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Acoustic ecology of harbour porpoise (Phocoena phocoena) between two US offshore wind energy areas

ICES JOURNAL OF MARINE SCIENCE 2023年第3期82卷

作者： Holdman, A. K. Tregenza, N. Van Parijs, S. M. Deangelis, A., I NOAA Integrated Stat Contract Northeast Fisheries Sci C Natl Marine Fisheries Serv Woods Hole MA 02543 USA Chelonia Ltd Mousehole TR19 6PH Cornwall England NOAA Northeast Fisheries Sci Ctr Natl Marine Fisheries Serv Woods Hole MA 05243 USA

Offshore wind energy is set to develop rapidly in waters off the east coast of the United States. There is considerable overlap between areas proposed for offshore wind development and harbour porpoise habitats in the Gulf of Maine (GOM) and Southern New England waters. Baseline data on the occurrence and foraging activity of porpoises was collected using 10 echolocation-click detectors (F-PODs) from 2020 to 2022. Porpoises were present year-round in the GOM with peak detections in the summer and fall. In line with previous reported distribution patterns, porpoise occurrence in Southern New England was high in fall, winter and spring, but porpoises were largely absent in the summer. One site in the GOM, Mount Desert Rock (MDR), was an anomaly as porpoise detections here were highest in the winter. On average, foraging was identified in 29% of all porpoise detections, with the most foraging occurring at MDR (53%). Results from generalized additive models suggest that time of year, hour of day, lunar illumination, and temperature are significant contributors to porpoise presence and/or foraging effort. European studies show that harbour porpoises exhibit behavioural changes, disruption of foraging and displacement due to wind energy development. Therefore, early identification of areas of importance, mitigating impacts, and monitoring changes is essential for the protection of this species in US waters.

关键词： baseline data distribution foraging generalized additive models harbour porpoise offshore wind energy area passive acoustic monitoring

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Non-Stationarity of Aerosol Extinction Coefficient per Unit of Mass in Autumn and Winter in Chengdu, China

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ATMOSPHERE 2022年第7期13卷

作者： Yang, Meng Ni, Changjian Yang, Yinshan Fan, Jin Chengdu Univ Informat Technol Coll Atmospher Sci Plateau Atmosphere & Environm Key Lab Sichuan Pro Chengdu 610225 Peoples R China Chengdu Acad Environm Sci Chengdu 610072 Peoples R China

Based on hourly observation data from the aethalometer and GRIMM180 environment particle monitor as well as the simultaneous data of visibility (V), relative humidity (RH) and nitrogen dioxide (NO2) from October to December in 2017 in Chengdu, the corresponding time series of aerosol extinction coefficient per unit of mass is retrieved. The generalized additive models (GAMs) are adopted to analyze the non-stationarity of the time series of aerosol extinction coefficient per unit of mass and to explore the responses of the aerosol extinction coefficient per unit of mass to the aerosol component structure factors (rho(BC)/rho(PM10), rho(PM1)/rho(PM2.5), rho(PM1 similar to 2.5)/rho(PM2.5) and rho(PM2.5)/rho(PM10);rho represents particle mass concentration) and RH. The results show that through the comparative analysis of stationary and non-stationary models, the time series of aerosol extinction coefficient per unit of mass in autumn and winter in Chengdu is non-stationary. In addition, the RH and aerosol component structure factors are all significant nonlinear covariates that affect the non-stationarity of the aerosol extinction coefficient per unit of mass. According to the influence of covariates, the sequence is as follows: RH > rho(BC)/rho(PM10) > rho(PM2.5)/rho(PM10) > rho(PM1)/rho(PM2.5). At PM2.5 pollution concentration (rho(PM2.5) > 75 mu g m(-3)), according to the influence of covariates, the sequence is as follows: RH > rho(PM1 similar to 2.5)/rho(PM2.5) > rho(BC)/rho(PM10) > rho(PM2.5)/rho(PM10). Moreover, the interaction between RH and aerosol component structure factors significantly affects the aerosol extinction coefficient per unit of mass. The condition of high RH, high rho(PM2.5)/rho(PM10), high rho(PM1)/rho(PM2.5) and low rho(BC)/rho(PM10) has a synergistic amplification effect on the increase of the aerosol extinction coefficient per unit of mass. At PM2.5 pollution concentration, the synergistic effect of high RH, high rho(PM2.5)/rho(PM10), high

关键词： aerosol unit mass extinction coefficient non-stationarity generalized additive models

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IDENTIFICATION OF COMMERCIAL TUNA HOTSPOT IN THE SOUTHERN WATERS OF JAVA-BALI THROUGH SATELLITE REMOTE SENSING DATA

