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van Rees CB, Hand BK, Carter SC, Bargeron C, Cline TJ, Daniel W, Ferrante JA, Gaddis K, Hunter ME, Jarnevich CS, McGeoch MA, Morisette JT, Neilson ME, Roy HE, Rozance MA, Sepulveda A, Wallace RD, Whited D, Wilcox T, Kimball JS, Luikart G. A framework to integrate innovations in invasion science for proactive management. Biol Rev Camb Philos Soc 2022; 97:1712-1735. [PMID: 35451197 DOI: 10.1111/brv.12859] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/26/2022]
Abstract
Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.
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Affiliation(s)
- Charles B van Rees
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
| | - Brian K Hand
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
| | - Sean C Carter
- Numerical Terradynamic Simulation Group, University of Montana, ISB 415, Missoula, MT, 59812, U.S.A
| | - Chuck Bargeron
- Center for Invasive Species and Ecosystem Health, University of Georgia, 4601 Research Way, Tifton, GA, 31793, U.S.A
| | - Timothy J Cline
- U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way STE 2, Bozeman MT 59717 & 320 Grinnel Drive, West Glacier, MT, 59936, U.S.A
| | - Wesley Daniel
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Jason A Ferrante
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Keith Gaddis
- NASA Biological Diversity and Ecological Forecasting Programs, 300 E St. SW, Washington, DC, 20546, U.S.A
| | - Margaret E Hunter
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Catherine S Jarnevich
- U.S. Geological Survey, Fort Collins Science Center, 2150 Centre Avenue Bldg C, Fort Collins, CO, 80526, U.S.A
| | - Melodie A McGeoch
- Department of Environment and Genetics, La Trobe University, Plenty Road & Kingsbury Drive, Bundoora, Victoria, 3086, Australia
| | - Jeffrey T Morisette
- U.S. Forest Service Rocky Mountain Research Station, 26 Fort Missoula Road, Missoula, 59804, MT, U.S.A
| | - Matthew E Neilson
- U.S. Geological Survey, Wetland and Aquatic Research Center, 7920 NW 71st Street, Gainesville, FL, 32653, U.S.A
| | - Helen E Roy
- UK Centre for Ecology & Hydrology, MacLean Building, Benson Lane, Crowmarsh Gifford, OX10 8BB, U.K
| | - Mary Ann Rozance
- Northwest Climate Adaptation Science Center, University of Washington, Box 355674, Seattle, WA, 98195, U.S.A
| | - Adam Sepulveda
- U.S. Forest Service Rocky Mountain Research Station, 26 Fort Missoula Road, Missoula, 59804, MT, U.S.A
| | - Rebekah D Wallace
- Center for Invasive Species and Ecosystem Health, University of Georgia, 4601 Research Way, Tifton, GA, 31793, U.S.A
| | - Diane Whited
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
| | - Taylor Wilcox
- U.S. Forest Service Rocky Mountain Research Station, 26 Fort Missoula Road, Missoula, 59804, MT, U.S.A
| | - John S Kimball
- Numerical Terradynamic Simulation Group, University of Montana, ISB 415, Missoula, MT, 59812, U.S.A
| | - Gordon Luikart
- Flathead Lake Biological Station, University of Montana, 32125 Bio Station Lane, Polson, MT, 59860, U.S.A
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Carter S, van Rees CB, Hand BK, Muhlfeld CC, Luikart G, Kimball JS. Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout ( Oncorhynchus mykiss) in Northwest Montana. Front Big Data 2021; 4:734990. [PMID: 34734177 PMCID: PMC8558495 DOI: 10.3389/fdata.2021.734990] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/17/2021] [Indexed: 11/13/2022] Open
Abstract
Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.
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Affiliation(s)
- S Carter
- Numerical Terradynamic Simulation Group, WA Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States
| | - C B van Rees
- Flathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, MT, United States
| | - B K Hand
- Flathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, MT, United States
| | - C C Muhlfeld
- Flathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, MT, United States.,U.S. Geological Survey, Northern Rocky Mountain Science Center, Glacier National Park, West Glacier, MT, United States.,Department of Ecosystem and Conservation Sciences, WA Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States
| | - G Luikart
- Flathead Lake Biological Station, Division of Biological Sciences, University of Montana, Polson, MT, United States
| | - J S Kimball
- Numerical Terradynamic Simulation Group, WA Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States.,Department of Ecosystem and Conservation Sciences, WA Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States
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Assessing the Potential Replacement of Laurel Forest by a Novel Ecosystem in the Steep Terrain of an Oceanic Island. REMOTE SENSING 2020. [DOI: 10.3390/rs12244013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12–17% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images.
