A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F25%3A103465" target="_blank" >RIV/60460709:41320/25:103465 - isvavai.cz</a>

Výsledek na webu

<a href="https://esajournals.onlinelibrary.wiley.com/doi/epdf/10.1002/ecm.70003?getft_integrator=clarivate&src=getftr&utm_source=clarivate" target="_blank" >https://esajournals.onlinelibrary.wiley.com/doi/epdf/10.1002/ecm.70003?getft_integrator=clarivate&src=getftr&utm_source=clarivate</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ecm.70003" target="_blank" >10.1002/ecm.70003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

Popis výsledku v původním jazyce

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as the time and place of species introduction and life-history characteristics, due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the inland commercial shipping network. Our analysis suggests that zebra mussels might have been introduced before 1981, indicating a lag of 5 years between the time of introduction and first detection in late 1986. Furthermore, using our statistical model, we estimated a one in three chance that they were introduced near Kingsville (Ontario, Canada), where they were first reported. We also find that survival, fecundity, and dispersal during early life (1-2 years) play a critical role in determining the expansion success of these mollusks. These results underscore the importance of fusing prior scientific knowledge with observation and demographic processes in a Bayesian framework for conceptual and practical understanding of how invasive species spread by human agency.

Název v anglickém jazyce

A mechanistic statistical approach to infer invasion characteristics of human-dispersed species with complex life cycle

Popis výsledku anglicky

The rising introduction of invasive species through trade networks threatens biodiversity and ecosystem services. Yet, we have a limited understanding of how transportation networks determine spatiotemporal patterns of range expansion. This knowledge gap may stem from two reasons. First, current analytical models fail to integrate the invader's life-history dynamics with heterogeneity in human-mediated dispersal patterns. Second, classical statistical methods often fail to provide reliable estimates of model parameters, such as the time and place of species introduction and life-history characteristics, due to spatial biases in the presence-only records and lack of informative demographic data. To address these gaps, we first formulate an age-structured metapopulation model that uses a probability matrix to emulate human-mediated dispersal patterns. The model reveals that an invader spreads radially along the shortest network path, such that the inter-patch network distances decrease with increasing traffic volume and reproductive value of hitchhikers. Next, we propose a hierarchical Bayesian statistical method to estimate model parameters using presence-only data and prior demographic knowledge. To show the utility of the statistical approach, we analyze zebra mussel (Dreissena polymorpha) expansion in North America through the inland commercial shipping network. Our analysis suggests that zebra mussels might have been introduced before 1981, indicating a lag of 5 years between the time of introduction and first detection in late 1986. Furthermore, using our statistical model, we estimated a one in three chance that they were introduced near Kingsville (Ontario, Canada), where they were first reported. We also find that survival, fecundity, and dispersal during early life (1-2 years) play a critical role in determining the expansion success of these mollusks. These results underscore the importance of fusing prior scientific knowledge with observation and demographic processes in a Bayesian framework for conceptual and practical understanding of how invasive species spread by human agency.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10618 - Ecology

Projekt

<a href="/cs/project/EF16_019%2F0000803" target="_blank" >EF16_019/0000803: Excelentní Výzkum jako podpora Adaptace lesnictví a dřevařství na globální změnu a 4. průmyslovou revoluci</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

ECOLOGICAL MONOGRAPHS

ISSN

0012-9615

e-ISSN

0012-9615

Svazek periodika

95

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001437031300001

EID výsledku v databázi Scopus

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