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研究生:林畇沂
研究生(外文):Yun-Yi Lin
論文名稱:探討獵物密度對物種分布模型之影響:以東海浮游動物為例
論文名稱(外文):Evaluating performance of species distribution model by including prey density: a case study of mesozooplankton in the East China Sea
指導教授:謝志豪謝志豪引用關係
指導教授(外文):Chih-hao Hsieh
口試委員:柯佳吟張以杰郭庭君何珮綺
口試委員(外文):Chia-Ying KoYi-Jay ChangTing-Chun KuoPei-Chi Ho
口試日期:2024-01-11
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:海洋研究所
學門:自然科學學門
學類:海洋科學學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
論文頁數:60
中文關鍵詞:物種分布模型生物交互作用橈足類浮游植物模型表現
外文關鍵詞:Species distribution modelingBiotic interactionsCopepodPhytoplanktonModel performance
DOI:10.6342/NTU202400642
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生物交互關係(例如掠食及競爭)被視為形塑物種地理空間分佈的基本因子。然而多數有關物種分布模型(Species distribution modeling, SDM)的研究卻忽略了這個因子,或只使用近似的指標(如遙測測量之海表葉綠素)代替,因此模式預測表現不見得是最佳的。物種分布模型以陸域生態系為基礎開始發展,對於海洋物種的應用起步較晚,但有逐年增長的趨勢。然而作為海洋食物網中重要基石的浮游生物群集,在這方面卻沒有獲得足夠的重視。更甚者,浮游動物時常展現所謂的選擇性啃食(Selective grazing):偏好攝食特定分類群或特定個體大小的浮游植物,因此葉綠素濃度(用以作為浮游植物總生物量的指標)不一定能完全代表浮游動物的獵物密度。
本研究的目標是希望了解浮游動物和其潛在獵物間的交互作用在預測其分佈時具有怎樣的重要性,特別針對東海海域。本研究針對真哲水蚤科(Eucalanidae)、長腹劍水蚤科(Oithonidae)、葉水蚤科(Sapphirinidae)及寬水蚤科(Temoridae)四科橈足類(Copepod)分別建構了三種擁有不同變數組合的模型:一、基本模型(Basic model),只包含環境因子;二、基本模型 + 總葉綠素螢光值(Chlorophyll fluorescence,浮游植物總生物量的近似值);三、基本模型 + 浮游植物特定分類群生物體積(Biovolume),其中生物體積分為矽藻(Diatom)、雙鞭毛藻(Dinoflagellate)等分類群,不同分類群視為獨立一項因子。我們預期葉綠素與生物體積所蘊涵的生物交互關係可為基本模型增添額外效果;並且在考慮選擇性啃食的情況下,生物體積應比葉綠素更能代表橈足類所偏好的食物。因此我們預期包含生物體積之模型表現應最佳,次為葉綠素模型,基本模型則表現最差。
比較廣義相加模型(Generalized additive model)的校正赤池信息準則(The Akaike Information Criterion with correction, AICc),我們發現在真哲水蚤科、葉水蚤科與寬水蚤科上,三個模型的解釋力(Explanatory power)支持本研究的假設;然而交叉驗證(Cross-validation)預測分佈卻呈現與AICc不一致的結果。分析結果顯示,模型在加入獵物–掠食者交互關係後解釋能力確實明顯得提升,但此作用在交叉驗證預測分佈時影響並不顯著。另外添加浮游植物沒有改善長腹劍水蚤科模型的能力,與過去研究中認為該科別橈足類為肉食性的論點相呼應。綜合上述發現,我們認為考慮橈足類的食性並選擇適當的獵物分類群作為因子,對建構其物種分佈模型具有一定的幫助和重要性。
Biotic interactions (e.g., predation, competition) are considered to be fundamental factors that shape species distribution. However, most studies on species distribution modeling (SDM) often ignore biotic factors in their models or use approximations (e.g., remotely sensed surface Chlorophyll-a); therefore, the model predictions are not necessarily the best. While the methodology of SDM was developed originally from studying terrestrial organisms, growing applications have been made in marine systems. Yet, seldom of these applications focus on plankton, although plankton play an important role in marine food webs. Moreover, zooplankton graze selectively—they have a preference for phytoplankton in specific taxonomic groups or size classes; Chlorophyll-a concentrations (a proxy for total phytoplankton biomass), therefore, may not necessarily represent prey density of zooplankton.
The goal of this study is to understand the importance of biotic interaction when modeling the distribution of marine zooplankton in the East China Sea while considering their potential prey. This study constructed 3 sets of SDM for 4 copepod families (Eucalanidae, Oithonidae, Sapphirinidae, and Temoridae), respectively. The sets are (1) basic model: with abiotic predictors only; (2) basic model + total Chlorophyll fluorescence (as a proxy for total phytoplankton biomass); (3) basic model + biovolume of a specific phytoplankton taxon (e.g., diatoms, dinoflagellates), with each of them added into the model as an independent predictor. Chlorophyll and biovolume are expected to provide effects of biotic interaction, in addition to the basic model. Furthermore, considering selective grazing, the taxon-specific biovolume is expected to be more representative of copepod’s prey than Chlorophyll. We therefore expect the model considering taxon-specific phytoplankton biovolume to perform the best, followed by the Chlorophyll, and then the basic model.
The results of GAM with Akaike Information Criterion with correction (AICc) show that the explanatory power of three model sets followed our expectation for Eucalanidae, Sapphirinidae, and Temoridae, although the results from cross-validation prediction are not consistent with that from AICc analyses. The evaluation shows that inclusion of prey-predator interaction does improve models'' explanatory abilities; however, when it comes to predictive power of cross-validation, the effect is not significant. Interestingly, the results of including phytoplankton for Oithonidae are not significant; this finding is consistent with the knowledge that many Oithonids are carnivorous. The findings highlight the importance of using taxon-specific prey data for improving copepod SDMs, with consideration of diet variability among copepod taxa.
誌謝 i
中文摘要 ii
Abstract iv
Table of contents vi
Figure references viii
Table references ix
Introduction 1
Materials and Methods 7
Sampling area and time 7
Mesozooplankton (copepod) sampling and processing 7
Copepod taxa 8
Phytoplankton sampling and processing 9
Abiotic (environmental) data sampling 10
Model and Statistical Analyses 10
Results 14
The distribution of copepod biovolume 14
Phytoplankton taxa associated with copepod families 14
Comparisons of SDM explanatory abilities 15
SDM predictions and comparisons of model predictive abilities 15
Discussion 17
Reference 42
Appendix 55
Appendix 1. Temporal heterogeneity in the data set 55
Appendix 2. Spatial heterogeneity in the data set 57
Supplementary Figure S1. Variation of RMSEs among sampling months of copepod families 55
Supplementary Figure S2. The MAEs of the Eucalanidae models 58
Supplementary Figure S3. The MAEs of the Temoridae models 59
Supplementary Figure S4. The MAEs of the Sapphirinidae models 60
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