scholarly journals Fine scale prediction of ecological community composition using a two-step sequential machine learning ensemble

2021 ◽  
Author(s):  
Icíar Civantos ◽  
Javier García-Algarra ◽  
David García-Callejas ◽  
Javier Galeano ◽  
Oscar Godoy ◽  
...  
Keyword(s):  
Species Composition ◽  
Species Interactions ◽  
Predictive Power ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Data Driven ◽  
Series Data ◽  
Soil Conditions ◽  
Fine Scale ◽  
Species Abundances

Prediction is one the last frontiers in ecology. Indeed, predicting fine scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models informed by ecological deterministic processes to predict species abundances using reasonably easy to obtain data. To overcome the classical procedure in ecology of parameterizing complex population models of multiple species interactions and poor predictive power, we followed instead a sequential data-driven modeling approach. We use this framework to predict species abundances over 5 years in a highly diverse annual plant community. Our models show a surprisingly high spatial predictive accuracy using only easy to measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggest that predicting the temporal dimension of our system requires longer time series data. Such data would likely capture additional sources of variability that determine temporal patterns of species abundances. In addition, we show that these data-driven models can also inform back mechanistic models of important missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Being able to gain predictive power at fine-scale species composition while maintaining a mechanistic understanding of the underlying processes can be a pivotal tool for conservation, specially given the human induced rapid environmental changes we are experiencing. Here, we document how this objective can be achieved by promoting the interplay between classic modelling approaches in ecology and recently developed data-driven models.

scholarly journals Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

2021 ◽  
Vol 17 (12) ◽  
pp. e1008906
Author(s):  
Icíar Civantos-Gómez ◽  
Javier García-Algarra ◽  
David García-Callejas ◽  
Javier Galeano ◽  
Oscar Godoy ◽  
...  
Keyword(s):  
Species Composition ◽  
Species Interactions ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Predictive Accuracy ◽  
Data Driven ◽  
Soil Conditions ◽  
Fine Scale ◽  
Data Set ◽  
Species Abundances

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.

scholarly journals Linking biotic homogenisation with large-scale changes of species associations

2020 ◽  
Author(s):  
Stanislas Rigal ◽  
Vincent Devictor ◽  
Pierre Gaüzère ◽  
Sonia Kéfi ◽  
Jukka T. Forsman ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Global Change ◽  
Species Composition ◽  
Species Interactions ◽  
Temporal Dynamics ◽  
Temporal Changes ◽  
Species Association ◽  
Β Diversity ◽  
Biotic Homogenisation ◽  
Spatio Temporal

AbstractAimThe impact of global change on biodiversity is commonly assessed in terms of changes in species distributions, species richness and species composition across communities. Whether and how much interactions between species are also changing is much less documented and mostly limited to local studies of ecological networks. Moreover, we largely ignore how biotic homogenisation (i.e. the replacement of a set of diverse and mainly specialist species by a few generalists) is affecting or being affected by changes in the structure of species interactions. Here, we approximate species interactions with species associations based on the correlation in species spatial co-occurrence to understand the spatio-temporal changes of species interactions and their relationship to biotic homogenisation.LocationFrance.Time period2001-2017.Major taxa studiedCommon breeding birds.MethodsWe use network approaches to build three community-aggregated indices to characterise species associations and we compare them to changes in species composition in communities. We evaluate the spatial distribution and temporal dynamics of these indices in a dataset of bird co-abundances of more than 100 species monitored for 17 years (2001-2017) from 1,969 sites across France. We finally test whether spatial and temporal changes of species associations are related to species homogenisation estimated as the spatio-temporal dynamics of β-diversity.ResultsWe document a non-random spatial distribution of both structure and temporal changes in species association networks. We also report a directional change in species associations linked to β-diversity modifications in space and time, suggesting that biotic homogenisation affects not only species composition but also species associations.Main ConclusionsOur study highlights the importance of evaluating changes of species association networks, in addition to species turnover when studying biodiversity responses to global change.

