirements of a field experiment (Harrison and List, 2004). The second half of each focus group session will be devoted to experimental auctions (bidding games) to assess landowners' willingness to accept compensation for incorporating glossy buckthorn management in their current system. Uniform price and discriminative price auctions will be tested for their efficiency and suitability to the task of eliciting incentive-compatible bids.Landowner survey. We will use John Cragg's (1971) double-hurdle model to explicitly model this two-stage decision-making process. The first stage of the model, the adoption decision, is estimated using a probit regression. The second stage, the quantity decision, is estimated using a truncated regression (tobit). From this second stage, we will estimate a landowner's supply for land area enrolled in an invasive species management program. A multi-attribute choice framework will be used in order to permit estimation of the marginal willingness to accept compensation for alternative bio-invasion control and MES and NMES damage scenarios (Adamowicz et al., 1998; Boxall et al., 1996; Hanley et al., 1993; Hanley et al., 1998; Johnston et al., 1999; Lupi et al., 2002; Stevens et al., 2000).Objective 4: Develop bioeconomic models of bio-invasion and control at the individual land parcel and landscape levels.Spatial game theoretic models. We will use classical non-cooperative (simultaneous and sequential moves) and cooperative (Nash bargaining) game theory with two neighboring forest landowners to generate hypotheses over the privately and socially optimal control strategies and estimate the social cost of the externality.Agent-based model. We will develop an agent-based model (ABM) of bio-invasion and control that is discrete in both time and space.Critical bioeconomic policy parameters, hypothesis testing, and policy scenarios. First, using extensive sensitivity analyses, we will identify ecological and socio-economic model parameters that have the highest marginal impact on model outcomes, most importantly, the bio-invasion risk and the likelihood that an externality emerges due to noncooperative management. Second, in order to test the hypothesis suggesting that heterogeneity among landowners has a detrimental effect on the aggregate landowner welfare (Dayton-Johnson and Bardhan, 2002; Baland et al. 2007), we will vary the values of model parameters driving heterogeneity, the relative weights landowners put on MES and NMES, landowners' planning horizon, age, or invasive removal cost, among others, and collect data on aggregate welfare, defined as the sum of landowner expected discounted utilities. By doing so, we will identify whether and for which parameters, increased spatial heterogeneity is detrimental to welfare (as found in Atallah et al., forthcoming 2017) and whether the relationship is linear or nonlinear. (A nonlinear relationship is proposed by the theoretical model of Dayton-Johnson and Bardhan, 2002). Third, for those heterogeneity parameters exhibiting a negative relationship with welfare and that can be affected by economic instruments, we will conduct policy scenarios that aim at reducing the spatial landowner heterogeneity and collect data on welfare impacts.