Scientific Paleobiology

2. PBDB 分類群情報検索

def pbdb_search_taxa(name=None, rank=None, interval=None, limit=500):
    """
    PBDB — 分類群検索。

    Parameters:
        name: str — 分類群名 (例: "Dinosauria")
        rank: str — ランク (例: "genus", "family", "order")
        interval: str — 地質年代区間
        limit: int — 最大結果数
    """
    url = f"{PBDB_BASE}/taxa/list.json"
    params = {
        "show": "attr,app,size",
        "limit": limit,
    }
    if name:
        params["base_name"] = name
    if rank:
        params["rank"] = rank
    if interval:
        params["interval"] = interval

    resp = requests.get(url, params=params, timeout=30)
    resp.raise_for_status()
    records = resp.json().get("records", [])

    results = []
    for r in records:
        results.append({
            "taxon_no": r.get("oid", ""),
            "taxon_name": r.get("nam", ""),
            "rank": r.get("rnk", ""),
            "parent_name": r.get("prl", ""),
            "n_occs": r.get("noc", 0),
            "first_appearance": r.get("fea", ""),
            "last_appearance": r.get("lla", ""),
            "extant": r.get("ext", ""),
        })

    df = pd.DataFrame(results)
    print(f"PBDB taxa: {len(df)} records (name={name})")
    return df

3. 地質年代多様性曲線

def pbdb_diversity_curve(taxon, time_resolution="stage",
                           rank="genus"):
    """
    PBDB — 地質年代多様性曲線生成。

    Parameters:
        taxon: str — 分類群名
        time_resolution: str — "stage" or "epoch" or "period"
        rank: str — カウントするランク ("genus", "family")
    """
    url = f"{PBDB_BASE}/occs/diversity.json"
    params = {
        "base_name": taxon,
        "count": rank,
        "time_reso": time_resolution,
    }
    resp = requests.get(url, params=params, timeout=60)
    resp.raise_for_status()
    records = resp.json().get("records", [])

    results = []
    for r in records:
        results.append({
            "interval_name": r.get("idn", ""),
            "max_ma": r.get("eag", None),
            "min_ma": r.get("lag", None),
            "mid_ma": (float(r.get("eag", 0)) +
                       float(r.get("lag", 0))) / 2,
            "sampled_in_bin": r.get("dsb", 0),
            "n_originations": r.get("dor", 0),
            "n_extinctions": r.get("dex", 0),
            "range_through": r.get("drt", 0),
        })

    df = pd.DataFrame(results)
    print(f"PBDB diversity: {len(df)} intervals, "
          f"max diversity={df['sampled_in_bin'].max()} {rank}")
    return df

4. 古生物学統合パイプライン

def paleobiology_pipeline(taxon, interval=None,
                            output_dir="results"):
    """
    古生物学統合パイプライン。

    Parameters:
        taxon: str — 分類群名 (例: "Dinosauria")
        interval: str — 地質年代区間 (オプション)
        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) 産出記録
    occ = pbdb_search_occurrences(taxon=taxon, interval=interval)
    occ.to_csv(output_dir / "occurrences.csv", index=False)

    # 2) 分類群情報
    taxa = pbdb_search_taxa(name=taxon)
    taxa.to_csv(output_dir / "taxa.csv", index=False)

    # 3) 多様性曲線
    diversity = pbdb_diversity_curve(taxon)
    diversity.to_csv(output_dir / "diversity.csv", index=False)

    # 4) 地理的サマリ
    if "lat" in occ.columns and "lng" in occ.columns:
        geo_summary = occ.groupby("early_interval").agg(
            n_records=("occurrence_no", "count"),
            mean_lat=("lat", "mean"),
            mean_lng=("lng", "mean"),
        ).reset_index()
        geo_summary.to_csv(output_dir / "geo_summary.csv", index=False)

    print(f"Paleobiology pipeline: {output_dir}")
    return {
        "occurrences": occ,
        "taxa": taxa,
        "diversity": diversity,
    }

ToolUniverse 連携

パイプライン統合

phylogenetics → paleobiology → environmental-ecology
  (系統解析)   (化石記録)      (GBIF/生態)
      │              │                ↓
  taxonomy ─────────┘         environmental-geodata
  (分類体系)    │              (環境モデリング)
                ↓
         macroevolution
         (大進化パターン)

パイプライン出力