Langfristige Entwicklung von zwei Waldgesellschaften im Białowieża-Urwald

2008 ◽  
Vol 159 (4) ◽  
pp. 80-90 ◽  
Author(s):  
Bogdan Brzeziecki ◽  
Feliks Eugeniusz Bernadzki
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Stand Structure ◽  
Temporal Dynamics ◽  
Structural Parameters ◽  
Forest Communities ◽  
Natural Forests ◽  
Tree Species Composition ◽  
Strong Trend ◽  
Long Term Study

The results of a long-term study on the natural forest dynamics of two forest communities on one sample plot within the Białowieża National Park in Poland are presented. The two investigated forest communities consist of the Pino-Quercetum and the Tilio-Carpinetum type with the major tree species Pinus sylvestris, Picea abies, Betula sp., Quercus robur, Tilia cordata and Carpinus betulus. The results reveal strong temporal dynamics of both forest communities since 1936 in terms of tree species composition and of general stand structure. The four major tree species Scots pine, birch, English oak and Norway spruce, which were dominant until 1936, have gradually been replaced by lime and hornbeam. At the same time, the analysis of structural parameters indicates a strong trend towards a homogenization of the vertical stand structure. Possible causes for these dynamics may be changes in sylviculture, climate change and atmospheric deposition. Based on the altered tree species composition it can be concluded that a simple ≪copying≫ (mimicking) of the processes taking place in natural forests may not guarantee the conservation of the multifunctional character of the respective forests.

scholarly journals Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 540
Author(s):  
Fabio Amaral ◽  
Wallace Casaca ◽  
Cassio M. Oishi ◽  
José A. Cuminato
Keyword(s):  
Learning Strategies ◽  
Time Series Data ◽  
Sao Paulo ◽  
The State ◽  
Data Driven ◽  
Series Data ◽  
São Paulo ◽  
Time Data ◽  
Unified Framework ◽  
Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

scholarly journals Repeated surveying over 6 years reveals that fine-scale habitat variables are key to tropical mountain ant assemblage composition and functional diversity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mulalo M. Muluvhahothe ◽  
Grant S. Joseph ◽  
Colleen L. Seymour ◽  
Thinandavha C. Munyai ◽  
Stefan H. Foord
Keyword(s):  
Climate Change ◽  
Species Composition ◽  
High Altitude ◽  
Functional Diversity ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Abiotic Factors ◽  
Composition Analysis ◽  
Fine Scale

AbstractHigh-altitude-adapted ectotherms can escape competition from dominant species by tolerating low temperatures at cooler elevations, but climate change is eroding such advantages. Studies evaluating broad-scale impacts of global change for high-altitude organisms often overlook the mitigating role of biotic factors. Yet, at fine spatial-scales, vegetation-associated microclimates provide refuges from climatic extremes. Using one of the largest standardised data sets collected to date, we tested how ant species composition and functional diversity (i.e., the range and value of species traits found within assemblages) respond to large-scale abiotic factors (altitude, aspect), and fine-scale factors (vegetation, soil structure) along an elevational gradient in tropical Africa. Altitude emerged as the principal factor explaining species composition. Analysis of nestedness and turnover components of beta diversity indicated that ant assemblages are specific to each elevation, so species are not filtered out but replaced with new species as elevation increases. Similarity of assemblages over time (assessed using beta decay) did not change significantly at low and mid elevations but declined at the highest elevations. Assemblages also differed between northern and southern mountain aspects, although at highest elevations, composition was restricted to a set of species found on both aspects. Functional diversity was not explained by large scale variables like elevation, but by factors associated with elevation that operate at fine scales (i.e., temperature and habitat structure). Our findings highlight the significance of fine-scale variables in predicting organisms’ responses to changing temperature, offering management possibilities that might dilute climate change impacts, and caution when predicting assemblage responses using climate models, alone.

