Species dispersal along rivers and streams may have variable importance to metapopulation structure

Chiu, Ming‐Chih; Ao, Sicheng; Resh, Vincent H.; He, Fengzhi; Cai, Qinghua

doi:10.1016/j.scitotenv.2020.144045

Cited by 6 publications

(6 citation statements)

References 64 publications

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“…The genetic deviation

θ_{k, l}

is modelled through the resistance‐decay genetic connection among local populations. Therefore, the covariance matrix

normalΩ

is used as a function of the environmental resistance between the local populations (Bradburd et al, 2013; Chiu et al, 2021). The environmental resistance was created along the shortest watercourse pathway (in in‐stream dispersal,

h_{ij}

) or the least‐cost pathway (in overland dispersal,

d_{ij}

) between populations i and j as:

Ω_{ij} goodbreak= ({, \begin{matrix} σ_{N}^{2} goodbreak+ \underset{a}{\underset{⏟}{σ_{D}^{2}}} goodbreak+ \underset{b}{\underset{⏟}{σ_{U}^{2}}} goodbreak+ \underset{c}{\underset{⏟}{σ_{O}^{2}}} & if i goodbreak= j \\ \underset{a}{\underset{⏟}{σ_{normalD}^{2} \exp (- (c_{normalD} h_{italicij}))}} goodbreak+ \underset{b}{\underset{⏟}{σ_{normalU}^{2} \exp (- (c_{normalU} h_{italicij}))}} & if flow goodbreak- connected \\ \underset{b}{\underset{⏟}{σ_{normalU}^{2} \exp (- (c_{normalU} h_{italicij}))}} goodbreak+ \underset{c}{\underset{⏟}{σ_{normalO}^{2} \exp (- (c_{normalO} d_{italicij}))}} & if flow goodbreak- disconnected \end{matrix}),

where parts a , b and c describe autocovariance that relies on dispersal modes.…”

Section: Methodsmentioning

confidence: 99%

“…The genetic deviation k,l is modelled through the resistancedecay genetic connection among local populations. Therefore, the covariance matrix Ω is used as a function of the environmental resistance between the local populations (Bradburd et al, 2013;Chiu et al, 2021). The environmental resistance was created along the shortest watercourse pathway (in in-stream dispersal, h ij ) or the (1)…”

Section: Study Areamentioning

confidence: 99%

See 1 more Smart Citation

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Chiu

Nukazawa

Resh

et al. 2022

Journal of Biogeography

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Aim: Both gene flow and genetic drift can affect the genetic diversity of interacting populations. However, the influence of the complex evolutionary histories of species may be obscured by simplified assumptions about how these roles operate in nature. To examine this issue, we mechanistically explored the relative importance of environmentmediated gene flow and genetic drift in determining genetic structure and diversity.Location: Northeast Japan.Taxon: Caddisflies (Hydropsyche orientalis and Stenopsyche marmorata; Insecta: Trichoptera). Methods:We developed a novel Bayesian framework to evaluate the individual and joint effects of these two evolutionary processes that operate through environmental networks on genetic structuring of these populations. Modelling was performed using genetic information on the two sympatric species of stream-macroinvertebrates having aerial-dispersing adult stages. Genotyping data for samples from 41 and 30 locations were used at 98 and 162 neutral amplified fragment length polymorphism loci in H. orientalis and S. marmorata, respectively.Results: Environmental conditions and habitat quality can mediate gene flow by providing different levels of resistance to interpopulation dispersal. Similarly, genetic drift may be altered through variable environmental limitations on population size.Compared to the role of genetic drift, gene flow has a greater influence on the genetic diversity of both species examined. The elevated gene-flow levels generally increase the genetic diversity in S. marmorata, whereas no obvious trend is evident across the various degrees of gene flow in H. orientalis.Main Conclusions: Information on environmentally mediated gene flow and genetic drift, coupled with different dispersal strategies in habitat networks, may influence management efforts and success. Consequently, evolutionary processes that act individually or jointly in determining genetic structure are important in the effectiveness of management strategies used for restoration and conservation efforts.

