Numerical and functional response of phagotrophic aquatic protists: the ideal experiment-and why we cannot get it

吞噬性水生原生生物的数量和功能反应:理想的实验——以及我们为何无法实现

阅读:1

Abstract

Protists are paramount for biogeochemical cycling in every aquatic ecosystem due to their vast population sizes and physiological versatility. Numerical response (NR) and functional response (FR) experiments are cornerstones of trait-based functional ecology and are increasingly studied experimentally with phagotrophic aquatic protists. Such experiments provide estimates of protist growth, production and consumption rates in relation to biotic (food supply) and abiotic variables (e.g., temperature, pH, and salinity) that can be used in mathematical models of ecosystem dynamics. Until now, NR and FR experiments lack standardization and are subject to potential pitfalls that received little attention in the literature. It is a common misconception that an experimental investigation of a phagotrophic protist's growth and ingestion rates represents a single experiment with replication. I demonstrate that a typical NR or FR experiment consists of a series of individual experiments in which not only the experimental target variable (food, i.e., prey abundance or biomass) changes but also other factors (physiological conditions of prey and predator, nutrient levels, unwanted contaminants) vary that may affect the experimental outcome. Standardizing all variables affecting a series of NR and FR experiments is virtually impossible. I further explain why FR experiments are more prone to experimental bias than NR experiments. Since it is principally impossible to perform an "ideal" NR or FR experiment, fulfilling all criteria of experimental standardization, the goal is to reduce the "noise" to obtain statistically significant and reproducible results. To this end, I provide guidelines that may help achieve this goal in future studies.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。