Biology subjects

Stepinski, T.

Publications and source records attributed to Stepinski, T..

3 recordsLinked to original sources

bioRxiv2024

Predicting future patterns of land cover from climate projections using machine learning

Vegetation plays a crucial role in the Earths system, and its characteristics are strongly influenced by climate. Previous studies have investigated the climate-vegetation relationship, often attempting to predict vegetation types based on climate data. Many of them have utilized biome types as proxies for different vegetation forms. Biomes, although widely used, are not always optimal for this task. They are broadly defined, a priori linked to climate, and subject to change over time. This study proposes a novel approach by using the local composition of land cover (LC) categories as descriptors of vegetation types and examines the feasibility of modeling such compositions based on climate data. The investigation focuses on the Sahel region of Africa, which is tessellated into 5 x 5 km square tiles, serving as the basic units of analysis. The independent variable comprises a set of bioclimatic variables assigned to each tile, while the dependent variable consists of shares of each LC category within the tile. The modeling framework involves a set of n regressions, one for each LC category. The K-nearest neighbors (KNN) algorithm is employed to ensure that interdependently predicted shares sum up to 100%. The models performance is validated using 2010 data, where both climate and LC information are available. The mean absolute value of residuals ranges from 1% to 11%, depending on the LC category. Subsequently, future predictions of LC patterns are made for 2040, 2070, and 2100 using climate projections under IPCC scenarios 370 and 585. A novel visualization technique called synthetic landscape is introduced to visually compare the temporal sequence of predicted LC maps from 2010 to 2100 with similar sequences of biome maps and Koppen-Geiger climate type maps. This comparison highlights overall similarities across all sequences but also reveals some significant differences.

ecology↗

bioRxiv2023

The kinetic Ising model encapsulates essential dynamics of land pattern change

A land pattern change represents a globally significant trend with implications for the environment, climate, and societal well-being. While various methods have been developed to predict land change, our understanding of the underlying change processes remains inadequate. To address this issue, we investigate the suitability of the 2D kinetic Ising model (IM), an idealized model from statistical mechanics, for simulating land change dynamics. We test the IM on a variety of diverse thematic contexts. Specifically, we investigate four sites characterized by distinct patterns, presumably driven by different physical processes. Each site is observed on eight occasions between 2001 and 2019. Given the observed pattern at times ti, i = 1, ..., 7, we find two parameters of the IM such that the model-evolved land pattern at ti+1 resembles the observed land pattern at that time. Our findings indicate that the IM produces approximate matches to the observed patterns in terms of layout, composition, texture, and patch size distributions. Notably, the IM simulations even achieve a high degree of cell-scale pattern accuracy in two of the sites. Nevertheless, the IM has certain limitations, including its inability to model linear features, account for the formation of new large patches, and handle pattern shifts.

ecology↗

bioRxiv2018

Information-theoretical approach to measuring landscape complexity

ContextQuantitative grouping of similar landscape patterns is an important part of landscape ecology due to the relationship between a pattern and an underlying ecological process. One of the priorities in landscape ecology is a development of the theoretically consistent framework for quantifying, ordering and classifying landscape patterns.\n\nObjectiveTo demonstrate that the Information Theory as applied to a bivariate random variable provides a consistent framework for quantifying, ordering, and classifying landscape patterns.\n\nMethodsAfter presenting Information Theory in the context of landscapes, information-theoretical metrics were calculated for an exemplar set of landscapes embodying all feasible configurations of land cover patterns. Sequences and 2D parametrization of patterns in this set were performed to demonstrate the feasibility of Information Theory for the analysis of landscape patterns.\n\nResultsUniversal classification of landscape into pattern configuration types was achieved by transforming landscapes into a 2D space of weakly correlated information-theoretical metrics. An ordering of landscapes by any single metric cannot produce a sequence of continuously changing patterns. In real-life patterns, diversity induces complexity - increasingly diverse patterns are increasingly complex.\n\nConclusionsInformation theory provides a consistent, theory-based framework for the analysis of landscape patterns. Information-theoretical parametrization of landscapes offers a method for their classification.

ecology↗