Modelling multivariate data using product copulas and minimum distance estimators: an exemplary application to ecological traits

Modelling and applying multivariate distributions is an important topic in ecology. In particular in plant ecology, the multidimensional nature of plant traits comes with challenges such as wide ranges in observations as well as correlations between several characteristics. In other disciplines (e.g., finances and economics), copulas have been proven as a valuable tool for modelling multivariate distributions. However, applications in ecology are still rarely used. Here, we present a copula-based methodology of fitting multivariate distributions to ecological data. We used product copula models to fit multidimensional plant traits, on example of observations from the global trait database TRY. The fitting procedure is split into two parts: fitting the marginal distributions and fitting the copula. We found that product copulas are well suited to model ecological data as they have the advantage of being asymmetric (similar to the observed data). Challenges in the fitting were mainly addressed to limited amount of data. In view of growing global databases, we conclude that copula modelling provides a great potential for ecological modelling.

A framework for defining livestock ecotypes based on ecological modelling and exploring genomic environmental adaptation: the example of Ethiopian village chicken

In evolutionary ecology, an ecotype is a population that is genetically adapted to specific environmental conditions. Environmental and genetic characterisation of livestock ecotypes can play a crucial role in conservation and breeding improvement, particularly to achieve climate resilience. However, livestock ecotypes are often arbitrarily defined without a detailed characterisation of their agro-ecologies. In this study, we employ a novel integrated approach, combining Ecological Niche Modelling (ENM) with genomics, to delineate ecotypes based on environmental characterisation of population habitats and unravel the signatures of adaptive selection in the ecotype genomes. The method was applied on 25 Ethiopian village chicken populations representing diverse agro-climatic conditions. ENM identified six key environmental drivers of adaptation and delineated 12 ecotypes. Within- ecotype selection signature analyses (using Hp and iHS methods) identified 1,056 candidate sweep regions (SRs) associated with diverse biological processes. A few biological pathways were shared amongst most ecotypes and the involved genes showed functions important for scavenging chickens, e.g. neuronal development/processes, immune response, vision development, and learning. Genotype-environment association using Redundancy Analysis (RDA) allowed for correlating ∼33% of the SRs with major environmental drivers. Inspection of some strong candidate genes from selection signature analysis and RDA showed highly relevant functions in relation to the major environmental drivers of corresponding ecotypes. This integrated approach offers a powerful tool to gain insight into the complex processes of adaptive evolution including the genotype x environment (GxE) interactions.

Geochemical and reactive transport modelling in R with the RedModRphree package

Advances in computing and experimental capabilities in the research of water-rock-interactions require geoscientists to routinely combine laboratory data and models to produce new knowledge. Data science is hence a more and more pervasive instrument for geochemists, which in turn demands flexible and easy to learn software adaptable to their specific needs. The GNU R language and programming environment has established itself as de facto standard language for statistics and machine learning, enjoying increasing diffusion in many applied scientific fields such as bioinformatics, chemometrics and ecological modelling. The availability of excellent third party extensions as well as its advanced graphical and numerical capabilities make R an ideal platform for comprehensive geochemical data analysis, experiment evaluation and modelling. We introduce the open source RedModRphree extension package, which leverages the R interface to the established PHREEQC geochemical simulator. The aim of RedModRphree is to provide the user with an easy-to-use, high-level interface to program algorithms involving geochemical models: parameter calibration, error and sensitivity analysis, thermodynamical database manipulation, up to CPU-intensive parallel coupled reactive transport models. Among the out-of-the-box features included in RedModRphree, we highlight the computation and visualization of Pourbaix (Eh-pH) diagrams using full speciation as computed by PHREEQC and the implementation of 1D advective reactive transport supporting the use of surrogate models replacing expensive equation-based calculations.