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Pyšek P, Bacher S, Kühn I, Novoa A, Catford JA, Hulme PE, Pergl J, Richardson DM, Wilson JRU, Blackburn TM. MAcroecological Framework for Invasive Aliens (MAFIA): disentangling large-scale context dependence in biological invasions. NEOBIOTA 2020. [DOI: 10.3897/neobiota.62.52787] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Macroecology is the study of patterns, and the processes that determine those patterns, in the distribution and abundance of organisms at large scales, whether they be spatial (from hundreds of kilometres to global), temporal (from decades to centuries), and organismal (numbers of species or higher taxa). In the context of invasion ecology, macroecological studies include, for example, analyses of the richness, diversity, distribution, and abundance of alien species in regional floras and faunas, spatio-temporal dynamics of alien species across regions, and cross-taxonomic analyses of species traits among comparable native and alien species pools. However, macroecological studies aiming to explain and predict plant and animal naturalisations and invasions, and the resulting impacts, have, to date, rarely considered the joint effects of species traits, environment, and socioeconomic characteristics. To address this, we present the MAcroecological Framework for Invasive Aliens (MAFIA). The MAFIA explains the invasion phenomenon using three interacting classes of factors – alien species traits, location characteristics, and factors related to introduction events – and explicitly maps these interactions onto the invasion sequence from transport to naturalisation to invasion. The framework therefore helps both to identify how anthropogenic effects interact with species traits and environmental characteristics to determine observed patterns in alien distribution, abundance, and richness; and to clarify why neglecting anthropogenic effects can generate spurious conclusions. Event-related factors include propagule pressure, colonisation pressure, and residence time that are important for mediating the outcome of invasion processes. However, because of context dependence, they can bias analyses, for example those that seek to elucidate the role of alien species traits. In the same vein, failure to recognise and explicitly incorporate interactions among the main factors impedes our understanding of which macroecological invasion patterns are shaped by the environment, and of the importance of interactions between the species and their environment. The MAFIA is based largely on insights from studies of plants and birds, but we believe it can be applied to all taxa, and hope that it will stimulate comparative research on other groups and environments. By making the biases in macroecological analyses of biological invasions explicit, the MAFIA offers an opportunity to guide assessments of the context dependence of invasions at broad geographical scales.
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Recursive Feature Elimination and Random Forest Classification of Natura 2000 Grasslands in Lowland River Valleys of Poland Based on Airborne Hyperspectral and LiDAR Data Fusion. REMOTE SENSING 2020. [DOI: 10.3390/rs12111842] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The use of hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of recursive feature elimination (RFE) in combination with random forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510, and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different spectral indices (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7–0.77 and habitats F1 accuracy around 0.8–0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation.
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From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe. SUSTAINABILITY 2020. [DOI: 10.3390/su12114508] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Biological invasions represent some of the most severe threats to local communities and ecosystems. Among invasive species, the vector-borne pathogen Xylella fastidiosa is responsible for a wide variety of plant diseases and has profound environmental, social and economic impacts. Once restricted to the Americas, it has recently invaded Europe, where multiple dramatic outbreaks have highlighted critical challenges for its management. Here, we review the most recent advances on the identification, distribution and management of X. fastidiosa and its insect vectors in Europe through genetic and spatial ecology methodologies. We underline the most important theoretical and technological gaps that remain to be bridged. Challenges and future research directions are discussed in the light of improving our understanding of this invasive species, its vectors and host–pathogen interactions. We highlight the need of including different, complimentary outlooks in integrated frameworks to substantially improve our knowledge on invasive processes and optimize resources allocation. We provide an overview of genetic, spatial ecology and integrated approaches that will aid successful and sustainable management of one of the most dangerous threats to European agriculture and ecosystems.
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