Identifying Information Gaps in Predicting Winter Foraging Habitat for Juvenile Gulf Sturgeon

Abstract

The Gulf Sturgeon Acipenser oxyrinchus desotoi is an anadromous species that inhabits Gulf of Mexico coastal waters from Louisiana to Florida and is listed as threatened under the U.S. Endangered Species Act. Seasonal cues (e.g., freshwater discharge) determine the timing of spawning and migration and may influence the availability of critical habitat during winter months in six estuaries. Large information gaps, especially related to critical estuarine habitat for juveniles, hinder recovery efforts to protect these habitats and assess risks from emerging threats. Using Apalachicola Bay, Florida, as a model system, we developed and analyzed a preliminary Bayesian network model so that we could identify knowledge gaps (i.e., where expert knowledge was lacking) and data gaps (i.e., where data were unavailable) that limit the ability to assess the quantity of critical estuarine habitat for juvenile Gulf Sturgeon. The model hypothesized habitat availability per winter month in estuarine habitat under alternative scenarios of river discharge and length of the winter foraging season. A search for geospatial data sets revealed that the largest gap involved salinity, temperature, and oxygen (i.e., water condition) monitoring data, with data available only for Apalachicola Bay. For the Apalachicola Bay model, data gaps prevented the development of 53% of water condition geospatial data sets and a sensitivity analysis showed that water condition data most limited the ability to predict habitat availability. Expert knowledge was low, and conditional certainty scores showed that the relationships with the lowest certainty were abiotic suitability and habitat availability. Reducing information gaps could aid the

Collapsed oyster populations in large Florida estuaries appear resistant to restoration using traditional cultching methods—Insights from ongoing efforts in multiple systems

“…Apalachicola Bay is the only one of the study bays for which upstream reservoir operations can influence freshwater inputs (Leitman et al 2016), and freshwater inputs are what create estuarine conditions in all three bays. Continuous data on salinity and nutrient inputs before, during, and after restoration efforts are not available for the restoration sites in any of the three bays, and regionwide water quality monitoring networks are lacking (Dale et al 2021). Because of the management interest in Apalachicola Bay related to water inputs, we summarized Apalachicola River discharge as a proxy for salinity and nutrient inputs (factors that have been widely discussed to influence oyster populations; Fisch and Pine 2016; Gledhill et al 2020) before, during, and after restoration efforts.…”

Data, Information and Knowledge in Smart Maintenance Processes: A Literature Review

“…As áreas relacionadas à geração de energia receberam estudos relacionados à incorporação de conhecimentos especialistas (Strachan et al, 2008), (Varde et al, 1998). Mineração recebeu o desenvolvimento de um modelo de absorção de conhecimento tácito (Amadi-Echendu & de Smidt, 2015) e, finalmente, a área de meio ambiente recebeu a criação de um modelo para identificar informações faltantes a respeito do habitat de uma espécie marinha (Dale et al, 2021).…”

“…Nota-se, na literatura analisada, que os sensores são as ferramentas mais utilizadas para a descoberta e captura de dados ((Mönks et al, 2016), (Villarejo et al, 2017), (Xiahou et al, 2021), (Strachan et al, 2008), (Maurya et al, 2021), (Wu et al, 2018), (Varde et al, 1998), (Wakiru et al, 2019), (Phillips & Diston, 2011), (Dale et al, 2021)), seguidos de conhecimento especialista e monitoramento. A obtenção de informação tem como principal ferramenta o monitoramento de condições ( (Villarejo et al, 2017), (Xiahou et al, 2021), (Wakiru et al, 2019), (Pan et al, 2021)), seguido por algoritmos e conhecimento especialista.…”

“…A obtenção de informação tem como principal ferramenta o monitoramento de condições ( (Villarejo et al, 2017), (Xiahou et al, 2021), (Wakiru et al, 2019), (Pan et al, 2021)), seguido por algoritmos e conhecimento especialista. Algoritmos de diversos conjuntos, inclusive os aplicados em aprendizado de máquina, são os recursos mais utilizados na descoberta de conhecimento ( (Mönks et al, 2016), (Xiahou et al, 2021), (Strachan et al, 2008), (Maurya et al, 2021), (Varde et al, 1998), (Wakiru et al, 2019), (Pan et al, 2021), (Chang et al, 2021), (Verma et al, 2016), (Dale et al, 2021)), seguido por conhecimento especialista.…”

“…Hierarquia DIK Dado Menção em todos os artigos Informação (Amadi-Echendu & de Smidt, 2015;Cao et al, 2022;Dale et al, 2021;Maurya et al, 2021;Mönks et al, 2016;Pan et al, 2021;Phillips & Diston, 2011;Varde et al, 1998;Verma et al, 2016;Villarejo et al, 2017;Wakiru et al, 2019;Wu et al, 2018;Xiahou et al, 2021) Conhecimento Menção em todos os artigos Áreas de aplicação Indústria (Mönks et al, 2016), (Galar et al, 2012;Maurya et al, 2021;Xiahou et al, 2021), (Wu et al, 2018), (Wakiru et al, 2019), (Cao et al, 2022) Aviação (Phillips & Diston, 2011), (Chang et al, 2021) Ferrovias (Villarejo et al, 2017) (Varde et al, 1998) Veículos (Pan et al, 2021) Dados: exploração Sensores (Mönks et al, 2016), (Villarejo et al, 2017), (Xiahou et al, 2021), (Strachan et al, 2008), (Maurya et al, 2021), (Wu et al, 2018), (Varde et al, 1998), (Wakiru et al, 2019), (Phillips & Diston, 2011), (Dale et al, 2021) Conhecimento especialista (Xiahou et al, 2021), (Pan et al, 2021), (Phillips & Diston, 2011) Monitoramento (Villa...…”