General rules for managing and surveying networks of pests, diseases, and endangered species

Abstract

The efficient management of diseases, pests, or endangered species is an important global issue faced by agencies constrained by limited resources. The management challenge is even greater when organisms are difficult to detect. We show how to prioritize management and survey effort across time and space for networks of susceptible-infected-susceptible subpopulations. We present simple and robust rules of thumb for protecting desirable, or eradicating undesirable, subpopulations connected in typical network patterns (motifs). We further demonstrate that these rules can be generalized to larger networks when motifs are combined in more complex formations. Results show that the best location to manage or survey a pest or a disease on a network is also the best location to protect or survey an endangered species. The optimal starting point in a network is the fastest motif to manage, where line, star, island, and cluster motifs range from fast to slow. Managing the most connected node at the right time and maintaining the same management direction provide advantages over previously recommended outside-in strategies. When a species or disease is not detected and our belief in persistence decreases, our results recommend shifting resources toward management or surveillance of the most connected nodes. Our analytic approximation provides guidance on how long we should manage or survey networks for hard-to-detect organisms. Our rules take into account management success, dispersal, economic cost, and imperfect detection and offer managers a practical basis for managing networks relevant to many significant environmental, biosecurity, and human health issues.conservation planning | decision theory | metapopulation | optimization | Markov decision process I nfectious diseases, invasive pests, and other threats to species persistence have profound impacts on human health, agriculture, and biodiversity (1-3). Many threatened or invasive species are difficult to detect and their presence in an area can be uncertain due to the imperfect nature of most detection methods (4, 5). Even large charismatic mammals such as the Sumatran tiger (Panthera tigris sumatrae) or the Sumatran rhinoceros (Dicerorrhinus sumatrensis) can be surprisingly hard to detect. It is possible that some areas are being managed while the invasive pests, diseases, or threatened species have already disappeared. It is also likely that managers, in the absence of sighting, might stop managing too early and give up too soon on a species or disease (6, 7). Managers need to know when to stop managing or surveying for species in areas of particular interest.At present the epidemiology, ecology, and conservation literature provides little guidance on how to approach such a problem. The problem of how to allocate management and surveillance effort has been studied for a single cryptic population (8-10). For example, Regan et al. (8) determined when to stop monitoring an invasive plant with a long-lived seed bank. Chadès et al. (9) determined when to stop managing ...

A Cost–Benefit Approach for Prioritizing Invasive Species

“…Chades et al (2011);Epanchin-Niell and Wilen (2012); Chalak et al (2016) and Costello et al (2017). Many papers also focus on optimal surveillance and detection strategies to limit the spread of a single invasive species with papers including Hauser and McCarthy (2009);McCarthy et al (2008); Epanchin-Niell et al (2014) and Holden et al (2016).…”

Impacts of climate change on plant diseases—opinions and trends

“…Moreover, preventive measures such as the use of cultivar mixtures in fields and the preservation of tree species diversity in forests are still likely to make sense also in the presence of novel climates (Finckh and Wolfe 1996;Garrett and Mundt 1999;Zhu et al 2000;Pautasso et al 2005;Bodin and Wiman 2007;Keesing et al 2010;Quijas et al 2010;Juroszek and von Tiedemann 2011). In addition, new approaches will be needed, from pest risk assessments including climate change and economic considerations (Yemshanov et al 2009), to involvement of the stakeholders for a certain plant pathosystem in the development of strategies to cope with the disease (for P. ramorum without considering climate change, Alexander and Lee 2010), from spatio-temporal analysis of known occurrences of a plant pathogen in the plant trade and the semi-natural environment ) to the use of network theory tools in targeting control and predicting climate change impacts (Araújo et al 2011;Chadès et al 2011;Moslonka-Lefebvre et al 2011). Innovative approaches in plant disease management will be required also given the likely increased importance of novel agroecosystems (e.g.…”

Economics of invasive species policy and management

“…Instead, resources often have been allocated based on ad hoc processes (such as dividing resources equally among states), political considerations, or relative risk maps. An increasing literature has been aimed at optimizing the design of invasive species surveillance programs (trapping programs and field surveys) to enhance cost-effectiveness in different contexts, including one-time survey programs and long-term surveillance efforts (e.g., Kaiser and Burnett 2010;Mehta et al 2007;Bogich et al 2008;Homans and Horie 2011;, 2014Horie et al 2013;Cacho and Hester 2011;Moore and McCarthy 2016;Yemshanov et al 2015;Holden et al 2016;Chadès et al 2011). Each subsequent study has typically relaxed assumptions of previous studies to increase the realism and applicability of the modeling approach for designing surveillance in specific systems.…”

“…Some economic studies of surveillance and monitoring for forest invasions also have accounted for uncertainty in knowledge about the state of invasion (e.g., about the probability that eradication has been successful or that a pest or disease is present) by enabling expectations about the probabilities of a particular state to be updated over time based on survey observations (Regan et al 2011;Haight and Polasky 2010;Rout et al 2009Rout et al , 2014Chadès et al 2011). These models determine optimal interventions based on current beliefs about the state of the system and how actions should change as beliefs are updated.…”

“…This is termed ''expected utility optimization'' and is the typical approach of many economic analyses. Other studies have instead modeled the uncertainty regarding beliefs about the true value of system parameters and allowed for the beliefs and the uncertainty surrounding them to be updated over time, typically using Bayesian approaches (e.g., Haight and Polasky 2010;Rout et al 2009Rout et al , 2014Chadès et al 2011;Springborn 2014;Burgman et al 2010). These studies optimize investments, explicitly accounting for the value of reduced uncertainty from those investments for improving management.…”