Effect of the Gabčíkovo Waterworks (Slovakia) on riparian floodplain forest ecosystems in the Danube inland delta: vegetation dynamics and trends

Biologia ◽  
2017 ◽  
Vol 72 (7) ◽  
Author(s):  
Mária Petrášová-Šibíková ◽  
Igor Matečný ◽  
Eva Uherčíková ◽  
Peter Pišút ◽  
Silvia Kubalová ◽  
...  
Keyword(s):  
Species Composition ◽  
Forest Ecosystems ◽  
Supply System ◽  
Floodplain Forest ◽  
Soil Conditions ◽  
Plant Species Composition ◽  
Forest Species ◽  
The Past ◽  
The Impact ◽  
Rare Opportunity

AbstractHuman alteration of watercourses is global phenomenon that has had significant impacts on local ecosystems and the services they provide. Monitoring of abiotic and biotic changes is essential to mitigating long-lasting effects, and the 23-year dataset from the Gabčíkovo Waterworks provided a rare opportunity to assess the impact of groundwater regimes on vegetation. The main aim of this study was to describe the effect of the Gabčíkovo Waterworks on vegetation structure and species composition of the adjacent riparian floodplain forests over the past 23 years. The results are based on studies of three permanent monitoring plots (PMPs) located in the Danube inland delta – two outside (PMP 1 and 3) and one (PMP 2) fully under the influence of the artificial supply system. Our results demonstrate that the Danube inland delta was negatively affected by the Gabčíkovo construction, particularly for sites outside of the artificial supply system. There was a significant decrease in soil moisture and increase in nitrogen at both external PMPs (1 and 3). Alter soil conditions were accompanied by negative changes in plant species composition demonstrated by decreases in the number of typical floodplain forest species that are characteristic for the alliance

scholarly journals A three-dimensional view on biodiversity changes: spatial, temporal, and functional perspectives on fish communities in the Baltic Sea

2018 ◽  
Vol 75 (7) ◽  
pp. 2463-2475 ◽  
Author(s):  
Romain Frelat ◽  
Alessandro Orio ◽  
Michele Casini ◽  
Andreas Lehmann ◽  
Bastien Mérigot ◽  
...  
Keyword(s):  
Baltic Sea ◽  
Environmental Conditions ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Marine Ecosystem ◽  
Fish Communities ◽  
The Baltic Sea ◽  
Functional Richness ◽  
Holistic Understanding ◽  
The Baltic

Abstract Fisheries and marine ecosystem-based management requires a holistic understanding of the dynamics of fish communities and their responses to changes in environmental conditions. Environmental conditions can simultaneously shape the spatial distribution and the temporal dynamics of a population, which together can trigger changes in the functional structure of communities. Here, we developed a comprehensive framework based on complementary multivariate statistical methodologies to simultaneously investigate the effects of environmental conditions on the spatial, temporal and functional dynamics of species assemblages. The framework is tested using survey data collected during more than 4000 fisheries hauls over the Baltic Sea between 2001 and 2016. The approach revealed the Baltic fish community to be structured into three sub-assemblages along a strong and temporally stable salinity gradient decreasing from West to the East. Additionally, we highlight a mismatch between species and functional richness associated with a lower functional redundancy in the Baltic Proper compared with other sub-areas, suggesting an ecosystem more susceptible to external pressures. Based on a large dataset of community data analysed in an innovative and comprehensive way, we could disentangle the effects of environmental changes on the structure of biotic communities—key information for the management and conservation of ecosystems.

scholarly journals Temporal Dynamics of Cloacal Microbiota in Adult Laying Chickens With and Without Access to an Outdoor Range

2021 ◽  
Vol 11 ◽  
Author(s):  
Janneke Schreuder ◽  
Francisca C. Velkers ◽  
Alex Bossers ◽  
Ruth J. Bouwstra ◽  
Willem F. de Boer ◽  
...  
Keyword(s):  
Microbial Community ◽  
Community Composition ◽  
Intestinal Microbiota ◽  
Environmental Changes ◽  
Temporal Dynamics ◽  
Microbial Community Composition ◽  
Sudden Change ◽  
Rrna Gene ◽  
Poultry House ◽  
Over Time