Large-bodied birds are over-represented in unstructured citizen science data

Citizen science platforms are quickly accumulating hundreds of millions of biodiversity observations around the world annually. Quantifying and correcting for the biases in citizen science datasets remains an important first step before these data are used to address ecological questions and monitor biodiversity. One source of potential bias among datasets is the difference between those citizen science programs that have unstructured protocols and those that have semi-structured or structured protocols for submitting observations. To quantify biases in an unstructured citizen science platform, we contrasted bird observations from the unstructured iNaturalist platform with that from a semi-structured citizen science platform—eBird—for the continental United States. We tested whether four traits of species (body size, commonness, flock size, and color) predicted if a species was under- or over-represented in the unstructured dataset compared with the semi-structured dataset. We found strong evidence that large-bodied birds were over-represented in the unstructured citizen science dataset; moderate evidence that common species were over-represented in the unstructured dataset; strong evidence that species in large groups were over-represented; and no evidence that colorful species were over-represented in unstructured citizen science data. Our results suggest that biases exist in unstructured citizen science data when compared with semi-structured data, likely as a result of the detectability of a species and the inherent recording process. Importantly, in programs like iNaturalist the detectability process is two-fold—first, an individual organism needs to be detected, and second, it needs to be photographed, which is likely easier for many large-bodied species. Our results indicate that caution is warranted when using unstructured citizen science data in ecological modelling, and highlight body size as a fundamental trait that can be used as a covariate for modelling opportunistic species occurrence records, representing the detectability or identifiability in unstructured citizen science datasets. Future research in this space should continue to focus on quantifying and documenting biases in citizen science data, and expand our research by including structured citizen science data to understand how biases differ among unstructured, semi-structured, and structured citizen science platforms.

Urban Ecosystem Vulnerability Assessment of Support Climate-Resilient City Development

Climate change poses a threat to cities. Geospatial information and communication technology (Geo-ICT) assisted planning is increasingly being utilised to foster urban sustainability and adaptability to climate change. To fill the theoretical and practical gaps of urban adaptive planning and Geo-ICT implementation, this article presents an urban ecosystem vulnerability assessment approach using integrated socio-ecological modelling. The application of the Geo-ICT method is demonstrated in a specific case study of climate-resilient city development in Nanjing (China), aiming at helping city decision-makers understand the general geographic data processing and policy revision processes in response to hypothetical future disruptions and pressures on urban social, economic, and environmental systems. Ideally, the conceptual framework of the climate-resilient city transition proposed in this study effectively integrates the geographic data analysis, policy modification, and participatory planning. In the process of model building, we put forward the index system of urban ecosystem vulnerability assessment and use the assessment result as input data for the socio-ecological model. As a result, the model reveals the interaction processes of local land use, economy, and environment, further generating an evolving state of future land use in the studied city. The findings of this study demonstrate that socio-ecological modelling can provide guidance in adjusting the human-land interaction and climate-resilient city development from the perspective of macro policy. The decision support using urban ecosystem vulnerability assessment and quantitative system modelling can be useful for urban development under a variety of environmental change scenarios.

A Sensitivity Analysis of the SPACSYS Model

A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.

МОДЕЛИ МАКСИМАЛЬНОЙ ЭНТРОПИИ И ПРОСТРАНСТВЕННОЕ РАСПРЕДЕЛЕНИЕ ВИДОВ ДОННЫХ СООБЩЕСТВ НА ТЕРРИТОРИИ СРЕДНЕГО И НИЖНЕГО ПОВОЛЖЬЯ

Представлены результаты применения метода максимальной энтропии (MaxEnt) для моделирования пространственного распределения видов макрозообентоса на территории Среднего и Нижнего Поволжья. Использовались данные гидробиологического мониторинга многолетних (1990-2019 гг.) исследований донных сообществ в 108 средних и малых реках. В качестве независимых переменных, отражающих условия среды, построенные модели включали климатические и ландшафтные показатели растрового типа, загружаемые с сервера WorldClim (средние температуры, количество осадков, высота и вертикальная расчлененность рельефа). Приводятся результаты тестирования качества и прогнозирующей силы моделей, а также статистические показатели относительной важности каждого из использованных абиотических факторов. Обсуждаются проблемы использования различных алгоритмов построения моделей пространственного распределения видов применительно к данным гидробиологических наблюдений пресноводных лотических экосистем. 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