Biology subjects

Garrett, K. A.

Publications and source records attributed to Garrett, K. A..

10 recordsLinked to original sources

bioRxiv2026

GIRAF 1.0: A unified global framework to anticipate plant pest invasions

Plant pests threaten 10-40% of global food production, resulting in $55-220 billion in annual economic losses. Despite these escalating risks, biosecurity remains largely reactive, lacking anticipatory frameworks that integrate pest-specific drivers governing transboundary spread. We present GIRAF 1.0 (Global Invasion Risk Assessment Framework), the first quantitative, data-driven system that unifies pest-specific multi-host landscapes, abiotic suitability, and global trade networks with international phytosanitary policies. We applied GIRAF to four globally devastating pests - ranging from viral to insect taxa - to reconstruct a century of transcontinental spread and generate the first multiscale atlases of future invasion potential. GIRAF reveals that 22-37% of Earths land surface can contain host communities that largely overlap with environmentally suitable hotspots. Over 115 countries are highly vulnerable to trade-mediated pest introductions despite adopted phytosanitary policies. GIRAF provides a foundation for proactive surveillance and pandemic preparedness, offering a scalable path for transnational biosecurity agencies and global food industries.

ecology↗

bioRxiv2025

Pathogen and pest communities in food security crops across climate gradients: Anticipating future challenges in the Great Lakes region of Africa

CONTEXTTropical cropping systems must adapt to the current and future geographic distribution of pathogen and pest communities. An important research gap is how climate change may shift the distribution of pathogens and pests in tropical lowlands and highlands. OBJECTIVEWe evaluated the current geographic risk of 27 pathogens and pests in the production of four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and the potential future risk under climate change. Models for each pathogen and pest indicate the potential for changes in geographic distribution, with model fit indicating the potential for decision support systems to facilitate management. METHODSFirst, cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests, such as locations in Rwanda and Burundi. Second, we surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying patterns of association. Third, we used machine learning to develop models of each species as a function of environmental variables, including host landscape variables. We also evaluated the increase in temperature across altitudes under future climate change scenarios in this region. RESULTS AND CONCLUSIONSAmong the ten machine-learning algorithms evaluated, random forests and support vector machines generally performed best for predicting severity and infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44% of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17% may become less common. SIGNIFICANCEThis study indicates adaptation priorities for crop health in a region with multiple challenges to agricultural sustainability. The models developed here also indicate which species may have more potential and relevance for future development of pathogen and pest forecasts.

ecology↗

bioRxiv2024

Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia

CONTEXTEffective seed systems must both distribute high-performing varieties efficiently and slow or stop the spread of pathogens and pests. Epidemics increasingly threaten crops around the world, endangering the incomes and livelihoods of smallholder farmers. Responding to these food and economic security challenges requires stakeholders to act quickly and decisively during the early stages of invasions, typically with very limited resources. The recent introduction of cassava mosaic virus into southeast Asia threatens cassava production in the region. OBJECTIVESOur goal in this study is to provide a decision-support framework for efficient management of healthy seed systems, applied to cassava mosaic disease. The specific objectives are to (1) evaluate disease risk in disease-free parts of Cambodia, Lao PDR, Thailand, and Vietnam by integrating disease occurrence, climate, topology, and land use, using machine learning; (2) incorporate this predicted environmental risk with seed exchange survey data and whitefly spread in the landscape to model epidemic spread in a network meta-population model; and (3) use scenario analysis to identify candidate regions to be prioritized in seed system management. RESULTS AND CONCLUSIONSThe analyses allow stakeholders to evaluate strategy options for allocating their resources in the field, guiding the implementation of seed system programs and responses. Fixed rather than adaptive deployment of clean seed produced more favorable outcomes in this model, as did prioritization of a higher number of districts through the deployment of smaller volumes of clean seed. SIGNIFICANCEThe decision-support framework presented here can be applied widely to seed systems challenged by the dual goals of distributing seed efficiently and reducing disease risk. Data-driven approaches support evidence-based identification of optimized surveillance and mitigation areas in an iterative fashion, providing guidance early in an epidemic, and revising them as data accrue over time.