Associations between animal health and performance, and the host’s microbiota have been recently established. In poultry, changes in the intestinal microbiota have been linked to housing conditions and host development, but how the intestinal microbiota respond to environmental changes under farm conditions is less well understood. To gain insight into the microbial responses following a change in the host’s immediate environment, we monitored four indoor flocks of adult laying chickens three times over 16 weeks, during which two flocks were given access to an outdoor range, and two were kept indoors. To assess changes in the chickens’ microbiota over time, we collected cloacal swabs of 10 hens per flock and performed 16S rRNA gene amplicon sequencing. The poultry house (i.e., the stable in which flocks were housed) and sampling time explained 9.2 and 4.4% of the variation in the microbial community composition of the flocks, respectively. Remarkably, access to an outdoor range had no detectable effect on microbial community composition, the variability of microbiota among chickens of the same flock, or microbiota richness, but the microbiota of outdoor flocks became more even over time. Fluctuations in the composition of the microbiota over time within each poultry house were mainly driven by turnover in rare, rather than dominant, taxa and were unique for each flock. We identified 16 amplicon sequence variants that were differentially abundant over time between indoor and outdoor housed chickens, however none were consistently higher or lower across all chickens of one housing type over time. Our study shows that cloacal microbiota community composition in adult layers is stable following a sudden change in environment, and that temporal fluctuations are unique to each flock. By exploring microbiota of adult poultry flocks within commercial settings, our study sheds light on how the chickens’ immediate environment affects the microbiota composition.

scholarly journals Learning data-driven reduced elastic and inelastic models of spot-welded patches

2021 ◽  
Vol 22 ◽  
pp. 32
Author(s):  
Agathe Reille ◽  
Victor Champaney ◽  
Fatima Daim ◽  
Yves Tourbier ◽  
Nicolas Hascoet ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Structural Response ◽  
Data Driven ◽  
Fine Scale ◽  
Time Step ◽  
Large Structures ◽  
Computational Performance ◽  
Local Patch ◽  
The Rich ◽  
Learning Data

Solving mechanical problems in large structures with rich localized behaviors remains a challenging issue despite the enormous advances in numerical procedures and computational performance. In particular, these localized behaviors need for extremely fine descriptions, and this has an associated impact in the number of degrees of freedom from one side, and the decrease of the time step employed in usual explicit time integrations, whose stability scales with the size of the smallest element involved in the mesh. In the present work we propose a data-driven technique for learning the rich behavior of a local patch and integrate it into a standard coarser description at the structure level. Thus, localized behaviors impact the global structural response without needing an explicit description of that fine scale behaviors.

scholarly journals ENVIRONMENTAL CHANGE AND LANDUSE SCENARIOS UNDER CHANGING CLIMATES: THE EXPERIENCE IN THE UPPER CROSS RIVER REGION, NIGERIA

2020 ◽  
Vol 6 (11) ◽  
pp. 32-42
Author(s):  
Innocent A. Ugbong ◽  
Ivan V. Budagov
Keyword(s):  
Environmental Changes ◽  
Floristic Composition ◽  
Life Forms ◽  
Soil Conditions ◽  
Land Use Patterns ◽  
Cross River ◽  
Cross River State ◽  
Use Patterns ◽  
River Region ◽  
Descriptive Approach

This paper seeks to show that due to changing climates, there are salient marginal Sahelian conditions (conditions of aridity) emerging on the Northern fringes of Cross River State, a state that is geographical positioned in the southern rainforest belt of Nigeria. The paper adopts a simple descriptive approach and shows the distinct characteristics of this zone, in terms of floristic composition and edaphic and geomorphic structures under changing conditions. Some relationships are established between environmental variables like health, water supply and crop-yield on one hand, and climatic variation, floral life-forms and soil conditions on the other. The changing land use patterns relative to environmental changes are also examined. The paper concludes with a look at current and future adaption strategies to these climate-induced conditions.

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