Dataset Information

A new method for modelling biological invasions from early spread data accounting for anthropogenic dispersal.

ABSTRACT: Biological invasions are one of the major causes of biodiversity loss worldwide. In spite of human aided (anthropogenic) dispersal being the key element in the spread of invasive species, no framework published so far accounts for its peculiar characteristics, such as very rapid dispersal and independence from the existing species distribution. We present a new method for modelling biological invasions using historical spatio-temporal records. This method first discriminates between data points of anthropogenic origin and those originating from natural dispersal, then estimates the natural dispersal kernel. We use the expectation-maximisation algorithm for the first step; we then use Ripley's K-function as a spatial similarity metric to estimate the dispersal kernel. This is done accounting for habitat suitability and providing estimates of the inference precision. Tests on simulated data show good accuracy and precision for this method, even in the presence of challenging, but realistic, limitations of data in the invasion time series, such as gaps in the survey times and low number of records. We also provide a real case application of our method using the case of Litoria frogs in New Zealand. This method is widely applicable across the field of biological invasions, epidemics and climate change induced range shifts and provides a valuable contribution to the management of such issues. Functions to implement this methodology are made available as the R package Biolinv (https://cran.r-project.org/package=Biolinv).

SUBMITTER: Butikofer L

PROVIDER: S-EPMC6258513 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A new method for modelling biological invasions from early spread data accounting for anthropogenic dispersal.

Butikofer Luca L Jones Beatrix B Sacchi Roberto R Mangiacotti Marco M Ji Weihong W

PloS one 20181127 11

Biological invasions are one of the major causes of biodiversity loss worldwide. In spite of human aided (anthropogenic) dispersal being the key element in the spread of invasive species, no framework published so far accounts for its peculiar characteristics, such as very rapid dispersal and independence from the existing species distribution. We present a new method for modelling biological invasions using historical spatio-temporal records. This method first discriminates between data points ...[more]

PMID: 30481174

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Project description:The extent and impacts of biological invasions on biodiversity are largely shaped by an array of socio-economic and environmental factors, which exhibit high variation among countries. Yet, a global analysis of how these factors vary across countries is currently lacking. Here, we investigate how five broad, country-specific socio-economic and environmental indices (Governance, Trade, Environmental Performance, Lifestyle and Education, Innovation) explain country-level (1) established alien species (EAS) richness of eight taxonomic groups, and (2) proactive or reactive capacity to prevent and manage biological invasions and their impacts. These indices underpin many aspects of the invasion process, including the introduction, establishment, spread and management of alien species. They are also general enough to enable a global comparison across countries, and are therefore essential for defining future scenarios for biological invasions. Models including Trade, Governance, Lifestyle and Education, or a combination of these, best explained EAS richness across taxonomic groups and national proactive or reactive capacity. Historical (1996 or averaged over 1996-2015) levels of Governance and Trade better explained both EAS richness and the capacity of countries to manage invasions than more recent (2015) levels, revealing a historical legacy with important implications for the future of biological invasions. Using Governance and Trade to define a two-dimensional socio-economic space in which the position of a country captures its capacity to address issues of biological invasions, we identified four main clusters of countries in 2015. Most countries had an increase in Trade over the past 25 years, but trajectories were more geographically heterogeneous for Governance. Declines in levels of Governance are concerning as they may be responsible for larger levels of invasions in the future. By identifying the factors influencing EAS richness and the regions most susceptible to changes in these factors, our results provide novel insights to integrate biological invasions into scenarios of biodiversity change to better inform decision-making for policy and the management of biological invasions.Supplementary informationThe online version contains supplementary material available at 10.1007/s11625-022-01166-3.

Dataset Information

A new method for modelling biological invasions from early spread data accounting for anthropogenic dispersal.

Publications

A new method for modelling biological invasions from early spread data accounting for anthropogenic dispersal.

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