Optimal dynamic control of invasions: applying a systematic conservation approach

Spatial prioritization for widespread invasive species control: Trade-offs between current impact and future spread

Spatially explicit prioritization of invasive species control is a complex issue, requiring consideration of trade-offs between immediate and future benefits. This study aimed to prioritize management efforts to account for current and future threats from widespread invasions and examine the strength of the trade-off between these different management goals. As a case study, we identified spatially explicit management priorities for the widespread invasion of introduced willow into riparian and wetland habitats across a 102,145-km2 region in eastern Australia. In addition to targeting places where willow threatens biodiversity now, a second set of management goals was to limit reinfestation and further spread that could occur via two different mechanisms (downstream and by wind). A model of likely willow distribution across the region was combined with spatial data for biodiversity (native vegetation, threatened species and communities), ecological conditions, management costs, and two potential dispersal layers. We used systematic conservation planning software (Zonation) to prioritize where willow management should be focussed across more than 100,000 catchments for a range of different scenarios that reflected different weights between management goals. For willow invasion, we found that we could prioritize willow management to reduce the future threat of dispersal downstream with little reduction in the protection of biodiversity. However, accounting for future threats from wind dispersal resulted in a stronger trade-off with protection of threatened biodiversity. The strongest trade-off was observed when both dispersal mechanisms were considered together. This study shows that considering current and future goals together offers the potential to substantially improve conservation outcomes for invasive species management. Our approach also informs land managers about the relative trade-offs among different management goals under different control scenarios, helping to make management decisions more transparent. This approach can be used for other widespread invasive species to help improve invasive species management decisions.

Costs in conservation: Common costly mistakes and how to avoid them

There has been an increasingly prevalent message that data regarding costs must be included in conservation planning activities to make cost-efficient decisions. Despite the growing acceptance that socioeconomic context is critical to conservation success, the approaches to embedded economic and financial considerations into planning have not significantly evolved. Inappropriate cost data is frequently included in decisions, with the potential of compromising biodiversity and social outcomes. For each conservation planning step, this essay details common mistakes made when considering costs, proposing solutions to enable conservation managers to know when and how to include costs. Appropriate use of high-quality cost data obtained at the right scale will improve decision-making and ultimately avoid costly mistakes.

The cost of not acting: Delaying invasive grass management increases costs and threatens assets in a national park, northern Australia

Globally, invasive grasses are a major threat to protected areas (PAs) due to their ability to alter community structure and function, reduce biodiversity, and alter fire regimes. However, there is often a mismatch between the threat posed by invasive grasses and the management response. We document a case study of the spread and management of the ecosystem-transforming invasive grass, Andropogon gayanus Kunth. (gamba grass), in Litchfield National Park; an iconic PA in northern Australia that contains significant natural, cultural and social values. We undertook helicopter-based surveys of A. gayanus across 143,931 ha of Litchfield National Park in 2014 and 2021-2022. We used these data to parametrise a spatially-explicit spread model, interfaced with a management simulation model to predict 10-year patterns of spread, and associated management costs, under three scenarios. Our survey showed that between 2014 and 2021-22 A. gayanus spread by 9463 ha, and 47%. The gross A. gayanus infestation covered 29,713 ha of the total survey area, making it the largest national park infestation in Australia. A. gayanus had not been locally eradicated within the Park's small existing 'gamba grass eradication zone', and instead increased by 206 ha over the 7-year timeframe. Our modelled scenarios predict that without active management A. gayanus will continue spreading, covering 42,388 ha of Litchfield within a decade. Alternative scenarios predict that: (i) eradicating A. gayanus in the small existing eradication zone would likely protect 18% of visitor sites, and cost ∼AU$825,000 over 5 years - more than double the original predicted cost in 2014; or (ii) eradicating A. gayanus in a much larger eradication zone would likely protect 86% of visitor sites and several species of conservation significance, and cost ∼AU$6.6 million over 5 years. Totally eradicating A. gayanus from the Park is no longer viable due to substantial spread since 2014. Our study demonstrates the value of systematic landscape-scale surveys and costed management scenarios to enable assessment and prioritisation of weed management. It also demonstrates the increased environmental and financial costs of delaying invasive grass management, and the serious threat A. gayanus poses to PAs across northern Australia.

