Global Hotspots Of Sharks And Longline Fishing by Kaggle | Other

About this Dataset

Global Hotspots Of Sharks And Longline Fishing

Global Hotspots of Sharks and Longline Fishing

Machine-Learning-Assisted Spatial Distribution of At-Risk Species

About this dataset

This dataset provides a critical global assessment of hotspots for shark interactions with industrial longline fisheries. It utilizes machine-learning techniques to identify at-risk shark species and their spatial distribution patterns, highlighting crucial risk areas for threatened shark populations. Through the various parameters of the data, such as catch size, catch units, fish group and presence/absence of species among other details, this dataset can be used to better understand which fishing activities pose a potential threat to sharks while protecting those that are not detrimental. With this information we can help conserve our oceans' fragile ecosystems by maneuvering strategies towards sustainability in order to ensure healthy oceans for generations to come

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How to use the dataset

This dataset provides valuable insights into the spatial distribution of shark interactions with industrial longline fisheries. It can be used by researchers and conservationists to understand potential risk areas for endangered shark populations in different parts of the world, as well as for developing targeted strategies and measures to protect them.

In order to use this dataset effectively, it is important to understand its structure and content. This dataset contains columns that provide information on each observation including: .pred_class (predicted class of the observation), pres_abs (presence or absence of species), catch (catch data for the species), rfmo (Regional Fisheries Management Organization), year (year of the observation), latitude/longitude (location information) and a variety of other variables related to environmental values, sea surface temperature/height, chlorophyll-a concentration etc. The catch data has been transformed using various methods so that they are easier to use in develop predictive models.

In addition to these variables, this dataset also includes information on prices associated with each observed interaction as well as results from machine-learning-assisted models such as Random Forest Classification/Regression Trees, Minimum Node Size Classifier/Regressor and Mean Absolute Error scores resulting from the model. The results generated by these models can help identify potential hotspots for future interactions between sharks and industrial longline fishing operations which may lead us towards designing better policies for preserving threatened shark populations around the world

Research Ideas

This dataset can be used to predict future patterns of shark interactions with industrial longline fisheries, as well as identify hotspots of activity.

This dataset can provide valuable insight into how human activities and climate change may be impacting sharks and their environments.

This dataset can help provide early warnings for conservation efforts that should focus on particular areas in order to protect threatened species from unsustainable exploitation or other anthropogenic threats (e.g., habitat degradation)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: IOTC_ll_untuned_final_predict.csv

Column name	Description
.pred_class	Predicted class of the species (String)
pres_abs	Presence or absence of the species in the region (Boolean)
catch	Total catch of the species (Integer)
rfmo	Regional Fisheries Management Organization (String)
year	Year of the data (Integer)
latitude	Latitude of the location (Float)
longitude	Longitude of the location (Float)
species_sciname	Scientific name of the species (String)
catch_units	Units of the catch (String)
gear_group	Type of fishing gear used (String)
spatial_notes	Notes about the spatial distribution of the species (String)
original_effort	Original effort of the fishing gear (Integer)
species_commonname	Common name of the species (String)
species_group	Group of the species (String)
species_resolution	Resolution of the species (String)
median_price_group	Median price of the group (Float)
median_price_species	Median price of the species (Float)
sdm	Statistical distribution model (String)
zone	Zone of the location (String)
location_cluster	Cluster of the location (String)
mean_sst	Mean sea surface temperature (Float)
median_sst	Median sea surface temperature (Float)
min_sst	Minimum sea surface temperature (Float)
max_sst	Maximum sea surface temperature (Float)
sd_sst	Standard deviation of sea surface temperature (Float)
se_sst	Standard error of sea surface temperature (Float)
cv_sst	Coefficient of variation of sea surface temperature (Float)
mean_chla	Mean chlorophyll-a concentration (Float)
median_chla	Median chlorophyll-a concentration (Float)
min_chla	Minimum chlorophyll-a concentration (Float)
max_chla	Maximum chlor
min_ssh	Minimum sea surface height (Float)
max_ssh	Maximum sea surface height (Float)
sd_ssh	Standard deviation of sea surface height (Float)
se_ssh	Standard error of sea surface height (Float)
cv_ssh	Coefficient of variation of sea surface height (Float)
bycatch_total_effort_portugal_longline	Total bycatch effort of Portugal longline (Integer)
bycatch_total_effort_spain_longline	Total bycatch effort of Spain longline (Integer)
bycatch_total_effort_france_longline	Total bycatch effort of France longline (Integer)
bycatch_total_effort_india_longline	Total bycatch effort of India longline (Integer)
bycatch_total_effort_seychelles_longline	Total bycatch effort of Seychelles longline (Integer)
bycatch_total_effort_taiwan_longline	Total bycatch effort of Taiwan longline (Integer)
bycatch_total_effort_madagascar_longline	Total bycatch effort of Madagascar longline (Integer)
bycatch_total_effort_mauritius_longline	Total bycatch effort of Mauritius longline (Integer)
bycatch_total_effort_united_kingdom_longline	Total bycatch effort of United Kingdom longline (Integer)
bycatch_total_effort_australia_longline	Total bycatch effort of Australia longline (Integer)
bycatch_total_effort_mozambique_longline	Total bycatch effort of Mozambique longline (Integer)
bycatch_total_effort_malaysia_longline	Total bycatch effort of Malaysia longline (Integer)
bycatch_total_effort_indonesia_longline	Total bycatch effort of Indonesia longline (Integer)
bycatch_total_effort_kenya_longline	Total bycatch effort of Kenya longline (Integer)
.final_pred	Predicted class of the species (String)
bycatch_total_effort	Total bycatch effort (Integer)
bycatch_total_effort_china_longline	Total bycatch effort of China longline (Integer)
bycatch_total_effort_korea_longline	Total bycatch effort of Korea longline (Integer)
bycatch_total_effort_japan_longline	Total bycatch effort of Japan longline (Integer)
sd_chla	Standard deviation of chlorophyll-a concentration (Float)
se_chla	Standard error of chlorophyll-a concentration (Float)
cv_chla	Coefficient of variation of chlorophyll-a concentration (Float)
mean_ssh	Mean sea surface height (Float)
median_ssh	Median sea surface height (Float)

File: WCPFC_ll_models_others_results.csv

Column name	Description
environmental_value	The environmental value associated with the area. (Float)
include_ssh	Whether or not sea surface height was included in the model. (Boolean)
price	The price of the data. (Float)
catch_transformation	The transformation applied to the catch data. (String)
mtry_class	The maximum number of variables randomly sampled at each split in the classification tree. (Integer)
min_n_class	The minimum observations in a node for a split to be considered valid. (Integer)
mtry_reg	The maximum number of variables randomly sampled at each split in the regression tree. (Integer)
min_n_reg	The minimum observations in a node for a split to be considered valid. (Integer)
rmse	The root mean square error. (Float)
rsq	The coefficient of determination. (Float)
mae	The mean absolute error. (Float)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

N 1x1 Count All Rfmos Ll Effort Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.n_1x1_count_all_rfmos_ll_effort_results

4.86 KB
12 rows
6 columns


CREATE TABLE n_1x1_count_all_rfmos_ll_effort_results (
  "model" VARCHAR,
  "effort_source" VARCHAR,
  "rfmo" VARCHAR,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

N 1x1 Mt To Count All Rfmos Ll Effort Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.n_1x1_mt_to_count_all_rfmos_ll_effort_results

4.78 KB
9 rows
6 columns


CREATE TABLE n_1x1_mt_to_count_all_rfmos_ll_effort_results (
  "model" VARCHAR,
  "effort_source" VARCHAR,
  "rfmo" VARCHAR,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

N 5x5 Count All Rfmos Ll Effort Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.n_5x5_count_all_rfmos_ll_effort_results

4.86 KB
12 rows
6 columns


CREATE TABLE n_5x5_count_all_rfmos_ll_effort_results (
  "model" VARCHAR,
  "effort_source" VARCHAR,
  "rfmo" VARCHAR,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

N 5x5 Mt To Count All Rfmos Ll Effort Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.n_5x5_mt_to_count_all_rfmos_ll_effort_results

4.78 KB
9 rows
6 columns


CREATE TABLE n_5x5_mt_to_count_all_rfmos_ll_effort_results (
  "model" VARCHAR,
  "effort_source" VARCHAR,
  "rfmo" VARCHAR,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

Iattc Ll Models Others Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.iattc_ll_models_others_results

8.34 KB
24 rows
11 columns


CREATE TABLE iattc_ll_models_others_results (
  "environmental_value" VARCHAR,
  "include_ssh" BOOLEAN,
  "price" VARCHAR,
  "catch_transformation" VARCHAR,
  "mtry_class" BIGINT,
  "min_n_class" BIGINT,
  "mtry_reg" BIGINT,
  "min_n_reg" BIGINT,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

Iattc Ll Untuned Final Predict

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.iattc_ll_untuned_final_predict

14.12 MB
339300 rows
54 columns


CREATE TABLE iattc_ll_untuned_final_predict (
  "n__pred_class" BIGINT,
  "pres_abs" VARCHAR,
  "catch" DOUBLE,
  "rfmo" VARCHAR,
  "year" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "species_sciname" VARCHAR,
  "gear_group" VARCHAR,
  "spatial_notes" VARCHAR,
  "species_commonname" VARCHAR,
  "species_group" VARCHAR,
  "species_resolution" VARCHAR,
  "median_price_group" DOUBLE,
  "median_price_species" DOUBLE,
  "catch_units" VARCHAR,
  "original_effort" DOUBLE,
  "sdm" DOUBLE,
  "zone" VARCHAR,
  "location_cluster" BIGINT,
  "target_effort" DOUBLE,
  "target_effort_belize_longline" DOUBLE,
  "target_effort_china_longline" DOUBLE,
  "target_effort_french_polynesia_longline" DOUBLE,
  "target_effort_japan_longline" DOUBLE,
  "target_effort_korea_longline" DOUBLE,
  "target_effort_spain_longline" DOUBLE,
  "target_effort_taipei_longline" DOUBLE,
  "target_effort_united_states_of_america_longline" DOUBLE,
  "target_effort_vanuatu_longline" DOUBLE,
  "target_effort_panama_longline" DOUBLE,
  "mean_sst" DOUBLE,
  "median_sst" DOUBLE,
  "min_sst" DOUBLE,
  "max_sst" DOUBLE,
  "sd_sst" DOUBLE,
  "se_sst" DOUBLE,
  "cv_sst" DOUBLE,
  "mean_chla" DOUBLE,
  "median_chla" DOUBLE,
  "min_chla" DOUBLE,
  "max_chla" DOUBLE,
  "sd_chla" VARCHAR,
  "se_chla" VARCHAR,
  "cv_chla" VARCHAR,
  "mean_ssh" DOUBLE,
  "median_ssh" DOUBLE,
  "min_ssh" DOUBLE,
  "max_ssh" DOUBLE,
  "sd_ssh" DOUBLE,
  "se_ssh" DOUBLE,
  "cv_ssh" DOUBLE,
  "n__pred" DOUBLE,
  "n__final_pred" DOUBLE
);

Iccat Ll Models Others Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.iccat_ll_models_others_results

8.33 KB
24 rows
11 columns


CREATE TABLE iccat_ll_models_others_results (
  "environmental_value" VARCHAR,
  "include_ssh" BOOLEAN,
  "price" VARCHAR,
  "catch_transformation" VARCHAR,
  "mtry_class" BIGINT,
  "min_n_class" BIGINT,
  "mtry_reg" BIGINT,
  "min_n_reg" BIGINT,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

Iccat Ll Untuned Final Predict

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.iccat_ll_untuned_final_predict

1.14 MB
8592 rows
69 columns


CREATE TABLE iccat_ll_untuned_final_predict (
  "n__pred_class" BIGINT,
  "pres_abs" VARCHAR,
  "catch" DOUBLE,
  "rfmo" VARCHAR,
  "year" BIGINT,
  "latitude" BIGINT,
  "longitude" BIGINT,
  "species_sciname" VARCHAR,
  "catch_units" VARCHAR,
  "gear_group" VARCHAR,
  "spatial_notes" VARCHAR,
  "original_effort" BIGINT,
  "species_commonname" VARCHAR,
  "species_group" VARCHAR,
  "species_resolution" VARCHAR,
  "median_price_group" DOUBLE,
  "median_price_species" DOUBLE,
  "sdm" DOUBLE,
  "zone" VARCHAR,
  "location_cluster" BIGINT,
  "target_effort" DOUBLE,
  "target_effort_belize_longline" DOUBLE,
  "target_effort_china_longline" DOUBLE,
  "target_effort_japan_longline" DOUBLE,
  "target_effort_korea_longline" DOUBLE,
  "target_effort_spain_longline" DOUBLE,
  "target_effort_taipei_longline" DOUBLE,
  "target_effort_united_states_of_america_longline" DOUBLE,
  "target_effort_vanuatu_longline" DOUBLE,
  "mean_sst" DOUBLE,
  "median_sst" DOUBLE,
  "min_sst" DOUBLE,
  "max_sst" DOUBLE,
  "sd_sst" DOUBLE,
  "se_sst" DOUBLE,
  "cv_sst" DOUBLE,
  "mean_chla" DOUBLE,
  "median_chla" DOUBLE,
  "min_chla" DOUBLE,
  "max_chla" DOUBLE,
  "sd_chla" DOUBLE,
  "se_chla" DOUBLE,
  "cv_chla" DOUBLE,
  "mean_ssh" DOUBLE,
  "median_ssh" DOUBLE,
  "min_ssh" DOUBLE,
  "max_ssh" DOUBLE,
  "sd_ssh" DOUBLE,
  "se_ssh" DOUBLE,
  "cv_ssh" DOUBLE,
  "target_effort_barbados_longline" DOUBLE,
  "target_effort_bermuda_longline" DOUBLE,
  "target_effort_brazil_longline" DOUBLE,
  "target_effort_canada_longline" DOUBLE,
  "target_effort_malta_longline" DOUBLE,
  "target_effort_maroc_longline" DOUBLE,
  "target_effort_mexico_longline" DOUBLE,
  "target_effort_namibia_longline" DOUBLE,
  "target_effort_philippines_longline" DOUBLE,
  "target_effort_st_vincent_and_grenadines_longline" DOUBLE,
  "target_effort_trinidad_and_tobago_longline" DOUBLE,
  "target_effort_uruguay_longline" DOUBLE,
  "target_effort_venezuela_longline" DOUBLE,
  "target_effort_turks_and_caicos_longline" DOUBLE,
  "target_effort_great_britain_longline" DOUBLE,
  "target_effort_cote_d_ivoire_longline" DOUBLE,
  "target_effort_cyprus_longline" DOUBLE,
  "n__pred" DOUBLE,
  "n__final_pred" DOUBLE
);

Iotc Ll Models Others Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.iotc_ll_models_others_results

8.33 KB
24 rows
11 columns


CREATE TABLE iotc_ll_models_others_results (
  "environmental_value" VARCHAR,
  "include_ssh" BOOLEAN,
  "price" VARCHAR,
  "catch_transformation" VARCHAR,
  "mtry_class" BIGINT,
  "min_n_class" BIGINT,
  "mtry_reg" BIGINT,
  "min_n_reg" BIGINT,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

Iotc Ll Untuned Final Predict

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.iotc_ll_untuned_final_predict

754.39 KB
13500 rows
61 columns


CREATE TABLE iotc_ll_untuned_final_predict (
  "n__pred_class" BIGINT,
  "pres_abs" VARCHAR,
  "catch" DOUBLE,
  "rfmo" VARCHAR,
  "year" BIGINT,
  "latitude" DOUBLE,
  "longitude" BIGINT,
  "species_sciname" VARCHAR,
  "catch_units" VARCHAR,
  "gear_group" VARCHAR,
  "spatial_notes" VARCHAR,
  "original_effort" BIGINT,
  "species_commonname" VARCHAR,
  "species_group" VARCHAR,
  "species_resolution" VARCHAR,
  "median_price_group" DOUBLE,
  "median_price_species" DOUBLE,
  "sdm" DOUBLE,
  "zone" VARCHAR,
  "location_cluster" BIGINT,
  "bycatch_total_effort" DOUBLE,
  "bycatch_total_effort_china_longline" DOUBLE,
  "bycatch_total_effort_korea_longline" DOUBLE,
  "bycatch_total_effort_japan_longline" DOUBLE,
  "mean_sst" DOUBLE,
  "median_sst" DOUBLE,
  "min_sst" DOUBLE,
  "max_sst" DOUBLE,
  "sd_sst" DOUBLE,
  "se_sst" DOUBLE,
  "cv_sst" DOUBLE,
  "mean_chla" DOUBLE,
  "median_chla" DOUBLE,
  "min_chla" DOUBLE,
  "max_chla" DOUBLE,
  "sd_chla" DOUBLE,
  "se_chla" DOUBLE,
  "cv_chla" DOUBLE,
  "mean_ssh" DOUBLE,
  "median_ssh" DOUBLE,
  "min_ssh" DOUBLE,
  "max_ssh" DOUBLE,
  "sd_ssh" DOUBLE,
  "se_ssh" DOUBLE,
  "cv_ssh" DOUBLE,
  "bycatch_total_effort_portugal_longline" DOUBLE,
  "bycatch_total_effort_spain_longline" DOUBLE,
  "bycatch_total_effort_france_longline" DOUBLE,
  "bycatch_total_effort_india_longline" DOUBLE,
  "bycatch_total_effort_seychelles_longline" DOUBLE,
  "bycatch_total_effort_taiwan_longline" DOUBLE,
  "bycatch_total_effort_madagascar_longline" DOUBLE,
  "bycatch_total_effort_mauritius_longline" DOUBLE,
  "bycatch_total_effort_united_kingdom_longline" DOUBLE,
  "bycatch_total_effort_australia_longline" DOUBLE,
  "bycatch_total_effort_mozambique_longline" DOUBLE,
  "bycatch_total_effort_malaysia_longline" DOUBLE,
  "bycatch_total_effort_indonesia_longline" DOUBLE,
  "bycatch_total_effort_kenya_longline" DOUBLE,
  "n__pred" DOUBLE,
  "n__final_pred" DOUBLE
);

Wcpfc Ll Models Others Results

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.wcpfc_ll_models_others_results

8.36 KB
24 rows
11 columns


CREATE TABLE wcpfc_ll_models_others_results (
  "environmental_value" VARCHAR,
  "include_ssh" BOOLEAN,
  "price" VARCHAR,
  "catch_transformation" VARCHAR,
  "mtry_class" BIGINT,
  "min_n_class" BIGINT,
  "mtry_reg" BIGINT,
  "min_n_reg" BIGINT,
  "rmse" DOUBLE,
  "rsq" DOUBLE,
  "mae" DOUBLE
);

Wcpfc Ll Untuned Final Predict

@kaggle.thedevastator_global_hotspots_of_sharks_and_longline_fishing.wcpfc_ll_untuned_final_predict

762.74 KB
25857 rows
45 columns


CREATE TABLE wcpfc_ll_untuned_final_predict (
  "n__pred_class" BIGINT,
  "pres_abs" VARCHAR,
  "catch" BIGINT,
  "rfmo" VARCHAR,
  "year" BIGINT,
  "latitude" DOUBLE,
  "longitude" DOUBLE,
  "species_sciname" VARCHAR,
  "catch_units" VARCHAR,
  "gear_group" VARCHAR,
  "spatial_notes" VARCHAR,
  "original_effort" BIGINT,
  "species_commonname" VARCHAR,
  "species_group" VARCHAR,
  "species_resolution" VARCHAR,
  "median_price_group" DOUBLE,
  "median_price_species" DOUBLE,
  "sdm" DOUBLE,
  "zone" VARCHAR,
  "location_cluster" BIGINT,
  "bycatch_total_effort" DOUBLE,
  "mean_sst" DOUBLE,
  "median_sst" DOUBLE,
  "min_sst" DOUBLE,
  "max_sst" DOUBLE,
  "sd_sst" DOUBLE,
  "se_sst" DOUBLE,
  "cv_sst" DOUBLE,
  "mean_chla" DOUBLE,
  "median_chla" DOUBLE,
  "min_chla" DOUBLE,
  "max_chla" DOUBLE,
  "sd_chla" DOUBLE,
  "se_chla" DOUBLE,
  "cv_chla" DOUBLE,
  "mean_ssh" DOUBLE,
  "median_ssh" DOUBLE,
  "min_ssh" DOUBLE,
  "max_ssh" DOUBLE,
  "sd_ssh" DOUBLE,
  "se_ssh" DOUBLE,
  "cv_ssh" DOUBLE,
  "bycatch_total_effort_na_longline" DOUBLE,
  "n__pred" DOUBLE,
  "n__final_pred" DOUBLE
);