ecology↗

bioRxiv2024

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

ContextInformal seed trade can exacerbate crop disease epidemics. Potato production across Ethiopia is threatened by the spread of seedborne pests and pathogens, particularly by bacterial wilt, caused by the Ralstonia solanacearum Species Complex (RSSC). The RSSC is commonly dispersed through informal trade of seed potato, with the potential to move long distances across Ethiopia and among trading countries. Efficient disease testing programs and formal seed systems can reduce the risk of disease expansion in a countrys potato cropping system. ObjectiveIn this study, we characterized networks of potato value chain actors. We also identified candidate locations for disease surveillance and management interventions for improved seed systems, and locations at high risk for bacterial wilt establishment. We propose strategies to reduce the spread of bacterial wilt via infected seed. MethodsWe surveyed seed potato stakeholders to characterize interaction networks of potato value chain actors with a special focus on stakeholders engaged in seed potato quality assurance. We collected data regarding Ethiopias potato seed systems and analyzed the risk of spread of RSSC and other pathogens across the country through expert knowledge elicitation. Network metrics were used to characterize the informal seed trade system across Ethiopia, simulating pathogen spread across a network through scenario analyses. We characterized potato exports and imports to identify the risk of bacterial wilt movement through Ethiopias formal trading partners and neighboring countries where bacterial wilt has not yet been reported. ResultsWare potato farmers and traders were reported to have weak communication with other stakeholders in the potato value chain. In network analyses and simulated epidemics, locations in Agew Awi, Gamo, Gofa, Kembata and Tembaro zones were identified as candidate priorities for national surveillance of pathogen invasion and expansion through management interventions and formal seed system development. Ethiopia has formal trade with Sudan, Southern Sudan, Russia, and other countries where bacterial wilt has not been reported. Ethiopia may be at risk of reintroduction of the RSSC from countries where it is present, like Kenya and India. SignificanceImproving seed systems to manage R. solanacearum and other seedborne pathogens is important for supporting food security and the livelihoods of smallholder farmers in Ethiopia. Implementing surveillance systems and management programs in locations like those identified in Agew Awi, Gamo, Gofa, Kembata, and Tembaro zones, and improving the communication between ware potato traders and other stakeholders, can help to strengthen informal trade of seed potato and mitigate bacterial wilt spread in infected seed.

ecology↗

bioRxiv2024

Pathogen and pest risks to vegetatively propagated crops in humanitarian contexts: Geographic priorities for Cameroon and Ethiopia

In humanitarian contexts, supporting agricultural recovery after natural or human-driven shocks--and strengthening vulnerable communities through development interventions--is critical when crop and seed shortages threaten food security and livelihoods. Cameroon and Ethiopia face humanitarian crises that disrupt the production of key food security crops like banana and plantain, cassava, potato, and sweetpotato. In this study, we address crop pathogen and pest effects in a humanitarian context, both in general and with a focus on their effects on planting material quality through seed degeneration. We provide the foundation for a national plant health risk analysis to inform regional and national surveillance and management strategies. We analyzed cropland density maps and applied network centrality metrics to identify locations that are candidates for disease and pest management and surveillance in Cameroon and Ethiopia. We used expert knowledge elicitation and global trade data to map pathogen and pest movement risks and characterize trade of crop-specific commodities and planting materials. We identified specific locations that may be important for pathogen and pest spread in Cameroon and Ethiopia, given their roles in the cropland network, and the reported presence of pathogens and pests. These results provide a baseline national plant health risk analysis for Cameroon and Ethiopia. We discuss strategies for ongoing improvement of a national plant health risk analysis, to inform decision-making in the humanitarian sector for designing on-the-ground actions to avoid unintentional spread of pathogens and pests during agricultural recovery interventions.

ecology↗

bioRxiv2020

Impact network analysis and the INA R package: Decision support for regional management interventions

The success of intervention projects in ecological systems depends not only on the quality of management technologies, but also patterns of adoption among land managers. Impact network analysis (INA) is a new framework for evaluating the likely success of regional interventions before, during, and after projects, for project implementers, policy makers, and funders. INA integrates across three key system components in a multilayer network analysis: (a) the quality of a management technology and the quality of research supporting it, (b) the socioeconomic networks through which managers learn about management technologies and decide whether to use them, and (c) the linked biophysical network for target species success or failure in the management landscape that results from managers decisions. The specific objectives of this paper are (1) to introduce the INA framework and INA R package, (2) to illustrate identification of key nodes for smart surveillance, for networks where the likelihood of invasive species entry into the biophysical network at a given node may be based on information available to the corresponding node in the socioeconomic network, (3) to illustrate 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 (4) to illustrate the use of INA for evaluating adaptation strategies under global change scenarios with pulse and press stressors, introducing adaptation functions for sustainability and resilience. Examples of use of the INA package show one of the key outcomes of analyses: identifying when systems may be non-responsive to the system components that are readily changed through management decisions, to explore what additional adaptations may be necessary for intervention success. The broader goal for the development of impact network analysis and the INA package is 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 for analysis of sustainability, resilience, and economic viability in intervention ecology applications.

ecology↗

bioRxiv2019

Integration of system phenotypes in microbiome networks to identify candidate synthetic communities: a study of the grafted tomato rhizobiome