IDENTIFICATION OF COMMERCIAL TUNA HOTSPOT IN THE SOUTHERN WA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Setiawati, Martiwi Diah Rachman, Herlambang Aulia As-Syakur, Abd. Rahman Syahailatua, Augy Indonesian Inst Sci Res Ctr Oceanog Jakarta Indonesia IPB Univ Fac Fisheries & Marine Sci Dept Marine Sci & Technol Bogor Indonesia Udayana Univ Fac Marine & Fisheries Dept Marine Sci Bali Indonesia

ISBN: (纸本)9781665403696

Tuna are mesopredators in oceanic food webs which play an essential role in pelagic communities and high sea ecosystems service. Hence, understanding the environmental preference of commercial tuna species is necessary to project the hot spot and to characterize its habitat. This study used generalized additive models (GAMs) to determine the relationship between the environmental variables derived from satellite data and the abundance of commercial tuna species. We used three environmental data as predictor variables and built seven GAMs models for each tuna species. The result stated that all environmental variables, namely sea surface temperature(SST), sea surface chlorophyll (SSC), and sea surface height (SSH) gave a significant influence on the existence of commercial tuna (P<0.01) in the study area. Also, we found different spatial patterns among three types of commercial tuna. Bigeye tuna prefers to stay in lower SST, while yellowfin tuna and albacore tuna prefer to stay in a certain SST range. Furthermore, they also have a different pattern of SSH where albacore and bigeye tuna prefer to stay in the higher SSH while yellowfin tuna prefers to stay between 0.49-0.65 m. However, three species have a similar range of SSC.

关键词： generalized additive models commercial tuna species remote sensing Indian Ocean

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Spatial modeling of the relative abundance of wading birds in peninsular Florida using citizen science data

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ECOSPHERE 2023年第6期14卷

作者： Lee, Veronica Skripnikov, Andrey New Coll Florida Dept Nat Sci Sarasota FL 34243 USA

Recent literature has shown citizen science data that include reported counts, such as observations from the eBird database, can be used to model the relative abundance of selected species. The objective of our study was to extend on both the methodology and the range of species considered by previous research on relative abundance modeling;we selected the Floridian peninsula as our geographical area of interest. We confirmed that, much like for other bird species and geographical areas, modeling such data with a classic Poisson generalized linear model was not appropriate due to overdispersion. Moreover, assuming linearity for the effects of all environmental and observer effort covariates on relative abundance was shown to be overly simplistic. These concerns led us to consider a variety of potentially suitable modeling techniques;the quasi-Poisson generalized additive model displayed the best combination of flexibility, strong predictive performance, and ease of interpretation. Expanding on previous research for eBird data, we chose to explicitly incorporate spatial dependence into the modeling task by performing hierarchical generalized additive modeling with a spatial conditional autoregressive structure for random effects. This practice allows us to account for spatial factors not represented by model covariates, such as species dispersal patterns or colonial breeding habits. After conducting appropriate testing procedures and fitting spatially explicit models, we found that our data contain moderate spatial dependence and that spatially explicit models have overall superior predictive performance to models that do not account for spatial dependence. Upon closer inspection, spatially explicit models perform especially well compared with their nonspatial counterparts in identifying situations with low abundance, with a negligible trade-off in prediction accuracy for situations with high abundance. We conclude that quasi-Poisson hierarchical generalized additive

关键词： citizen science eBird generalized additive models hierarchical modeling relative abundance spatial dependence species distribution modeling

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Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 125卷

作者： Ahmed, Mahnoor Tanyas, Hakan Dahal, Ashok Titti, Giacomo Borgatti, Lisa Francioni, Mirko Lombardo, Luigi Huser, Raphael Univ Urbino Carlo Bo Dept Pure & Appl Sci Via Ca Suore2-4Campus Sci Enrico Mattei I-61029 Urbino Italy Univ Twente Fac Geoinformat Sci & Earth Observat ITC POB 217 NL-7500AE Enschede Netherlands King Abdullah Univ Sci & Technol KAUST Stat Program Comp Elect & Math Sci & Engn CEMSE Div Thuwal 239556900 Saudi Arabia Alma Mater Studiorum Univ Bologna Dept Civil Chem Environm & Mat Engn Bologna Italy

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial generalized additive models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions. This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine.