Operations research applicability in spatial conservation planning

A large fraction of the current environmental crisis derives from the large rates of human-driven biodiversity loss. Biodiversity conservation questions human practices towards biodiversity and, therefore, largely conflicts with ordinary societal aspirations. Decisions on the location of protected areas, one of the most convincing conservation tools, reflect such a competitive endeavor. Operations Research (OR) brings a set of analytical models and tools capable of resolving the conflicting interests between ecology and economy. Recent technological advances have boosted the size and variety of data available to planners, thus challenging conventional approaches bounded on optimized solutions. New models and methods are needed to use such a massive amount of data in integrative schemes addressing a large variety of concerns. This study provides an overview on the past, present and future challenges that characterize spatial conservation models supported by OR. We discuss the progress of OR models and methods in spatial conservation planning and how those models may be optimized through sophisticated algorithms and computational tools. Moreover, we anticipate possible panoramas of modern spatial conservation studies supported by OR and we explore possible avenues for the design of optimized interdisciplinary collaborative platforms in the era of Big Data, through consortia where distinct players with different motivations and services meet. By enlarging the spatial, temporal, taxonomic and societal horizons of biodiversity conservation, planners navigate around multiple socioecological/environmental equilibria and are able to decide on cost-effective strategies to improve biodiversity persistence under complex environments.

Improving biodiversity protection through artificial intelligence

Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, substantially improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resourcelimited world.

Predicting the potential global distribution of Ageratina adenophora under current and future climate change scenarios

AIM

Invasive alien species (IAS) threaten ecosystems and humans worldwide, and future climate change may accelerate the expansion of IAS. Predicting the suitable areas of IAS can prevent their further expansion. Ageratina adenophora is an invasive weed over 30 countries in tropical and subtropical regions. However, the potential suitable areas of A. adenophora remain unclear along with its response to climate change. This study explored and mapped the current and future potential suitable areas of Ageratina adenophora.

LOCATION

Global.

TAXA

Asteraceae A. adenophora (Spreng.) R.M.King & H.Rob. Commonly known as Crofton weed.

METHODS

Based on A. adenophora occurrence data and climate data, we predicted its suitable areas of this weed under current and future (four RCPs in 2050 and 2070) by MaxEnt model. We used ArcGIS 10.4 to explore the potential suitable area distribution characteristics of this weed and the "ecospat" package in R to analyze its altitudinal distribution changes.

RESULTS

The area under the curve (AUC) value (>0.9) and true skill statistics (TSS) value (>0.8) indicated excelled model performance. Among environment factors, mean temperature of coldest quarter contributed most to the model. Globally, the suitable areas for A. adenophora invasion decreased under climate change scenarios, although regional increases were observed, including in six biodiversity hotspot regions. The potential suitable areas of A. adenophora under climate change would expand in regions with higher elevation (3,000-3,500 m).

MAIN CONCLUSIONS

Mean temperature of coldest quarter was the most important variable influencing the potential suitable area of A. Adenophora. Under the background of a warming climate, the potential suitable area of A. adenophora will shrink globally but increase in six biodiversity hotspot regions. The potential suitable area of A. adenophora would expand at higher elevation (3,000-3,500 m) under climate change. Mountain ecosystems are of special concern as they are rich in biodiversity and sensitive to climate change, and increasing human activities provide more opportunities for IAS invasion.

