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Ecology & Biodiversity Research

生态学与生物多样性研究

Reasoning Strategy

推理策略

1. Species & Taxonomy Questions

1. 物种与分类学问题

When a question involves identifying or comparing species:
  1. LOOK UP DON'T GUESS — Use 
    GBIF_search_species
    to get taxonomy, 
    WoRMS_search_species
    for marine organisms
  2. If the question asks about invasive species impacts, consider: ecological niche overlap, reproductive rate, predator release, and ecosystem engineering effects
  3. Use 
    PubMed_search_articles
    or 
    EuropePMC_search_articles
    to find studies on specific ecological impacts
当问题涉及物种识别或对比时：
  1. 查资料而非猜测——使用
    GBIF_search_species
    获取分类信息，针对海洋生物使用
    WoRMS_search_species
  2. 如果问题涉及入侵物种影响，需考虑：生态位重叠、繁殖率、天敌释放效应及生态系统工程效应
  3. 使用
    PubMed_search_articles
    或
    EuropePMC_search_articles
    查找特定生态影响相关研究

2. Invasive Species Impact Assessment

2. 入侵物种影响评估

Reasoning framework — when comparing invasive species impacts:
  1. Identify the ecosystem: What habitat/biome is affected?
  2. Assess impact mechanisms: Competition? Predation? Disease vector? Habitat modification? Hybridization?
  3. Scale of impact: Local (single site) vs regional vs continental?
  4. Trophic position: Invasives at higher trophic levels (predators) often cause more damage than lower (herbivores)
  5. Ecosystem engineering: Species that modify habitats (beavers, earthworms, honeybees displacing native pollinators) cause outsized impacts
  6. Look up specifics — don't rely on general knowledge. Search for "[species name] invasive impact [region]" in literature
推理框架——对比入侵物种影响时：
  1. 确定生态系统：受影响的栖息地/生物群系是什么？
  2. 评估影响机制：竞争？捕食？疾病传播媒介？栖息地改造？杂交？
  3. 影响规模：局部（单一地点）vs 区域 vs 大陆？
  4. 营养级位置：处于较高营养级的入侵物种（捕食者）通常比低营养级物种（草食动物）造成的破坏更大
  5. 生态系统工程：能够改造栖息地的物种（海狸、蚯蚓、取代本土传粉者的蜜蜂）会造成巨大影响
  6. 查找具体信息——不要依赖常识。在文献中搜索“[物种名称] 入侵影响 [区域]”

3. Pollinator Ecology

3. 传粉者生态学

Reasoning framework for pollination questions:
  1. Foraging behavior: Distinguish investigation (approach/assessment) from actual feeding (proboscis insertion)
  2. Interaction types: Mutualistic (pollination reward), parasitic (nectar robbing), commensal
  3. Observation methods: Camera traps have resolution/FOV limitations — consider what's identifiable at given resolution
  4. Statistical considerations: Observer agreement (inter-rater reliability), sampling effort, temporal patterns
  5. Ethogram interpretation: Each behavior category has specific start/end criteria — follow them precisely
推理框架——针对传粉相关问题：
  1. 觅食行为：区分探查（接近/评估）与实际取食（口器插入）
  2. 交互类型：互利共生（传粉回报）、寄生（盗蜜）、偏利共生
  3. 观测方法：相机陷阱存在分辨率/视野限制——需考虑在给定分辨率下可识别的内容
  4. 统计考量：观察者一致性（评分者间信度）、采样工作量、时间模式
  5. 行为谱解读：每个行为类别都有明确的起止标准——需严格遵循

4. Population Dynamics

4. 种群动态

Reasoning framework for population ecology questions:
  1. Growth models: Exponential (unlimited), logistic (K-limited), Allee effects (low-density problems)
  2. Extinction analysis: Distinguish deterministic extinction (r < 0) from stochastic extinction (small population fluctuations)
  3. Survival analysis: Time-to-event analysis needs appropriate statistical tests (log-rank, Cox regression, Kaplan-Meier)
  4. Microbial ecology: For microbial stressor responses, use survival curve analysis with time-kill kinetics. To compare extinction points between populations, you need time-to-extinction data analyzed with survival statistics (not just endpoint comparisons)
推理框架——针对种群生态学问题：
  1. 增长模型：指数增长（资源无限）、逻辑斯蒂增长（受K值限制）、阿利效应（低密度问题）
  2. 灭绝分析：区分确定性灭绝（r < 0）与随机性灭绝（小种群波动）
  3. 生存分析：时间-事件分析需要合适的统计检验（log-rank检验、Cox回归、Kaplan-Meier法）
  4. 微生物生态学：针对微生物应激反应，使用生存曲线分析结合时间-杀灭动力学。若要对比种群间的灭绝点，需使用生存统计分析时间-灭绝数据（而非仅对比端点）

