Biotic and abiotic drivers affect parasite richness, prevalence and abundance in<i>Mytilus galloprovincialis</i>along the Northern Adriatic Sea

Bommarito, Claudia; Wahl, Martin; Thieltges, David W.; Pansch, Christian; Zucchetta, Matteo; Pranovi, Fabio

doi:10.1017/s0031182021001438

Cited by 7 publications

(3 citation statements)

References 86 publications

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“…In the North Adriatic Sea prevalence of several endobionts (including Eug. inquilina ) was found to be correlated with eutrophication 79 …”

Section: Resultsmentioning

confidence: 98%

“…inquilina) was found to be correlated with eutrophication. 79 F I G U R E 6 Prevalence of Eugymnanthea japonica and Eutima japonica (considered together 26 ) and temperature trends from four localities (a, Tsushima, b, Hamada, c, Senzaki, and d, Morotsu) in North Japan with relative Spearman's correlation coefficient. Shaded areas represent peaks in bud production.…”

Section: Abiotic Factorsmentioning

confidence: 99%

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Guests or pests? Eirenid hydroids living on the soft tissue of bivalves

Camillo

Roveta

Mantas

et al. 2023

Reviews in Aquaculture

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Both wild and reared bivalves can host hydroids from the Eirenidae family which settle on the soft body parts of their host. Thousands of hydroids have been observed on a single bivalve, and often have severe detrimental effects on the host. However, this relationship has received little attention in aquaculture research, which is probably due to a lack of baseline data. An extensive review was conducted (i) to show the current level of knowledge on the association between eirenids and bivalves, providing a benchmark for future research; (ii) to detect a critical combination of biotic/abiotic factors that could switch the relationship from commensalism to infestation and (iii) to identify research priorities for future studies. Seventy scientific papers were screened to provide ecological information useful in understanding the hydroid‐host‐environment interactions and to show the global spatial and bathymetric distribution of the relationship. Although the main parameters influencing the hydroid abundance were identified, it was challenging to collate information from such heterogeneous data sources. A standardized method for data collection is proposed to obtain more robust and comparable data on the association. The most relevant and unstudied issue is the potential physiological and qualitative changes that could occur in infested bivalves. Monitoring the association could provide data needed to prevent or to control hyper‐proliferation of the symbionts and to detect eventual synergistic effects with climate change. This could be fundamental for species living in areas particularly prone to climate regime shifts, such as semi‐enclosed basins and estuarine habitats.

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“…In the North Adriatic Sea prevalence of several endobionts (including Eug. inquilina ) was found to be correlated with eutrophication 79 …”

Section: Resultsmentioning

confidence: 98%

Section: Abiotic Factorsmentioning

confidence: 99%

Guests or pests? Eirenid hydroids living on the soft tissue of bivalves

Camillo

Roveta

Mantas

et al. 2023

Reviews in Aquaculture

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“…Biotic conditions and interactions are closely linked to landscape structure (48). Specifically, biotic factors, such as species richness, distribution, population density and the structure of host communities and reservoirs are essential in the transmission of infectious or parasitic agents (49,50). Population and community structure are further associated with habitat variables, such as the amount of habitat available for species in a single land cover patch or the total amount of habitat available for species across all the different land cover patches, which can influence the type, frequency, and intensity of biotic interactions (48).…”

Section: Recommendations For Future Studiesmentioning

confidence: 99%

A global examination of ecological niche modeling to predict emerging infectious diseases: a systematic review

Lawrence,

Takenaka,

Garg

et al. 2023

Front. Public Health

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IntroductionAs emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species’ spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases?MethodsWe searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs.ResultsWe show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis.DiscussionOur findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier (CRD42021251968).

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