scientific-marine-ecology - SKILL.md Agent Skill

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", ""),
            "class": taxon.get("class", ""),
            "order": taxon.get("order", ""),
            "family": taxon.get("family", ""),
            "genus": taxon.get("genus", ""),
            "is_marine": taxon.get("isMarine", 0),
            "is_brackish": taxon.get("isBrackish", 0),
            "is_freshwater": taxon.get("isFreshwater", 0),
        })

    df = pd.DataFrame(results)
    print(f"WoRMS: '{name}' → {len(df)} taxa")
    return df


def worms_classification(aphia_id):
    """
    WoRMS — 完全分類階層取得。

    Parameters:
        aphia_id: int — AphiaID
    """
    url = f"{WORMS_BASE}/AphiaClassificationByAphiaID/{aphia_id}"
    resp = requests.get(url, timeout=30)
    resp.raise_for_status()
    data = resp.json()

    hierarchy = []
    node = data
    while node:
        hierarchy.append({
            "aphia_id": node.get("AphiaID", ""),
            "name": node.get("scientificname", ""),
            "rank": node.get("rank", ""),
        })
        node = node.get("child")

    df = pd.DataFrame(hierarchy)
    print(f"WoRMS classification: {len(df)} levels")
    return 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", 0),
        })

    df = pd.DataFrame(results)
    print(f"GBIF species: '{name}' → {len(df)} taxa")
    return df


def gbif_occurrence_search(taxon_key=None, country=None,
                            year_range=None, limit=300):
    """
    GBIF — 出現記録検索。

    Parameters:
        taxon_key: int — GBIF taxon key
        country: str — ISO 国コード (例: "JP")
        year_range: tuple — (start, end)
        limit: int — 最大件数
    """
    url = f"{GBIF_BASE}/occurrence/search"
    params = {"limit": min(limit, 300)}

    if taxon_key:
        params["taxonKey"] = taxon_key
    if country:
        params["country"] = country
    if year_range:
        params["year"] = f"{year_range[0]},{year_range[1]}"

    resp = requests.get(url, params=params, timeout=60)
    resp.raise_for_status()
    data = resp.json()

    records = []
    for rec in data.get("results", []):
        records.append({
            "gbif_id": rec.get("gbifID", ""),
            "scientific_name": rec.get("scientificName", ""),
            "latitude": rec.get("decimalLatitude", None),
            "longitude": rec.get("decimalLongitude", None),
            "country": rec.get("country", ""),
            "year": rec.get("year", ""),
            "basis_of_record": rec.get("basisOfRecord", ""),
            "institution": rec.get("institutionCode", ""),
        })

    df = pd.DataFrame(records)
    print(f"GBIF occurrences: {len(df)} records (total: {data.get('count', 0)})")
    return 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", ""),
            "habitat": fish.get("DemersPelag", ""),
            "depth_range": f"{fish.get('DepthRangeShallow', '')}-{fish.get('DepthRangeDeep', '')}",
        })

    df = pd.DataFrame(records)
    print(f"FishBase: {len(df)} species")
    return 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)

    # 1) WoRMS 分類検証
    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)

    # 2) OBIS 海洋分布
    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)

    # 3) GBIF 補完
    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()

    # 4) 多様性指標
    if len(obis_data) > 0:
        n_species = obis_data["scientific_name"].nunique()
        lat_range = (obis_data["latitude"].min(), obis_data["latitude"].max())
        depth_range = (obis_data["depth"].min(), obis_data["depth"].max())
        print(f"Diversity: {n_species} species, "
              f"lat {lat_range}, depth {depth_range}")

    print(f"Marine ecology pipeline: {output_dir}")
    return {
        "taxonomy": taxonomy,
        "obis_occurrences": obis_data,
        "gbif_occurrences": 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