Understanding factors influencing microbial interactions, and designing methods to identify key taxa, are complex challenges for achieving microbiome-based agriculture. Here we study how grafting and the choice of rootstock influence root-associated fungal communities in a grafted tomato system. We studied three tomato rootstocks (BHN589, RST-04-106 and Maxifort) grafted to a BHN589 scion and profiled the fungal communities in the endosphere and rhizosphere by sequencing the Internal Transcribed Spacer (ITS2). The data provided evidence for a rootstock effect (explaining ~2% of the total captured variation, p < 0.01) on the fungal community. Moreover, the most productive rootstock, Maxifort, supported greater fungal species richness than the other rootstocks or controls. We then constructed a phenotype-OTU network analysis (PhONA) using an integrated machine learning and network analysis approach based on sequence-based fungal Operational Taxonomic Units (OTUs) and associated tomato yield data. PhONA provides a graphical framework to select a testable and manageable number of OTUs to support microbiome-enhanced agriculture. We identified differentially abundant OTUs specific to each rootstock in both endosphere and rhizosphere compartments. Subsequent analyses using PhONA identified OTUs that were directly associated with tomato fruit yield, and others that were indirectly linked to yield through their links to these OTUs. Fungal OTUs that are directly or indirectly linked with tomato yield may represent candidates for synthetic communities to be explored in agricultural systems. IMPORTANCEThe realized benefits of microbiome analyses for plant health and disease management are often limited by the lack of methods to select manageable and testable synthetic microbiomes. We evaluated the composition and diversity of root-associated fungal communities from grafted tomatoes. We then constructed a phenotype-OTU network analysis (PhONA) using these linear and network models. By incorporating yield data in the network, PhONA identified OTUs that were directly predictive of tomato yield, and others that were indirectly linked to yield through their links to these OTUs. Follow-up functional studies of taxa associated with effective rootstocks, identified using approaches like PhONA, could support the design of synthetic fungal communities for microbiome-based crop production and disease management. The PhONA framework is flexible for incorporation of other phenotypic data and the underlying models can readily be generalized to accommodate other microbiome or other omics data.

ecology↗

bioRxiv2019

Host density dependence and environmental factors affecting laurel wilt invasion

Host size, density and distribution, in addition to climate, can affect the likelihood a pathogen will invade and saturate landscapes. Laurel wilt, caused by the vector-borne forest pathogen Raffaelea lauricola, has devastated populations of native Lauraceae in the Southeastern US, and continues to spread. We surveyed 87 plots in six coastal islands in South Carolina, Georgia and North Florida, and one inland site (Archbold Biological Station) in South Florida for laurel wilt-affected and non-affected individual plants belonging to the genus Persea. The coastal island sites were surveyed once in 2008 or 2009, and the inland site was surveyed eight times from 2011 to 2013. Disease incidence per plot ranged from 0% to 96%, with mean disease incidence 45% across all sites. Disease incidence was positively correlated with trunk diameter and density of hosts with trunk diameter > 5 cm, but negatively with the degree of clustering, which was highest for small trees. A recursive partitioning model indicated that higher disease incidence was associated with moderate temperatures, wider trunk diameter, lower relative humidity, and lower wind speeds. Disease progress over time at Archbold followed a Gompertz curve, plateauing at 3% in two years. The dispersal kernel for disease incidence from a focus followed a negative exponential distribution. The number of plots with diseased trees at Archbold was similar for redbay (P. borbonia) and swampbay (P. palustris), but was lower for silkbay (P. humilis). Understanding how host density, size, and diversity interact with environmental effects will help guide future risk prediction efforts.

plant biology↗

bioRxiv2017

A risk assessment framework for seed degeneration: Informing an integrated seed health strategy for vegetatively-propagated crops

Pathogen build-up in vegetative planting material, termed seed degeneration, is a major problem in many low-income countries. When smallholder farmers use seed produced on-farm or acquired outside certified programs, it is often infected. We introduce a risk assessment framework for seed degeneration, evaluating the relative performance of individual and combined components of an integrated seed health strategy. The frequency distribution of management performance outcomes was evaluated for models incorporating biological and environmental heterogeneity, with the following results. (1) On-farm seed selection can perform as well as certified seed, if the rate of success in selecting healthy plants for seed production is high; (2) When choosing among within-season management strategies, external inoculum can determine the relative usefulness of incidence-altering management (affecting the proportion of diseased plants/seeds) and rate-altering management (affecting the rate of disease transmission in the field); (3) Under severe disease scenarios, where it is difficult to implement management components at high levels of effectiveness, combining management components can produce synergistic benefits and keep seed degeneration below a threshold; (4) Combining management components can also close the yield gap between average and worst-case scenarios. We also illustrate the potential for expert elicitation to provide parameter estimates when data are unavailable.

epidemiology↗

bioRxiv2016

Microbiome engineers: Grazers, browsers, and the phytobiome stampede

We define microbiome engineers as species that modify the microbiome associated with other host species via changes in the physical environment, potentially including the creation of dispersal networks for microbiome consortia across multiple hosts. Grazers such as bison are indirect plant microbiome engineers through alteration of the structure of plant communities in grasslands and forests. They also can directly engineer plant microbiomes if they distribute microbial consortia. Direct engineering may include simpler examples such as the role of truffle-eating animals in structuring forest mycorrhizal communities, as well as more complex roles in structuring the evolution of bacterial, fungal, and viral microbiome networks. Grazers and browsers may have important historic and current roles in engineering microbiomes by (a) stabilizing and homogenizing microbiomes in their preferred plant species, and (b) selecting for microbiomes in their preferred plant species that are differentiated from microbiomes in plant species they rarely consume.

ecology↗