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Predicting the Abundance and Distribution of Freshwater Fishes Under Global Change /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Predicting the Abundance and Distribution of Freshwater Fishes Under Global Change // Christopher Custer.
Author:	Custer, Christopher,
Description:	1 electronic resource (181 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Physiology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31631216
ISBN:	9798346393801

Predicting the Abundance and Distribution of Freshwater Fishes Under Global Change /
Custer, Christopher,

Predicting the Abundance and Distribution of Freshwater Fishes Under Global Change /Christopher Custer. - 1 electronic resource (181 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

Freshwater ecosystems are important components of global biodiversity and are experiencing greater rates of biodiversity loss compared to terrestrial and marine ecosystems due to anthropogenic activities and climate change. Despite containing only a small portion of Earth's water, freshwater systems contain nearly 40% of global fish biodiversity. There is a critical need to better understand the ecological drivers and effects of climate change on the distributions and abundance of freshwater fishes in order to inform management, conservation, and climate adaptation strategies. Advances in the application and development of statistical models that predict species abundance and distributions are needed for a better understanding of the relationship between fishes and environmental conditions -- both current and predicted future conditions. This is particularly important under the context of global change since many organisms are experiencing novel habitat conditions due to disturbances, including invasive species, human development, and climate change. My dissertation quantified and developed species distribution and abundance modeling frameworks that accounted for species and spatial dependencies to inform fisheries management and conservation in a changing world.Although the focus of my analyses were on freshwater fish assemblages, the quantitative techniques used and developed throughout my research are applicable to a wide array of organisms. However, freshwater fishes were ideal case studies as they are ecologically and socioeconomically important organisms worldwide, and many of the advanced statistical techniques explored within my research had only been sparsely applied to the modeling efforts of their abundance and distributions. Species distribution models that are both species and spatially dependent are relatively novel advancements in the field of quantitative ecology, and accounting for such dependencies has been shown to improve predictions and reduce uncertainty. My dissertation can be summarized into two main groupings. First, I explored the potential effectiveness and relative importance of these dependencies using stream fish distribution and abundance data. Second, I developed a species and spatially dependent abundance model that incorporated thermal physiology to scale predictions under novel climate conditions, and apply this model to ecologically and socioeconomically important lake fishes in >10,000 north temperate lakes.My first chapter sought to quantify the relative importance of biotic and abiotic factors in landscape-based models of stream fish distributions. Fish distributions are frequently predicted using remotely sensed habitat variables that characterize the adjacent landscape and serve as proxies for instream habitat. Recent advancements in statistical methodology, however, allow for leveraging fish assemblage co-occurrence data when predicting species distributions. This is important because assemblage composition (co-occurrence) likely provides better information about instream habitat compared to landscape-derived metrics and therefore may improve predictions. Joint species distribution models are a popular technique for accounting for species dependencies but are limited in the inference potential of the estimated dependencies. Thus, to better understand the value of using fish assemblage data within stream fish modeling efforts, I fit a conditional random field (CRF) model to quantify the relative importance of fish assemblage co-occurrence, landscape-derived habitat variables, and interactions between these two predictor groups. Predictive performance was also compared against single-species models. The results suggested that species co-occurrence data both improved predictive performance and were more informative than abiotic data alone.

English

ISBN: 9798346393801Subjects--Topical Terms:

518431
Physiology.

Predicting the Abundance and Distribution of Freshwater Fishes Under Global Change /
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502 $b Ph.D. $c The Pennsylvania State University $d 2024.
520 $a Freshwater ecosystems are important components of global biodiversity and are experiencing greater rates of biodiversity loss compared to terrestrial and marine ecosystems due to anthropogenic activities and climate change. Despite containing only a small portion of Earth's water, freshwater systems contain nearly 40% of global fish biodiversity. There is a critical need to better understand the ecological drivers and effects of climate change on the distributions and abundance of freshwater fishes in order to inform management, conservation, and climate adaptation strategies. Advances in the application and development of statistical models that predict species abundance and distributions are needed for a better understanding of the relationship between fishes and environmental conditions -- both current and predicted future conditions. This is particularly important under the context of global change since many organisms are experiencing novel habitat conditions due to disturbances, including invasive species, human development, and climate change. My dissertation quantified and developed species distribution and abundance modeling frameworks that accounted for species and spatial dependencies to inform fisheries management and conservation in a changing world.Although the focus of my analyses were on freshwater fish assemblages, the quantitative techniques used and developed throughout my research are applicable to a wide array of organisms. However, freshwater fishes were ideal case studies as they are ecologically and socioeconomically important organisms worldwide, and many of the advanced statistical techniques explored within my research had only been sparsely applied to the modeling efforts of their abundance and distributions. Species distribution models that are both species and spatially dependent are relatively novel advancements in the field of quantitative ecology, and accounting for such dependencies has been shown to improve predictions and reduce uncertainty. My dissertation can be summarized into two main groupings. First, I explored the potential effectiveness and relative importance of these dependencies using stream fish distribution and abundance data. Second, I developed a species and spatially dependent abundance model that incorporated thermal physiology to scale predictions under novel climate conditions, and apply this model to ecologically and socioeconomically important lake fishes in >10,000 north temperate lakes.My first chapter sought to quantify the relative importance of biotic and abiotic factors in landscape-based models of stream fish distributions. Fish distributions are frequently predicted using remotely sensed habitat variables that characterize the adjacent landscape and serve as proxies for instream habitat. Recent advancements in statistical methodology, however, allow for leveraging fish assemblage co-occurrence data when predicting species distributions. This is important because assemblage composition (co-occurrence) likely provides better information about instream habitat compared to landscape-derived metrics and therefore may improve predictions. Joint species distribution models are a popular technique for accounting for species dependencies but are limited in the inference potential of the estimated dependencies. Thus, to better understand the value of using fish assemblage data within stream fish modeling efforts, I fit a conditional random field (CRF) model to quantify the relative importance of fish assemblage co-occurrence, landscape-derived habitat variables, and interactions between these two predictor groups. Predictive performance was also compared against single-species models. The results suggested that species co-occurrence data both improved predictive performance and were more informative than abiotic data alone.
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650 4 $a Surface water. $3 3685235
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650 4 $a Creeks & streams. $3 3685269
650 4 $a Watersheds. $3 544815
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650 4 $a Rivers. $3 519737
650 4 $a Visualization. $3 586179
650 4 $a Climate change. $2 bicssc $3 2079509
650 4 $a Water temperature. $3 2131767
650 4 $a Fish. $3 3564374
650 4 $a Aquatic sciences. $3 3174300
650 4 $a Ecology. $3 516476
650 4 $a Hydrologic sciences. $3 3168407
650 4 $a Statistics. $3 517247
650 4 $a Water resources management. $3 794747
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