Impact network analysis and the ina r package: Decision support for regional management interventions

Abstract

1. The success of intervention projects for species management depends on both the effectiveness of management technologies and the geographical landscape of management adoption. Impact network analysis (INA) is a new framework for evaluating the likely level of success of regional species management before, during and after projects, for project implementers, policymakers and funders. INA evaluates the effects of management performance in a multilayer network analysis. The socioeconomic network represents manager communication about technologies and influence about technology adoption decisions in an agent-based model. The linked biophysical network represents species dispersal, resulting in regional success or failure of species management. 2. The specific objectives of this paper are to (a) introduce the INA framework and ina r package; (b) illustrate the identification of key nodes for smart surveillance strategies; (c) illustrate the application of the INA framework for evaluating the likely degree of success of a project in intervention ecology, before, during and after an intervention; and (d) illustrate the use of INA for evaluating adaptation strategies under global change scenarios with pulse and press stressors, introducing 'adaptation functions' to evaluate the management response required for sustainability and resilience.3. Examples of use of the ina package show key outcomes of analyses: identifying the limiting factors for effective regional management, pointing out when systems may be non-responsive to the system components that are readily changed through management decisions, and identifying the additional adaptations that may be necessary for intervention success. 4. The broader goals for the development of impact network analysis and the ina package are to provide a common framework that integrates across intervention ecology, to enhance opportunities for lessons learned across systems and scientific disciplines, to support the development of a community of practice, and to create a general platform to operationalize analysis of sustainability, resilience and economic viability in intervention ecology applications.This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The phytosanitary risks posed by seeds for sowing trade networks

“…Follow up research could involve further scenario analyses using the existing network data such as those described as “impact network analyses” [ 64 ]. A per-seed lot model could be useful, since effectively the expected numbers of incursions could be counted.…”

Translating Ethiopian potato seed networks: identifying strategic intervention points for managing bacterial wilt and other diseases

“…This suggests there is an opportunity to increase the quality of seed potato in Ethiopia by incorporating all actors in the potato seed chain through education and building disease management knowledge (Andersen Onofre et al, 2021b;Bentley et al, 2018;Damtew et al, 2018). Strengthening the stakeholder relationships could help establish a seed trade system that integrates both the formal and informal seed trade networks, in which policy makers, input dealers, and private seed companies can take the lead in seed potato certification processes and quality seed delivery to more ware potato farmers (Almekinders and Elings, 2001;Bentley et al, 2018;Choudhury et al, 2017;Garrett, 2021a;Thomas-Sharma et al, 2017).…”

Translating virome analyses to support biosecurity, on-farm management, and crop breeding

“…Pathogen surveillance can be conducted with multiple interconnected goals, including detection of outbreaks, pathogen characterization, and geographic characterization – along with characterizing the current phytosanitary measures and quarantine regulations and whether they are sufficient ( Carvajal Yepes et al., 2019 ; Kumar et al., 2019 ). Effective surveillance is based on data collection to determine the pathogen’s etiology and current distribution, and to parameterize epidemiological models of potential pathogen spread and management effects ( Parnell et al., 2017 ; Garrett, 2021 ; Mastin et al., 2022 ). Eventually, virome surveillance will be based on routine collection of genomic and ecological data, building understanding and improving knowledge of emerging and re-emerging viruses in production areas ( Carvajal Yepes et al., 2019 ).…”

“…Virome surveillance is crucial to attempts to eradicate and limit pathogens and to warn growers about new pathogens they may need to manage. Analysis of host distribution and disease dispersal across the agricultural landscape helps identify geographic priorities for surveillance and management interventions ( Xing et al., 2020 ; Garrett, 2021 ). Researchers and international agencies continue to develop new diagnostic tools implementing machine learning and AI to provide early warnings of outbreaks and support prompt and effective responses ( Pavan et al., 2011 ; Ouma et al., 2019 ).…”

“…Virome surveillance is crucial to attempts to eradicate and limit pathogens and to warn growers about new pathogens they may need to manage. Analysis of host distribution and disease dispersal across the agricultural landscape helps identify geographic priorities for surveillance and management interventions (Xing et al, 2020;Garrett, 2021).…”