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“…The genetic deviation

θ_{k, l}

is modelled through the resistance‐decay genetic connection among local populations. Therefore, the covariance matrix

normalΩ

h_{ij}

) or the least‐cost pathway (in overland dispersal,

d_{ij}

) between populations i and j as:

Ω_{ij} goodbreak= ({, \begin{matrix} σ_{N}^{2} goodbreak+ \underset{a}{\underset{⏟}{σ_{D}^{2}}} goodbreak+ \underset{b}{\underset{⏟}{σ_{U}^{2}}} goodbreak+ \underset{c}{\underset{⏟}{σ_{O}^{2}}} & if i goodbreak= j \\ \underset{a}{\underset{⏟}{σ_{normalD}^{2} \exp (- (c_{normalD} h_{italicij}))}} goodbreak+ \underset{b}{\underset{⏟}{σ_{normalU}^{2} \exp (- (c_{normalU} h_{italicij}))}} & if flow goodbreak- connected \\ \underset{b}{\underset{⏟}{σ_{normalU}^{2} \exp (- (c_{normalU} h_{italicij}))}} goodbreak+ \underset{c}{\underset{⏟}{σ_{normalO}^{2} \exp (- (c_{normalO} d_{italicij}))}} & if flow goodbreak- disconnected \end{matrix}),

where parts a , b and c describe autocovariance that relies on dispersal modes.…”

Section: Methodsmentioning

confidence: 99%

Section: Study Areamentioning

confidence: 99%

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Chiu

Nukazawa

Resh

et al. 2022

Journal of Biogeography

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“…Thus, the dynamics within the meta-ecosystem can result in differential contributions to the local ecosystem, in adjacent or nearby ecosystems, and even in entire watersheds. 33,34 Figure 2. Sankey diagram of current scientific efforts used to infer biotic MP pathways at spatial or temporal scales across national or international watersheds.…”

Section: Challenges and Agendas For Meta-ecosystem Managementmentioning

confidence: 99%

Meta-ecosystem Frameworks Can Enhance Control of the Biotic Transport of Microplastics

Chiu,

Ao,

Ling

et al. 2024

Environ. Sci. Technol.

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The lack of systematic approaches and analyses to identify, quantify, and manage the biotic transport of microplastics (MPs) along cross-ecosystem landscapes prevents the current goals of sustainable environmental development from being met. This Perspective proposes a metaecosystem framework, which considers organismal and resource flows among ecosystems to shed light on the research and management challenges related to both abiotic and biotic MP transport at landscape levels. We discuss MP transport pathways through species movements and trophic transfers among ecosystems and sub-ecosystems, and highlight these pathways in the mitigation of MP pollution. The integration of biotic pathways across landscapes prioritizes management actions for MP transport using diverse approaches such as wastewater treatment and plastic removal policies to mitigate contamination. In addition, our framework emphasizes the potential sink enhancement of MPs through habitat conservation and enhancement of riparian vegetation. By considering the mechanisms of meta-ecosystem dynamics through the processes of biotic dispersal, accumulation, and the ultimate fate of MPs, advances in the environmental impact assessment and management of MP production can proceed more effectively.

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“…Unlike other freshwater organisms such as fish and algae, macroinvertebrates are highly sensitive to anthropogenic disturbance, and their community composition is strongly influenced by changes in river habitat and water quality. Consequently, they are often used to assess river ecological health and biodiversity [41][42][43][44]. Furthermore, spatial and temporal changes in benthic-macroinvertebrate species diversity represent the integration of ecological and evolutionary processes operating at different spatiotemporal scales [45].…”

Section: Introductionmentioning

confidence: 99%

Environmental Factors Shape the Differences in Biodiversity-Area Relationships in Riverine Macroinvertebrates of Two Rivers in the Tibetan Plateau in China

Li,

Chiu,

Lin

et al. 2024

Water

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The species-area relationship (SAR) is a well-established, globally recognized ecological pattern, and research on SAR has expanded to include the phylogenetic diversity-area relationship (PDAR). However, this research has generally been limited to terrestrial systems. Using data on freshwater macroinvertebrates, the log–log form of the SAR and PDAR power models were compared between the Lhasa River and the Niyang River on the Tibetan Plateau in China. The study reveals that there is a significant difference in the slopes of SAR and PDAR between the two rivers, with the Lhasa River having a considerably higher slope. The beta diversity calculations in these two basins support this pattern, with the Lhasa River exhibiting significantly higher numbers of species and greater total phylogenetic beta diversity than the Niyang River. Regarding species replacement, the turnover component was the primary driver of both species and phylogenetic beta diversity in both rivers. These differences in the beta diversity components were mainly driven by dispersal constraints because spatial distance had a large effect on total beta diversity and turnover fractions. In addition, the nestedness component was more affected by climate and land cover, indicating that highland rivers are subject to the threats of anthropogenic disturbance and climate change. Therefore, spatial factors play a crucial role in determining the distribution of passively dispersed benthic organisms as the scale of change in rivers increases from local to regional effects.

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Species dispersal along rivers and streams may have variable importance to metapopulation structure

Cited by 6 publications

References 64 publications

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Environmental effects, gene flow and genetic drift: Unequal influences on genetic structure across landscapes

Meta-ecosystem Frameworks Can Enhance Control of the Biotic Transport of Microplastics

Environmental Factors Shape the Differences in Biodiversity-Area Relationships in Riverine Macroinvertebrates of Two Rivers in the Tibetan Plateau in China

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