Species interaction: Revisiting its terminology and concept

How to define a dolphin "group"? Need for consistency and justification based on objective criteria

Group living is a critical component of the ecology of social animals such as delphinids. In many studies on these animals, groups represent sampling units that form the basis of the collection and analysis of data on their abundance, behavior, and social structure. Nevertheless, defining what constitutes a group has proven problematic. There is inconsistency in the terms and criteria used and many definitions lack biological justification. We conducted a literature review and an online expert survey to assess various terms (group, school, party, and pod), and their definitions as applied to delphinids to identify issues to ultimately make recommendations. Of 707 studies analyzed, 325 explicitly defined one or more terms, providing 344 definitions. Additionally, 192 definitions were obtained from the survey. Among these definitions, group was the most common term used (review: 286 definitions, 83.1%; survey: 69 definitions, 35.9%) and the most familiar to the survey respondents (73 respondents, 100.0%). In definitions of group, spatial proximity was the most used criterion (review: 200 definitions, 71.2%; survey: 53 definitions, 81.5%) followed by behavior (review: 176 definitions, 62.6%; survey: 38 definitions, 58.5%). The terms and criteria used to define delphinid groups vary considerably. Rather than proposing a single formal definition, we instead recommend that the term group and spatial proximity criteria be used to define sampling units of individuals observed in the field. Furthermore, we propose a process for formulating definitions that involves analyzing interindividual distances to determine naturally occurring patterns that are indicative of group membership. Although this process is based principally on the spatial proximity of individuals, it could also incorporate the behavior of group members by evaluating the influence of behavior on interindividual distances. Such a process produces definitions that are biologically meaningful and compatible across studies and populations, thus increasing our ability to draw strong conclusions about group living in delphinids.

Data standardization of plant-pollinator interactions

Background

Animal pollination is an important ecosystem function and service, ensuring both the integrity of natural systems and human well-being. Although many knowledge shortfalls remain, some high-quality data sets on biological interactions are now available. The development and adoption of standards for biodiversity data and metadata has promoted great advances in biological data sharing and aggregation, supporting large-scale studies and science-based public policies. However, these standards are currently not suitable to fully support interaction data sharing.

Results

Here we present a vocabulary of terms and a data model for sharing plant–pollinator interactions data based on the Darwin Core standard. The vocabulary introduces 48 new terms targeting several aspects of plant–pollinator interactions and can be used to capture information from different approaches and scales. Additionally, we provide solutions for data serialization using RDF, XML, and DwC-Archives and recommendations of existing controlled vocabularies for some of the terms. Our contribution supports open access to standardized data on plant–pollinator interactions.

Conclusions

The adoption of the vocabulary would facilitate data sharing to support studies ranging from the spatial and temporal distribution of interactions to the taxonomic, phenological, functional, and phylogenetic aspects of plant–pollinator interactions. We expect to fill data and knowledge gaps, thus further enabling scientific research on the ecology and evolution of plant–pollinator communities, biodiversity conservation, ecosystem services, and the development of public policies. The proposed data model is flexible and can be adapted for sharing other types of interactions data by developing discipline-specific vocabularies of terms.

Inferring ecosystem networks as information flows

The detection of causal interactions is of great importance when inferring complex ecosystem functional and structural networks for basic and applied research. Convergent cross mapping (CCM) based on nonlinear state-space reconstruction made substantial progress about network inference by measuring how well historical values of one variable can reliably estimate states of other variables. Here we investigate the ability of a developed optimal information flow (OIF) ecosystem model to infer bidirectional causality and compare that to CCM. Results from synthetic datasets generated by a simple predator-prey model, data of a real-world sardine-anchovy-temperature system and of a multispecies fish ecosystem highlight that the proposed OIF performs better than CCM to predict population and community patterns. Specifically, OIF provides a larger gradient of inferred interactions, higher point-value accuracy and smaller fluctuations of interactions and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-diversity including their characteristic time delays. We propose an optimal threshold on inferred interactions that maximize accuracy in predicting fluctuations of effective \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α-diversity, defined as the count of model-inferred interacting species. Overall OIF outperforms all other models in assessing predictive causality (also in terms of computational complexity) due to the explicit consideration of synchronization, divergence and diversity of events that define model sensitivity, uncertainty and complexity. Thus, OIF offers a broad ecological information by extracting predictive causal networks of complex ecosystems from time-series data in the space-time continuum. The accurate inference of species interactions at any biological scale of organization is highly valuable because it allows to predict biodiversity changes, for instance as a function of climate and other anthropogenic stressors. This has practical implications for defining optimal ecosystem management and design, such as fish stock prioritization and delineation of marine protected areas based on derived collective multispecies assembly. OIF can be applied to any complex system and used for model evaluation and design where causality should be considered as non-linear predictability of diverse events of populations or communities.