Quantifying the expected value of uncertain management choices for over-abundant Greylag Geese

In many parts of the world, conservation successes or global anthropogenic changes have led to increasing native species populations that then compete with human resource use. In the Orkney Islands, Scotland, a 60-fold increase in Greylag Goose Anser anser numbers over 24 years has led to agricultural damages and culling attempts that have failed to prevent population increase. To address uncertainty about why populations have increased, we combined empirical modelling of possible drivers of Greylag Goose population change with expert-elicited benefits of alternative management actions to identify whether to learn versus act immediately to reduce damages by geese. We built linear mixed-effects models relating annual goose densities on farms to land-use and environmental covariates and estimated AICc model weights to indicate relative support for six hypotheses of change. We elicited from experts the expected likelihood that one of six actions would achieve an objective of halting goose population growth, given each hypothesis for population change. Model weights and expected effects of actions were combined in Value of Information analysis (VoI) to quantify the utility of resolving uncertainty in each hypothesis through adaptive management and monitoring. The action with the highest expected value under existing uncertainty was to increase the extent of low quality habitats, whereas assuming equal hypothesis weights changed the best action to culling. VoI analysis showed that the value of learning to resolve uncertainty in any individual hypothesis for goose population change was low, due to high support for a single hypothesis of change. Our study demonstrates a two-step framework that learns about the most likely drivers of change for an over-abundant species, and uses this knowledge to weight the utility of alternative management actions. Our approach helps inform which strategies might best be implemented to resolve uncertainty when there are competing hypotheses for change and competing management choices

Quantifying the impact of Gambusia holbrooki on the extinction risk of the critically endangered red-finned blue-eye

Managing competing endangered and invasive species in spatially structured environments is challenging because it is often difficult to control invasive species without negatively impacting the endangered species. Effective management action requires an understanding of the factors affecting the presence and absence of each species so that promising sites for relocation of endangered species combined with eradication of invasive species can be identified. We investigate competing hypotheses about the factors affecting occupancy of the critically endangered red-finned blue-eye (Scaturiginichthys vermeilipinnis; hereafter 'RFBE'), a native Australian fish with a global distribution that is restricted to a group of shallow artesian springs. RFBE are threatened by competition with invasive mosquito fish (Gambusia holbrooki ), which are steadily colonizing the springs, resulting in local extinctions of RFBE in most cases. While hypotheses about the influences of Gambusia on RFBE exist, none have been tested with a quantitative model. We used a spatially-structured two-species occupancy modeling approach to examine the occupancy dynamics of these fish and tested competing hypotheses on how Gambusia occupancy affected RFBE. Gambusia occupancy had a strong negative effect on RFBE occupancy and colonization potential; increasing the probability of local extinction at a spring and decreasing the persistence probability of RFBE in a spring by 8.0%± 2.7% (mean ± 1 SE). We found strongest support for the hypotheses that elevation and spring area influence colonization, and that spring area influences patch extinction probability. Using colonization and local extinction estimates for both species, we identify promising sites for eradication of Gambusia and relocation of RFBE

An inventory of Queensland prioritised invasive plant species for management and research

There are currently ~300 declared weeds in Queensland (QLD), Australia, but this list requires a review as the last known exercise dealing with weed risk assessment and prioritisation was undertaken about two decades ago. In this paper we propose an updated list of invasive alien (plant) species of significant concern in QLD, based on information derived from pest management plans of local governments, herbarium records and published/ grey literature, expert knowledge, and opinion from diverse groups of stakeholders. Weed diversity varies significantly between local government areas and regions. Regions on the mainland eastern seaboard of the State share similar weed communities, unlike western regions and the Torres Strait islands, which share fewer, weed species. Stakeholders identified the top research and management priorities for the weed list. These were: studies involving biological control options (34.8% of respondents), public awareness and education (18.5%), effective herbicide-use and application techniques (15.2%), ecology, taxonomy and risk analysis (11.5%), and adaptive pasture management (9.3%). Based on occurrence and distribution across local government areas/regional jurisdictions and onground stakeholders’ perceived weed severity, a weed priority list of high-medium and-low impact scores for policy, research and management was compiled for each region and State-wide

Management feasibility of established invasive plant species in Queensland, Australia: A stakeholders’ perspective

Managing and monitoring invasive alien species (IAS) is costly, and because resources are limited, prioritization decisions are required for planning and management. We present findings on plant pest prioritization for 63 established invader species of natural and grazing ecosystems of Queensland, Australia. We used an expert elicitation approach to assess risk (species occurrence, spread, and impact) and feasibility of control for each IAS. We elicit semi-quantitative responses from diverse expert stakeholders to score IAS on three management approaches (biocontrol, chemical and mechanical) in relation to cost, effectiveness and practicality, and incorporate uncertainty in expert inputs and model outputs. In the process, we look for promising management opportunities as well as seek general trends across species' ecological groups and management methods. Stakeholders were cautiously optimistic about the feasibility of managing IAS. Taking into consideration all factors, the overall feasibility of control was uncorrelated with the stakeholders’ level of confidence. However, within individual management criterion, positive trend was observed for the same bivariate traits for chemical control, and negative trends for biocontrol and mechanical controls. Utility and confidence in IAS management options were in the order: chemical > biocontrol = mechanical, with practicality and effectiveness being the main driver components. Management feasibility differed significantly between IAS life forms but not between habitats invaded. Lastly, we combined IAS risk assessment and management feasibility scores to create a risk matrix to guide policy goals (i.e. eradication, spread containment, protection of sensitive sites, targeted control, site management, monitoring, and limited action). The matrix identifies promising species to target for each of these policy outcomes. Overall, our general approach illustrates (i) the importance of understanding the feasibility of IAS control actions and the factors that drive it, and (ii) demonstrates how quantifying management feasibility can be used to enhance traditional risk assessment rankings to improve policy outcomes

Setting conservation priorities for migratory networks under uncertainty

Conserving migratory species requires protecting connected habitat along the pathways they travel. Despite recent improvements in tracking animal movements, migratory connectivity remains poorly resolved at a population level for the vast majority of species, thus conservation prioritization is hampered. To address this data limitation, we developed a novel approach to spatial prioritization based on a model of potential connectivity derived from empirical data on species abundance and distance traveled between sites during migration. We applied the approach to migratory shorebirds of the East Asian-Australasian Flyway. Conservation strategies that prioritized sites based on connectivity and abundance metrics together maintained larger populations of birds than strategies that prioritized sites based only on abundance metrics. The conservation value of a site therefore depended on both its capacity to support migratory animals and its position within the migratory pathway; the loss of crucial sites led to partial or total population collapse. We suggest that conservation approaches that prioritize sites supporting large populations of migrants should, where possible, also include data on the spatial arrangement of sites

The Heritage Landscape of Plymouth, MA Greenway Planning and Cultural Design

In anticipation of the 400th anniversary of the Pilgrim’s landing in 1620, this studio combined greenway planning and cultural landscape design and interpretation to revitalize the public landscape of Plymouth, Massachusetts and convey the significance of these historic places to visitors and residents. Through the use of landscape research combining GIS data and analysis with historical narratives and maps, this fourteen-week senior BSLA capstone studio developed a regional and town-scale greenway plan along with detailed designs of individual public landscapes in historic downtown Plymouth

A General Modeling Framework for Describing Spatially Structured Population Dynamics

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network‐based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life‐history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network‐based population is modeled with discrete time steps. Using both theoretical and real‐world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network‐based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles

Vote-processing rules for combining control recommendations from multiple models

Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability