| name | scientific-marine-ecology |
| description | 海洋生態学統合スキル。OBIS 海洋生物分布・WoRMS 海洋分類体系・
GBIF 生物多様性レコード・FishBase 魚類データ。ToolUniverse
連携: obis, worms, gbif。
|
| tu_tools | [{"key":"obis","name":"OBIS (Ocean Biodiversity Information System)","description":"海洋生物の出現・分布データを提供する国際プラットフォーム"},{"key":"worms","name":"WoRMS (World Register of Marine Species)","description":"海洋生物種の権威ある分類学的参照データベース"},{"key":"gbif","name":"GBIF (Global Biodiversity Information Facility)","description":"生物多様性データの国際的なオープンアクセスインフラ"}] |
Scientific Marine Ecology
OBIS / WoRMS / GBIF / FishBase を活用した海洋生物多様性・
分布解析パイプラインを提供する。
When to Use
- 海洋生物の地理的分布データを取得するとき
- 海洋生物の分類学的情報 (WoRMS) を検証するとき
- 生物多様性ホットスポットを解析するとき
- 魚類の生態・形態データ (FishBase) を取得するとき
- 海洋保全区域の生物多様性評価を行うとき
- 海洋環境変動と種分布の関係を分析するとき
Quick Start
1. OBIS 海洋生物分布
import requests
import pandas as pd
import numpy as np
OBIS_BASE = "https://api.obis.org/v3"
def obis_occurrence_search(taxon_name=None, taxon_id=None,
geometry=None, year_range=None, limit=1000):
"""
OBIS — 海洋生物出現記録検索。
Parameters:
taxon_name: str — 学名 (例: "Delphinidae")
taxon_id: int — AphiaID
geometry: str — WKT ジオメトリ (例: "POLYGON((...))")
year_range: tuple — (start_year, end_year)
limit: int — 最大取得件数
"""
url = f"{OBIS_BASE}/occurrence"
params = {"size": min(limit, 5000)}
if taxon_name:
params["scientificname"] = taxon_name
if taxon_id:
params["taxonid"] = taxon_id
if geometry:
params["geometry"] = geometry
if year_range:
params["startdate"] = f"{year_range[0]}-01-01"
params["enddate"] = f"{year_range[1]}-12-31"
resp = requests.get(url, params=params, timeout=60)
resp.raise_for_status()
data = resp.json()
records = []
for rec in data.get("results", []):
records.append({
"scientific_name": rec.get("scientificName", ""),
"aphia_id": rec.get("aphiaID", ""),
"latitude": rec.get("decimalLatitude", None),
"longitude": rec.get("decimalLongitude", None),
"depth": rec.get("depth", None),
"date": rec.get("date_mid", ""),
"dataset_id": rec.get("dataset_id", ""),
"basis_of_record": rec.get("basisOfRecord", ""),
})
df = pd.DataFrame(records)
print(f"OBIS: '{taxon_name or taxon_id}' → {len(df)} occurrences")
return df
def obis_checklist(geometry=None, area_id=None):
"""
OBIS — 地域別種チェックリスト。
Parameters:
geometry: str — WKT ジオメトリ
area_id: int — OBIS エリア ID
"""
url = f"{OBIS_BASE}/checklist"
params = {"size": 5000}
if geometry:
params["geometry"] = geometry
if area_id:
params["areaid"] = area_id
resp = requests.get(url, params=params, timeout=60)
resp.raise_for_status()
data = resp.json()
species = []
for sp in data.get("results", []):
species.append({
"scientific_name": sp.get("scientificName", ""),
"aphia_id": sp.get("taxonID", ""),
"records": sp.get("records", 0),
"kingdom": sp.get("kingdom", ""),
"phylum": sp.get("phylum", ""),
"class": sp.get("class", ""),
"order": sp.get("order", ""),
"family": sp.get("family", ""),
})
df = pd.DataFrame(species)
print(f"OBIS checklist: {len(df)} species")
return df
2. WoRMS 分類学検索
WORMS_BASE = "https://www.marinespecies.org/rest"
def worms_taxon_search(name, fuzzy=True, marine_only=True):
"""
WoRMS — 海洋生物分類学的検索。
Parameters:
name: str — 種名/属名
fuzzy: bool — ファジー検索
marine_only: bool — 海洋種のみ
"""
url = f"{WORMS_BASE}/AphiaRecordsByName/{name}"
params = {
"like": str(fuzzy).lower(),
"marine_only": str(marine_only).lower(),
}
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
results = []
for taxon in data if isinstance(data, list) else [data]:
results.append({
"aphia_id": taxon.get("AphiaID", ""),
"scientific_name": taxon.get("scientificname", ""),
"authority": taxon.get("authority", ""),
"status": taxon.get("status", ""),
"rank": taxon.get("rank", ""),
"valid_name": taxon.get("valid_name", ""),
"kingdom": taxon.get("kingdom", ""),
"phylum": taxon.get("phylum", ""),
: taxon.get(, ),
: taxon.get(, ),
: taxon.get(, ),
: taxon.get(, ),
: taxon.get(, ),
: taxon.get(, ),
: taxon.get(, ),
})
df = pd.DataFrame(results)
()
df
():
url =
resp = requests.get(url, timeout=)
resp.raise_for_status()
data = resp.json()
hierarchy = []
node = data
node:
hierarchy.append({
: node.get(, ),
: node.get(, ),
: node.get(, ),
})
node = node.get()
df = pd.DataFrame(hierarchy)
()
df
3. GBIF 生物多様性レコード
GBIF_BASE = "https://api.gbif.org/v1"
def gbif_species_search(name, limit=20):
"""
GBIF — 種名検索・分類マッチング。
Parameters:
name: str — 種名
limit: int — 結果上限
"""
url = f"{GBIF_BASE}/species/search"
params = {"q": name, "limit": limit}
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
results = []
for sp in data.get("results", []):
results.append({
"taxon_key": sp.get("key", ""),
"scientific_name": sp.get("scientificName", ""),
"canonical_name": sp.get("canonicalName", ""),
"status": sp.get("taxonomicStatus", ""),
"rank": sp.get("rank", ""),
"kingdom": sp.get("kingdom", ""),
"phylum": sp.get("phylum", ""),
"class": sp.get("class", ""),
"order": sp.get("order", ""),
"family": sp.get("family", ""),
"num_occurrences": sp.get("numOccurrences", ),
})
df = pd.DataFrame(results)
()
df
():
url =
params = {: (limit, )}
taxon_key:
params[] = taxon_key
country:
params[] = country
year_range:
params[] =
resp = requests.get(url, params=params, timeout=)
resp.raise_for_status()
data = resp.json()
records = []
rec data.get(, []):
records.append({
: rec.get(, ),
: rec.get(, ),
: rec.get(, ),
: rec.get(, ),
: rec.get(, ),
: rec.get(, ),
: rec.get(, ),
: rec.get(, ),
})
df = pd.DataFrame(records)
()
df
4. FishBase 魚類データ
FISHBASE_BASE = "https://fishbase.ropensci.org"
def fishbase_species(genus=None, species=None, family=None):
"""
FishBase — 魚類種データ取得。
Parameters:
genus: str — 属名
species: str — 種小名
family: str — 科名
"""
url = f"{FISHBASE_BASE}/species"
params = {"limit": 100}
if genus:
params["Genus"] = genus
if species:
params["Species"] = species
if family:
params["Family"] = family
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
records = []
for fish in data.get("data", []):
records.append({
"spec_code": fish.get("SpecCode", ""),
"genus": fish.get("Genus", ""),
"species": fish.get("Species", ""),
"family": fish.get("Family", ""),
"body_shape": fish.get("BodyShapeI", ""),
"max_length": fish.get("Length", None),
"vulnerability": fish.get("Vulnerability", None),
"importance": fish.get("Importance", ),
: fish.get(, ),
: ,
})
df = pd.DataFrame(records)
()
df
5. 海洋生態学統合パイプライン
def marine_ecology_pipeline(taxon_name, region_wkt=None,
output_dir="results"):
"""
OBIS + WoRMS + GBIF + FishBase 統合パイプライン。
Parameters:
taxon_name: str — 分類群名
region_wkt: str — 調査海域 WKT
output_dir: str — 出力ディレクトリ
"""
from pathlib import Path
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
taxonomy = worms_taxon_search(taxon_name)
taxonomy.to_csv(output_dir / "worms_taxonomy.csv", index=False)
aphia_id = None
if len(taxonomy) > 0:
aphia_id = taxonomy.iloc[0]["aphia_id"]
classification = worms_classification(aphia_id)
classification.to_csv(output_dir / "classification.csv", index=False)
obis_data = obis_occurrence_search(
taxon_name=taxon_name,
taxon_id=aphia_id,
geometry=region_wkt,
)
obis_data.to_csv(output_dir / "obis_occurrences.csv", index=False)
gbif_sp = gbif_species_search(taxon_name)
if len(gbif_sp) > 0:
taxon_key = gbif_sp.iloc[0]["taxon_key"]
gbif_occ = gbif_occurrence_search(taxon_key=taxon_key)
gbif_occ.to_csv(output_dir / "gbif_occurrences.csv", index=False)
else:
gbif_occ = pd.DataFrame()
if len(obis_data) > 0:
n_species = obis_data[].nunique()
lat_range = (obis_data[].(), obis_data[].())
depth_range = (obis_data[].(), obis_data[].())
(
)
()
{
: taxonomy,
: obis_data,
: gbif_occ,
}
ToolUniverse 連携
| TU Key | ツール名 | 連携内容 |
|---|
obis | OBIS | 海洋生物出現・分布レコード検索 |
worms | WoRMS | 海洋種分類学的検証・階層取得 |
gbif | GBIF | 生物多様性出現レコード検索 |
パイプライン統合
environmental-ecology → marine-ecology → phylogenetics
(陸域生態学) (OBIS/WoRMS/GBIF) (系統解析)
│ │ ↓
biodiversity-db ───────────┘ species-distribution
(ENA/BOLD) │ (空間分布モデル)
↓
fisheries-management
(水産資源管理)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|
results/worms_taxonomy.csv | WoRMS 分類情報 | → phylogenetics |
results/obis_occurrences.csv | OBIS 出現記録 | → species-distribution |
results/gbif_occurrences.csv | GBIF 出現記録 | → biodiversity-db |
results/classification.csv | 分類階層 | → environmental-ecology |
