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Assessing the biogeographical and socio-ecological representativeness of the ILTER site network.

ABSTRACT: The challenges posed by climate and land use change are increasingly complex, with rising and accelerating impacts on the global environmental system. Novel environmental and ecosystem research needs to properly interpret system changes and derive management recommendations across scales. This largely depends on advances in the establishment of an internationally harmonised, long-term operating and representative infrastructure for environmental observation. This paper presents an analysis evaluating 743 formally accredited sites of the International Long-Term Ecological Research (ILTER) network in 47 countries with regard to their spatial distribution and related biogeographical and socio-ecological representativeness. "Representedness" values were computed from six global datasets. The analysis revealed a dense coverage of Northern temperate regions and anthropogenic zones most notably in the US, Europe and East Asia. Significant gaps are present in economically less developed and anthropogenically less impacted hot and barren regions like Northern and Central Africa and inner-continental parts of South America. These findings provide the arguments for our recommendations regarding the geographic expansion for the further development of the ILTER network.

SUBMITTER: Wohner C

PROVIDER: S-EPMC8171146 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Assessing the biogeographical and socio-ecological representativeness of the ILTER site network.

Wohner Christoph C Ohnemus Thomas T Zacharias Steffen S Mollenhauer Hannes H Ellis Erle C EC Klug Hermann H Shibata Hideaki H Mirtl Michael M

Ecological indicators 20210801

The challenges posed by climate and land use change are increasingly complex, with rising and accelerating impacts on the global environmental system. Novel environmental and ecosystem research needs to properly interpret system changes and derive management recommendations across scales. This largely depends on advances in the establishment of an internationally harmonised, long-term operating and representative infrastructure for environmental observation. This paper presents an analysis evalu ...[more]

PMID: 34345225

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Project description:We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.

Project description:BackgroundUnderstanding sample representativeness is key to interpreting findings from epidemiological research and applying these findings to broader populations. Though techniques for assessing sample representativeness are available, they rely on access to raw data detailing the population of interest which are often not readily available and may not be suitable for comparing large datasets. In reality, population-based data are often only available in an aggregated format. In this study, we aimed to examine the capability of population stability index (PSI), a popular metric to assess data drift for artificial intelligence studies, in detecting sample differences using population-based data.MethodWe obtained United States cancer statistics from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database. We queried the SEER 17-registry research database to obtain cancer count data by age, sex, and cancer site groups from the rate sessions of the SEER*State incidence database for 2000 and 2015 - 2020. We then calculated PSI scores to estimate yearly data distribution shift from 2015 to 2020 for each variable. We compared the PSI results to the Chi-Square and Cramér's V tests for the same comparisons.ResultsScores for PSI comparing age, sex, and cancer site distribution between years ranged widely from 2.96 to less than 0.01. In line with our expectations, we found moderate to substantial differences in cancer population characteristics between 2000 and all other included years using PSI. Despite small effect sizes (Cramér's V 0.01 - 0.09), Chi-Square tests were significant for most comparisons, indicating likely type-I error caused by our large sample.ConclusionsPopulation stability index can be used to examine sample differences in healthcare studies where only binned data are available or where large datasets may reduce the reliability of other metrics. Inclusion of PSI in epidemiological research will give greater confidence that results are representative of the general population.

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Assessing the biogeographical and socio-ecological representativeness of the ILTER site network.

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Assessing the biogeographical and socio-ecological representativeness of the ILTER site network.

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