Holocene vegetation dynamics on the Apakará summit of the neotropical Guayana Highlands and potential environmental drivers

Abstract In dry habitats of European lowlands terricolous lichens and bryophytes are almost neglected in conservation practises, even if they may strongly contribute to biodiversity. This study aims at (a) testing the role of heathlands, acidic and calcareous dry grasslands for lichen and bryophyte diversity and conservation in lowland areas of northern Italy characterized by high human impact and habitat fragmentation; (b) detecting the effect of environmental drivers and vegetation dynamics on species richness and composition. Lichens, bryophytes, vascular plants, and environmental variables were recorded in 287 circular plots for 75 sites. Our results indicate that heathlands, acidic and calcareous dry grasslands host peculiar terricolous lichen and bryophyte communities that include several species of conservation concern. Thus, each habitat provides a complementary contribution to lichen and bryophyte diversity in continental lowland landscapes. Furthermore, in each habitat different factors drive species richness and composition with contrasting patterns between lichens and bryophytes. In terms of conservation, our results indicate that management of lowland dry habitats should act at both local and landscape scales. At local scale, vegetation dynamics should be controlled in order to avoid biodiversity loss due to vegetation dynamics and wood encroachment. At the landscape scale, patches of all the three habitats should be maintained to maximize regional diversity.

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New deep-learning based approaches for forest landscape modeling

10.5194/egusphere-egu21-16528 ◽

2021 ◽

Author(s):

Werner Rammer ◽

Rupert Seidl

Keyword(s):

Deep Learning ◽

Vegetation Dynamics ◽

Large Scale ◽

Spatial Scales ◽

Environmental Drivers ◽

Conceptual Approach ◽

Vegetation Development ◽

Novel Approach ◽

Process Understanding ◽

Forest Landscape Modeling

<p>In times of rapid global change, the ability to faithfully predict the development of vegetation on larger scales is of key relevance to society. However, ecosystem models that incorporate enough process understanding for being applicable under future and non-analog conditions are often restricted to finer spatial scales due to data and computational constraints. Recent breakthroughs in machine learning, particularly in the field of deep learning, allow bridging this scale mismatch by providing new means for analyzing data, e.g., in remote sensing, but also new modelling approaches. We here present a novel approach for Scaling Vegetation Dynamics (SVD) which uses a deep neural network for predicting large-scale vegetation development. In a first step, the network learns its representation of vegetation dynamics as a function of current vegetation state and environmental drivers from process-based models and empirical data. The trained model is then used within of a dynamic simulation on large spatial scales. In this contribution we introduce the conceptual approach of SVD and show results for example applications in Europe and the US. More broadly we discuss aspects of applying deep learning in the context of ecological modeling.</p>

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Vegetation dynamics and mesophication in response to conifer encroachment within an ultramafic system

Australian Journal of Botany ◽

10.1071/bt14241 ◽

2015 ◽

Vol 63 (4) ◽

pp. 292 ◽

Cited By ~ 5

Author(s):

J. Burgess ◽

K. Szlavecz ◽

N. Rajakaruna ◽

S. Lev ◽

C. Swan

Keyword(s):

Species Composition ◽

Spatial Data ◽

Vegetation Dynamics ◽

Basal Area ◽

Woody Species ◽

Environmental Drivers ◽

Evolutionary Significance ◽

Stem Density ◽

Endemic Plant ◽

Α Diversity

The biological, ecological, and evolutionary significance of serpentine habitats has long been recognised. We used an integrated physiochemical dataset combining plot spatial data with temporal data from tree cores to evaluate changes in soils and vegetation. Data suggest that this unique habitat is undergoing a transition, endangering local biodiversity and endemic plant species. The objective of this work was to analyse the vegetation dynamics of a xeric serpentine savanna located in the Mid-Atlantic, USA. We employed vegetation surveys of 32 10 × 15 m quadrats to obtain woody species composition, density, basal area, and developed a spatial physiochemical dataset of substrate geochemistry to independently summarise the data using regression and ordination techniques. This information was interpreted alongside historical, dendrochronologic and soil stable carbon isotopic data to evaluate successional dynamics. Comparisons among geologic, pedologic and vegetation environmental drivers indicated broad correlations across an environmental gradient, corresponding to a grassland to forest transition. The woodland communities appear to be part of a complex soil moisture and chemistry gradient that affects the extent, density, basal area and species composition of these communities. Over the gradient, there is an increase in α diversity, a decrease in the density of xeric and invasive species, and an increase in stem density of more mesic species. Dendrochronology suggests poor recruitment of xeric species and concomitant increase in more mesic species. The data indicated that former C4-dominated grasslands were initially invaded by conifers and are now experiencing mesophication, with growing dominance by Acer, Nyssa and more mesic Quercus and Fagus species.

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Importance of drivers and processes for the predictive uncertainty in vegetation dynamics change across European forests

10.5194/egusphere-egu21-3831 ◽

2021 ◽

Author(s):

Johannes Oberpriller ◽

Peter Anthoni ◽

Almut Arneth ◽

Christine Herschlein ◽

Andreas Krause ◽

...

Keyword(s):

Environmental Conditions ◽

Vegetation Dynamics ◽

State Of The Art ◽

Stress Gradient ◽

Environmental Drivers ◽

Vegetation Model ◽

Predictive Uncertainty ◽

European Forests ◽

Model Predictions ◽

Increasing Temperature

<p>Model predictions about future states of ecosystems under environmental change are uncertain. Understanding which factors drive these uncertainties is of immense value for directing research, but also for their interpretations. Here, we analyse sensitivities and uncertainties of a state of the art dynamic vegetation model (LPJ-GUESS) across European forests. We found that predictions of carbon fluxes are most sensitive to structure-related and mortality-related parameters, but most uncertainty is induced by drivers, nitrogen-, water- and mortality-modules. The uncertainty induced by drivers increases with increasing temperature, decreasing precipitation and from north to south across Europe. Moreover, environmental conditions change the resulting uncertainties in other processes. In this context, we encounter that the stress-gradient hypothesis is implicitly displayed in the model processes. In conclusion, our study stresses the importance of&#160; environmental drivers for ecosystem predictions not only due to their uncertainty contributions but also because they determine the uncertainties of other processes. &#160;</p>

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