关键词： Dynamic susceptibility Landslide prediction Early warning system generalized additive models

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Influence of vertical structure of water temperature on the spatial and temporal distribution of the Sthenoteuthis oualaniensis fishery in the Northwest Indian Ocean

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Aquaculture and Fisheries 2025年

作者： Lu, Huajie Yang, Sizhe Zhao, Maolin Zhang, Biqiang Sun, Tianzi Guo, Ruiyu College of Marine Sciences Shanghai Ocean University Shanghai 201306 China Key Laboratory of Marine Ecological Monitoring and Restoration Technologies MNRs Shanghai 201306 China National Distant-Water Fisheries Engineering Research Center Shanghai Ocean University Shanghai 201306 China Key Laboratory of Oceanic Fisheries Exploration Ministry of Agriculture and Rural Affairs Shanghai Ocean University Shanghai 201306 China

Sthenoteuthis oualaniensis, a commercially important cephalopod in China, exhibits distinct diurnal vertical movements and thrives in warm waters. We investigated the relationship between seawater temperature and S. oualaniensis abundance in the Northwest Indian Ocean (2017–2020) using Chinese squid fishery data. We analyzed temperature data from 0 to 300 m depths at 50 m intervals and calculated temperature differentials. generalized additive models (GAM) predicted the fishery's spatial and temporal distribution. The optimal GAM explained 69.1% of the variance. Key contributors were latitude-longitude interactions (Lon, Lat), month, sea surface temperature (SST) T0, and seawater temperature (SWT) T200 at 200 m. The 50–100 m depth layer had the most influence. Fishing grounds were influenced by temperature variations and sea surface conditions. The optimal sea surface temperature range for S. oualaniensis was 27–30 °C, with prime areas at 58°-64°E and 14°-19°N. Seasonal variability was observed, with higher yields in fall and winter. The high-value area shifted northward over time. This GAM model improves predictions of S. oualaniensis distribution and demonstrates the impact of vertical temperature structures. It provides valuable insights for fishery production and offers a new approach for studying thermophilic species with vertical behaviors. © 2025 Shanghai Ocean University.

关键词： generalized additive models Northwest Indian Ocean Spatial and temporal distribution Sthenoteuthis oualaniensis Vertical water temperature

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generalized additive Regression models of Discharge and Mean Velocity Associated with Direct-Runoff Conditions in Texas: Utility of the US Geological Survey Discharge Measurement Database

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JOURNAL OF HYDROLOGIC ENGINEERING 2013年第10期18卷 1331-1348页

作者： Asquith, William H. Herrmann, George R. Cleveland, Theodore G. Texas Tech Univ Lubbock Field Off US Geol Survey Lubbock TX 79409 USA Texas Tech Univ Dept Civil & Environm Engn Lubbock TX 79409 USA Texas Dept Transportat Austin TX 78701 USA

A database containing more than 17,700 discharge values and ancillary hydraulic properties was assembled from summaries of discharge measurement records for 424 U.S. Geological Survey streamflow-gauging stations (stream gauges) in Texas. Each discharge exceeds the 90th-percentile daily mean streamflow as determined by period-of-record, stream-gauge-specific, flow-duration curves. Each discharge therefore is assumed to represent discharge measurement made during direct-runoff conditions. The hydraulic properties of each discharge measurement included concomitant cross-sectional flow area, water-surface top width, and reported mean velocity. Systematic and statewide investigation of these data in pursuit of regional models for the estimation of discharge and mean velocity has not been previously attempted. generalized additive regression modeling is used to develop readily implemented procedures by end-users for estimation of discharge and mean velocity from select predictor variables at ungauged stream locations. The discharge model uses predictor variables of cross-sectional flow area, top width, stream location, mean annual precipitation, and a generalized terrain and climate index (OmegaEM) derived for a previous flood-frequency regionalization study. The mean velocity model uses predictor variables of discharge, top width, stream location, mean annual precipitation, and OmegaEM. The discharge model has an adjusted R-squared value of about 0.95 and a residual standard error (RSE) of about 0.22 base-10 logarithm (cubic meters per second);the mean velocity model has an adjusted R-squared value of about 0.67 and an RSE of about 0.063 fifth root (meters per second). Example applications and computations using both regression models are provided.

关键词： Streamflow Discharge measurement Velocity Hydraulic properties Runoff Regional analysis Texas Databases U S Geological Survey Streamflow Stream gauge Discharge measurement Mean velocity Hydraulic properties Direct runoff Regional analysis Regression generalized additive models Texas

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Hegyi competition index decomposition to improve estimation accuracy of Larix olgensis crown radius

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ECOLOGICAL INDICATORS 2022年 143卷

作者： Sun, Zhao Wang, Yifu Pan, Lei Sun, Yujun Beijing Forestry Univ State Forestry & Grassland Adm Key Lab Forest Reso Beijing 100083 Peoples R China Inst Natl Forestry Grassland & Adm Northwest Surveying Planning & Designing Beijing Peoples R China

