Spatial and temporal statistical analysis of bycatch data: patterns of sea turtle bycatch in the North Atlantic

2008 ◽  
Vol 65 (11) ◽  
pp. 2461-2470 ◽  
Author(s):  
Beth Gardner ◽  
Patrick J. Sullivan ◽  
Stephen J. Morreale ◽  
Sheryan P. Epperly
Keyword(s):  
North Atlantic ◽  
Spatial Clustering ◽  
Function Analysis ◽  
Temporal Patterns ◽  
Sea Turtle ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Spatial And Temporal Patterns ◽  
The North ◽  
K Function

Loggerhead ( Caretta caretta ) and leatherback ( Dermochelys coriacea ) sea turtle distributions and movements in offshore waters of the western North Atlantic are not well understood despite continued efforts to monitor, survey, and observe them. Loggerhead and leatherback sea turtles are listed as endangered by the World Conservation Union, and thus anthropogenic mortality of these species, including fishing, is of elevated interest. This study quantifies spatial and temporal patterns of sea turtle bycatch distributions to identify potential processes influencing their locations. A Ripley’s K function analysis was employed on the NOAA Fisheries Atlantic Pelagic Longline Observer Program data to determine spatial, temporal, and spatio-temporal patterns of sea turtle bycatch distributions within the pattern of the pelagic fishery distribution. Results indicate that loggerhead and leatherback sea turtle catch distributions change seasonally, with patterns of spatial clustering appearing from July through October. The results from the space–time analysis indicate that sea turtle catch distributions are related on a relatively fine scale (30–200 km and 1–5 days). The use of spatial and temporal point pattern analysis, particularly K function analysis, is a novel way to examine bycatch data and can be used to inform fishing practices such that fishing could still occur while minimizing sea turtle bycatch.

scholarly journals Spatial and Temporal Patterns in the Cod Fisheries of the North Atlantic

2015 ◽  
Vol 13 (4) ◽  
pp. 414 ◽  
Author(s):  
Anne Hayden ◽  
James Acheson ◽  
Michael Kersula ◽  
James Wilson
Keyword(s):  
North Atlantic ◽  
Temporal Patterns ◽  
Spatial And Temporal Patterns ◽  
The North ◽  
The North Atlantic ◽  
Cod Fisheries

scholarly journals Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao
Keyword(s):  
North Atlantic ◽  
Function Analysis ◽  
Biological Activities ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
The North ◽  
The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

Spatiotemporal Point Pattern Analysis Using Ripley's K Function

2017 ◽  
pp. 155-176
Author(s):  
Alexander Hohl ◽  
Minrui Zheng ◽  
Wenwu Tang ◽  
Eric Delmelle ◽  
Irene Casas
Keyword(s):  
Pattern Analysis ◽  
Point Pattern Analysis ◽  
Point Pattern ◽  
Ripley’S K Function ◽  
Ripley’S K ◽  
Ripley's K ◽  
K Function

scholarly journals Revisiting the humid Roman hypothesis: novel analyses depict oscillating patterns

2011 ◽  
Vol 7 (4) ◽  
pp. 2355-2389 ◽  
Author(s):  
B. J. Dermody ◽  
H. J. de Boer ◽  
M. F. P. Bierkens ◽  
S. L. Weber ◽  
M. J. Wassen ◽  
...  
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
Mediterranean Climate ◽  
Eastern Mediterranean ◽  
Low Frequency ◽  
Interdisciplinary Approach ◽  
Archaeological Site ◽  
Roman Period ◽  
Spatial And Temporal Patterns ◽  
The North