5. Community Ecology & Food Webs

5. 群落生态学与食物网

  1. Trophic cascades: Removing top predators → mesopredator release → prey decline
  2. Keystone species: Disproportionate impact relative to abundance
  3. Island biogeography: Species-area relationship, distance-colonization tradeoff
  4. Competitive exclusion: Two species cannot stably coexist on single limiting resource (Gause's principle)
  1. 营养级联：移除顶级捕食者→中型捕食者释放→猎物数量下降
  2. 关键物种：影响程度与丰度不成比例的物种
  3. 岛屿生物地理学：物种-面积关系、距离-殖民权衡
  4. 竞争排斥：两个物种无法在单一限制资源下稳定共存（高斯原理）

6. Evolutionary Ecology

6. 进化生态学

  1. Aposematism: Warning coloration signals toxicity/unpalatability
  2. Mimicry: Batesian (harmless mimics dangerous) vs Mullerian (dangerous mimics dangerous)
  3. Life history tradeoffs: r-selected (many offspring, low investment) vs K-selected (few offspring, high investment)
  4. Birth-death models: For phylogenetic questions, identifiability issues arise with time-varying rates. Strategies to resolve: constrain rate variation, add fossil data, use molecular data calibration, or restrict to specific functional forms
  1. 警戒色：通过警示色传递毒性/不可食信号
  2. 拟态：贝氏拟态（无害物种模仿危险物种）vs 缪勒拟态（危险物种互相模仿）
  3. 生活史权衡：r-选择（多后代、低投入）vs K-选择（少后代、高投入）
  4. 生死模型：针对系统发育问题，时变速率会导致识别性问题。解决策略：限制速率变异、添加化石数据、使用分子数据校准或限定为特定函数形式

Available Tools

可用工具

ToolUse For
IUCN_get_conservation_status
Red List conservation status (CR/EN/VU/NT/LC) by scientific name — the authoritative extinction-risk source (needs a free IUCN_API_KEY)
GBIF_search_species
Species taxonomy, occurrence data, distribution
GBIF_search_occurrences
Where has a species been observed?
GBIF_get_taxon_parents
Walk UP the GBIF Backbone tree — ranked ancestor lineage (kingdom→genus) for a taxonKey
GBIF_get_taxon_children
Walk DOWN the tree — direct child taxa (e.g. species in a genus) for a taxonKey
GBIF_get_taxon_synonyms
Alternative / historical scientific names for an accepted taxonKey
GBIF_get_vernacular_names
Common names (with language code) for a taxonKey; optional 
language
filter
GBIF_parse_name
Parse messy/authored name strings into canonical name + genus/epithet/author/year
iDigBio_search_records
Search 130M+ digitized museum/herbarium specimen records (Darwin Core) by 
genus
/
scientificname
/locality — use to complement GBIF with physical-specimen provenance
iDigBio_get_record
Full Darwin Core detail for one specimen by 
uuid
(from 
iDigBio_search_records
)
WoRMS_search_species
Marine species taxonomy
ensembl_get_taxonomy
Taxonomic classification
NCBIDatasets_get_taxonomy
NCBI taxonomy lookup
PubMed_search_articles
Literature on ecology topics
EuropePMC_search_articles
European literature including ecology
工具适用场景
IUCN_get_conservation_status
根据学名获取红色名录保护状态（CR/EN/VU/NT/LC）——权威的灭绝风险来源（需要免费的IUCN_API_KEY）
GBIF_search_species
物种分类、出现数据、分布情况
GBIF_search_occurrences
物种观测地点查询
GBIF_get_taxon_parents
遍历GBIF主干分类树——获取分类单元的祖先谱系（界→属）
GBIF_get_taxon_children
遍历分类树向下——获取分类单元的直接子分类（如属内物种）
GBIF_get_taxon_synonyms
已接受分类单元的别名/历史学名
GBIF_get_vernacular_names
分类单元的通用名称（带语言代码）；可选
language
筛选
GBIF_parse_name
将不规范/带作者的名称字符串解析为标准名称 + 属/种加词/作者/年份
iDigBio_search_records
通过
genus
/
scientificname
/locality搜索1.3亿+数字化博物馆/植物标本馆记录（Darwin Core）——用于补充GBIF的实体标本来源信息
iDigBio_get_record
通过
uuid
（来自
iDigBio_search_records
）获取单个标本的完整Darwin Core详情
WoRMS_search_species
海洋物种分类
ensembl_get_taxonomy
分类学分类
NCBIDatasets_get_taxonomy
NCBI分类查询
PubMed_search_articles
生态学主题文献检索
EuropePMC_search_articles
包含生态学在内的欧洲文献检索