The current individual tree competition indexes cannot provide information on the competitive pressure of trees in different directions. To quantitatively describe the magnitude of competitive pressure on trees in different directions, this study proposes a method to calculate the magnitude of competitive pressure in the east, west, south, and north directions based on the Hegyi competition index. The Hegyi competition index was assumed as a vector and it is decomposed in two vertical directions by attributing a weight in each direction. Under the condition that the Hegyi index is constant, the Hegyi index is allocated to the four directions. Correlation analysis between the Hegyi competition index components in the four directions of trees and the crown radius of the corresponding azimuth was carried out. The crown radius prediction model containing the Hegyi competition index component was established by using generalized additive models (GAMs). The results show that the Hegyi competition index component was significantly negatively correlated with the crown radius in the corresponding direction, and the correlation coefficient with the crown radius in the corresponding direction was the largest compared with other directions. The crown radius in the four directions decreased with the increase of the Hegyi competition index component in the corresponding direction, indicating that the competition pressure on the crown in a specific direction inhibited the growth and extension of the crown in this direction. After adding the Hegyi competition index component, the fitting effect of crown radius models in different directions is greatly improved. In general, the Hegyi competition index component quantitatively expresses the competitive pressure of trees in different directions. The proposed method has particular significance for the study of tree crown morphology and provides a new idea for studying the competitive growth relationship of plants.

关键词： Hegyi competition index decomposition Crown radius model Crown morphology generalized additive models

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The Influence of Satellite-Derived Environmental and Oceanographic Parameters on Marine Turtle Time at Surface in the Gulf of Mexico

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REMOTE SENSING 2022年第18期14卷

作者： Roberts, Kelsey E. Garrison, Lance P. Ortega-Ortiz, Joel Hu, Chuanmin Zhang, Yingjun Sasso, Christopher R. Lamont, Margaret Hart, Kristen M. US Geol Survey Cherokee Nat Technol Wetland & Aquat Res Ctr Davie FL 33314 USA Louisiana State Univ Dept Oceanog & Coastal Sci Baton Rouge LA 70803 USA NOAA Southeast Fisheries Sci Ctr Miami FL 33149 USA Univ Miami Cooperat Inst Marine & Atmospher Studies Miami FL 33149 USA Univ S Florida Coll Marine Sci St Petersburg FL 33701 USA US Geol Survey Wetland & Aquat Res Ctr Gainesville FL 32653 USA US Geol Survey Wetland & Aquat Res Ctr Davie FL 33314 USA

The aftermath of the 2010 Deepwater Horizon oil spill highlighted the lack of baseline spatial, behavioral, and abundance data for many species, including imperiled marine turtles, across the Gulf of Mexico. The ecology of marine turtles is closely tied to their vertical movements within the water column and is therefore critical knowledge for resource management in a changing ocean. A more comprehensive understanding of diving behavior, specifically surface intervals, can improve the accuracy of density and abundance estimates by mitigating availability bias. Here, we focus on the proportion of time marine turtles spend at the top 2 m of the water column to coincide with depths where turtles are assumed visible to observers during aerial surveys. To better understand what environmental and oceanographic conditions influence time at surface, we analyzed dive and spatial data from 136 satellite tags attached to three species of threatened or endangered marine turtles across 10 years. We fit generalized additive models with 11 remotely sensed covariates, including sea surface temperature (SST), bathymetry, and salinity, to examine dive patterns. Additionally, the developed model is the first to explicitly examine the potential connection between turtle dive patterns and ocean frontal zones in the Gulf of Mexico. Our results show species-specific associations of environmental covariates related to increased time at surface, particularly for depth, salinity, and frontal features. We define seasonal and spatial variation in time-at-surface patterns in an effort to contribute to marine turtle density and abundance estimates. These estimates could then be utilized to generate correction factors for turtle detection availability during aerial surveys.

关键词： dive behavior marine turtle Gulf of Mexico satellite remote sensing generalized additive models bio-logging

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A generalized additive Model approach to evaluating water quality: Chesapeake Bay case study

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ENVIRONMENTAL MODELLING & SOFTWARE 2019年 118卷 1-13页

作者： Murphy, Rebecca R. Perry, Elgin Harcum, Jon Keisman, Jennifer Univ Maryland Chesapeake Bay Program Off Ctr Environm Sci 410 Severn AveSuite 112 Annapolis MD 21403 USA 377 Resolut Rd Colonial Beach VA 22443 USA Tetra Tech Inc 3475 East Foothill Blvd Pasadena CA 91107 USA US Geol Survey 5522 Res Pk Dr Baltimore MD 21228 USA

Nutrient reduction efforts have been undertaken in recent decades to mitigate the impacts of eutrophication in coastal and estuarine systems worldwide. To track progress in response to one of these efforts we use generalized additive models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, an estuary on the east coast of the United States. Model development included selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporating hydrologic variability via either river flow or salinity, and using interventions to model method or laboratory changes suspected to impact data. This approach, transferable to other systems, allows for evaluation of water quality data in a statistically rigorous way, while being suitable for application to many sites and variables. This enables consistent generation of annual updates, while providing a tool for developing insights to a range of management- and research-focused questions.

关键词： generalized additive models Water quality Chesapeake Bay Trend analysis

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