Abstract. Previous studies have proposed that potential vegetation in the Mediterranean maintained a wetter climate during the Roman Period until the initiation of large scale deforestation. The reduction in evapotranspirative fluxes associated with deforestation is suggested to have caused climatic aridification leading to the establishment of the present-day Mediterranean climate. There is also evidence to indicate that during the Roman Period Mediterranean climate was influenced by low frequency fluctuations in sea level pressure over the North Atlantic, termed here: the Centennial North Atlantic Oscillation (CNAO). In order to understand the importance of each of these mechanisms and disentangle their respective signals in the proxy record, we have employed an interdisciplinary approach that exploits a range of tools and data sources. An analysis of archaeological site distribution and historical texts demonstrate that climate did not increase in aridity since the Roman Period. Using an Earth system model of intermediate complexity prescribed with a reconstruction of ancient deforestation, we find that Mediterranean climate was insensitive to deforestation in the Late Holocene. A novel analysis of a composite of proxy indicators of climatic humidity depicts spatial and temporal patterns consistent with the CNAO. The link between the CNAO during the Roman Period and climatic humidity signals manifest in our composite analysis are demonstrated using a modelling approach. Finally, we present evidence indicating that fluctuations in the CNAO contributed to triggering a societal tipping point in the Eastern Mediterranean at the end of the Roman Period.

Long-term spatial and temporal patterns of sea turtle strandings in southern Brazil

2016 ◽  
Vol 163 (12) ◽  
Author(s):  
Danielle S. Monteiro ◽  
Sérgio C. Estima ◽  
Tiago B. R. Gandra ◽  
Andrine P. Silva ◽  
Leandro Bugoni ◽  
...  
Keyword(s):  
Temporal Patterns ◽  
Sea Turtle ◽  
Southern Brazil ◽  
Spatial And Temporal Patterns

Using Hierarchical Nearest Neighbor Analysis and Animation to Investigate the Spatial and Temporal Patterns of Raccoon Rabies in West Virginia

2011 ◽  
pp. 155-171 ◽  
Author(s):  
Andrew Curtis ◽  
Michael Leitner ◽  
Cathleen Hanlon
Keyword(s):  
Infectious Disease ◽  
Data Analysis ◽  
West Virginia ◽  
Nearest Neighbor ◽  
Spatial Clustering ◽  
Temporal Patterns ◽  
Spatial Cluster ◽  
Spatial And Temporal Patterns ◽  
Disease Pattern ◽  
Data Investigation

One of the most powerful uses of GIS in the field of public health is as an exploratory data analysis tool. By combining the three post-input defining components of a GIS (data manipulation, data investigation, data analysis), the spatial understanding of a disease can be furthered by identifying patterns of cases, or associations between disease and other spatial phenomena (such as elevation). This chapter sets the groundwork for one such exploratory tool that could be used to identify the spatial and temporal patterns of an infectious disease. The disease in question is raccoon rabies in West Virginia during 1999-2000. The exploratory tool, animation, has the potential to give insights into an evolving disease pattern that current spatial cluster techniques could miss. The current raccoon rabies epizootic presents a complex spatial surface as multiple disease foci may be present. Added to this could be a residual “background” or enzootic level of rabies. In order to reduce the impact of multiple foci, an appropriate “scale” of animation is needed. This scale has to be of a small enough geographic area that only one disease focus is considered, and is of practical use so that other meaningful spatial information (such as land cover or elevation) can be interpreted. The purpose of this chapter is to decide on an appropriate method of identifying this scale of animation for an infectious disease of this type. This chapter will select one commonly used technique, Nearest Neighbor Hierarchical (NNH) spatial clustering, to identify the correct scale and location on which to perform an animation. NNH spatial clustering will be applied to three combinations of Raccoon Rabies data for West Virginia, for 1999, 2000 and both years combined. NNH cluster analysis will also be performed on a four-county area identified as having the highest intensity of rabies cases in the state. These results will then be compared to a preliminary animation of rabies cases in West Virginia from which subjects were asked to identify dynamically evolving disease clusters. An animation was also run for the same area of high disease intensity. Cluster and animation results were compared for similarities. It was found that a spatial cluster technique, such as NNH spatial clustering, provides an adequate means of identifying the scale and location on which a more sophisticated animation can be based. The chapter concludes with a discussion of how, once a scale has been decided, a more sophisticated animation can be constructed and ultimately used to guide the placement of interventions such as oral vaccine barriers.

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