Navigating the GBIF taxonomic tree

遍历GBIF分类树

Resolve a name to a GBIF 
usageKey
once, then navigate the Backbone tree:
python
key = tu.run_tool("GBIF_match_name", {"name": "Panthera leo"})["data"]["usageKey"]  # 5219404
tu.run_tool("GBIF_get_taxon_parents", {"taxon_key": key})        # Animalia→...→Felidae→Panthera
tu.run_tool("GBIF_get_taxon_synonyms", {"taxon_key": key})       # Felis leo Linnaeus, 1758, ...
tu.run_tool("GBIF_get_vernacular_names", {"taxon_key": key, "language": "eng"})  # Lion, African Lion
先将名称解析为GBIF 
usageKey
，再遍历主干分类树：
python
key = tu.run_tool("GBIF_match_name", {"name": "Panthera leo"})["data"]["usageKey"]  # 5219404
tu.run_tool("GBIF_get_taxon_parents", {"taxon_key": key})        # Animalia→...→Felidae→Panthera
tu.run_tool("GBIF_get_taxon_synonyms", {"taxon_key": key})       # Felis leo Linnaeus, 1758, ...
tu.run_tool("GBIF_get_vernacular_names", {"taxon_key": key, "language": "eng"})  # Lion, African Lion

Walk down from a genus key (Panthera = 2435194) to its species:

从属的key（Panthera = 2435194）向下遍历到其物种：

tu.run_tool("GBIF_get_taxon_children", {"taxon_key": 2435194, "limit": 8})
tu.run_tool("GBIF_get_taxon_children", {"taxon_key": 2435194, "limit": 8})

Normalize an authored name string without a key:

无需key即可标准化带作者的名称字符串：

tu.run_tool("GBIF_parse_name", {"name": "Quercus robur L."}) # canonicalName 'Quercus robur'

All five tools hit the public GBIF API with no key. Get the starting `taxon_key`
from `GBIF_match_name` or `GBIF_search_species`.
tu.run_tool("GBIF_parse_name", {"name": "Quercus robur L."}) # canonicalName 'Quercus robur'

以上五个工具均可调用公开GBIF API，无需密钥。可通过`GBIF_match_name`或`GBIF_search_species`获取初始`taxon_key`。

LOOK UP DON'T GUESS

查资料而非猜测

Ecology questions often have counter-intuitive answers. For example:
  • Honeybees (Apis mellifera) are invasive in the Americas and displace native pollinators — this surprises people who think of bees as "good"
  • The most damaging invasive species are often not the most obvious ones
  • Microbial extinction points require survival analysis, not simple t-tests
Always search the literature before answering ecology questions. Use 
PubMed_search_articles
with specific terms like "[species] invasive impact [region]" or "[organism] [ecological process]".
生态学问题的答案往往违反直觉。例如：
  • 蜜蜂（Apis mellifera）在美洲属于入侵物种，会取代本土传粉者——这会让认为蜜蜂“有益”的人感到惊讶
  • 最具破坏性的入侵物种往往不是最显眼的那些
  • 微生物灭绝点需要生存分析，而非简单的t检验
回答生态学问题前务必检索文献。使用
PubMed_search_articles
，搭配特定术语如“[物种] 入侵影响 [区域]”或“[生物] [生态过程]”。

COMPUTE, DON'T DESCRIBE

计算而非描述

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
当分析需要计算（统计、数据处理、评分、富集分析）时，通过Bash编写并运行Python代码。不要描述你会做什么——直接执行并报告实际结果。使用ToolUniverse工具获取数据，再用Python（pandas、scipy、statsmodels、matplotlib）进行分析。