Spatial optimization of invasive species control informed by management practices

Optimization of spatial resource allocation is crucial for the successful control of invasive species under a limited budget but requires labor-intensive surveys to estimate population parameters. In this study, we devised a novel framework for the spatially explicit optimization of capture effort allocation using state-space population models from past capture records. We applied it to a control program for invasive snapping turtles to determine effort allocation strategies that minimize the population density over the whole area. We found that spatially heterogeneous density dependence and capture pressure limit the abundance of snapping turtles. Optimal effort allocation effectively improved the control effect, but the degree of improvement varied substantially depending on the total effort. The degree of improvement by the spatial optimization of allocation effort was only 3.21% when the total effort was maintained at the 2016 level. However, when the total effort was increased by two, four, and eight times, spatial optimization resulted in improvements of 4.65%, 8.33%, and 20.35%, respectively. To achieve the management goal for snapping turtles in our study area, increasing the current total effort by more than four times was necessary, in addition to optimizing the spatial effort. The snapping turtle population is expected to reach the target density one year after the optimal management strategy is implemented, and this rapid response can be explained by high population growth rate coupled with density-dependent feedback regulation. Our results demonstrated that combining a state-space model with optimization makes it possible to adaptively improve the management of invasive species and decision-making. The method used in this study, based on removal records from an invasive management program, can be easily applied to monitoring data for wildlife and pest control management using traps in a variety of ecosystems.

Spatial Evolution of Prosopis Invasion and its Effects on LULC and Livelihoods in Baringo, Kenya

Woody alien plant species have been deliberately introduced globally in many arid and semi-arid regions, as they can provide services and goods to the rural poor. However, some of these alien trees and shrubs have become invasive over time, with important impacts on biodiversity, ecosystem services, and human well-being. Prosopis was introduced in Baringo County, Kenya, in the 1980s, but since then, it has spread rapidly from the original plantations to new areas. To assess land-use and land-cover (LULC) changes and dynamics in Baringo, we used a combination of dry and wet season Landsat satellite data acquired over a seven-year time interval between 1988–2016, and performed a supervised Random Forest classification. For each time interval, we calculated the extent of Prosopis invasion, rates of spread, gains and losses of specific LULC classes, and the relative importance of Prosopis invasion on LULC changes. The overall accuracy and kappa coefficients of the LULC classifications ranged between 98.1–98.5% and 0.93–0.96, respectively. We found that Prosopis coverage increased from 882 ha in 1988 to 18,792 ha in 2016. The highest negative changes in LULC classes were found for grasslands (−6252 ha; −86%), irrigated cropland (−849 ha; −57%), Vachellia tortilis-dominated vegetation (−3602 ha; −42%), and rainfed cropland (−1432 ha; −37%). Prosopis invasion alone directly accounted for over 30% of these negative changes, suggesting that Prosopis invasion is a key driver of the observed LULC changes in Baringo County. Although the management of Prosopis by utilization has been promoted in Baringo for 10–15 years, the spread of Prosopis has not stopped or slowed down. This suggests that Prosopis management in Baringo and other invaded areas in East Africa needs to be based on a more integrated approach.

The need for spatially explicit quantification of benefits in invasive-species management

Worldwide, invasive species are a leading driver of environmental change across terrestrial, marine, and freshwater environments and cost billions of dollars annually in ecological damages and economic losses. Resources limit invasive-species control, and planning processes are needed to identify cost-effective solutions. Thus, studies are increasingly considering spatially variable natural and socioeconomic assets (e.g., species persistence, recreational fishing) when planning the allocation of actions for invasive-species management. There is a need to improve understanding of how such assets are considered in invasive-species management. We reviewed over 1600 studies focused on management of invasive species, including flora and fauna. Eighty-four of these studies were included in our final analysis because they focused on the prioritization of actions for invasive species management. Forty-five percent (n = 38) of these studies were based on spatial optimization methods, and 35% (n = 13) accounted for spatially variable assets. Across all 84 optimization studies considered, 27% (n = 23) explicitly accounted for spatially variable assets. Based on our findings, we further explored the potential costs and benefits to invasive species management when spatially variable assets are explicitly considered or not. To include spatially variable assets in decision-making processes that guide invasive-species management there is a need to quantify environmental responses to invasive species and to enhance understanding of potential impacts of invasive species on different natural or socioeconomic assets. We suggest these gaps could be filled by systematic reviews, quantifying invasive species impacts on native species at different periods, and broadening sources and enhancing sharing of knowledge.