Biology subjects

Wiegand, K.

Publications and source records attributed to Wiegand, K..

4 recordsLinked to original sources

bioRxiv2025

ALGR: A multi-purpose agricultural landscape generator in R

O_LIAgricultural and ecological modelers commonly use maps as input for spatially explicit simulations. While real world maps are often used, they are limited by being static objects, therefore making it difficult to assess how patterns within the landscape contribute to ecological processes. Agricultural landscape generators (ALG) are a useful tool for simulating maps in a more flexible way. They can increase robustness of models that rely on landscape maps as input, they allow modelers to give spatial representation to non-spatial models, and they are a useful tool for recreating spatial patterns in agricultural-dominated landscapes. A limitation of previous ALGs is that they have rarely been designed for general use (non-open source software, not written in R, and designed for specific projects). Furthermore, they are typically either extremely general and thus oversimplified or have a high specificity for particular use cases. C_LIO_LIALGR bridges this gap by providing a general-purpose, dynamic landscape generator that balances structural realism with adaptability. ALGR generates agricultural landscapes with a three-step approach: first, outlining potential space, second, field placement inside of that space, and third, enrichment of the landscape with information. This stepwise approach ensures that ALGR generates landscapes with realistic spatial patterns while remaining adaptable to diverse regions and applications. C_LIO_LIIt is the first ALG that is specifically designed to allow a simple integration within the R programming environment and the r-spatial package environment. ALGR is designed as a general-purpose generator, which is simple to use and facilitates an easy integration in modelling workflow. C_LIO_LIWe present several examples of workflows using ALGR, to demonstrate its usefulness. Our examples include: 1) simulating different land use shares, 2) parameter tuning of ALGR to recreate real world landscape patterns 3) spatially distributing crop portfolios, and 4) using real world maps as a basis for field placement C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/646291v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@19c02c3org.highwire.dtl.DTLVardef@17975f6org.highwire.dtl.DTLVardef@12f7aa5org.highwire.dtl.DTLVardef@1cfdd0_HPS_FORMAT_FIGEXP M_FIG Visual Abstract: The three-step conceptual framework of ALGR for landscape generation C_FIG

ecology↗

bioRxiv2023

Scale-dependent landscape-biodiversity relationships shape multi-taxa diversity in an oil palm monoculture under restoration

Enhancing biodiversity in monoculture-dominated landscapes is a pressing restoration challenge. Tree islands can enhance biodiversity locally, but the role of scale-dependent processes on local biodiversity remains unclear. Using a multi-scale approach, we explored how scale-dependent processes influence the diversity of seven taxa (woody plants, understory arthropods, birds, herbaceous plants and soil bacteria, fauna, and fungi) within 52 experimental tree islands embedded in an oil palm landscape. We show that local, metacommunity (between islands), and landscape properties shaped above- and below-ground taxa diversity, with the stronger effects on above-ground taxa. The spatial extent that best-predicted diversity ranged from 150 m for woody plants to 700 m for understory arthropods with below-ground taxa responding at large spatial extents. Our results underscore the need for multi-scale approaches to restoration. Additionally, our findings contribute to understanding the complex processes shaping multi-taxa diversity and offer insights for targeted conservation and restoration strategies.

ecology↗

bioRxiv2017

Interactions between ecological, evolutionary, and environmental processes unveil complex dynamics of island biodiversity

AimsUnderstanding how biodiversity emerges and varies in space and time is central to ecology and biogeography. Multiple processes affect biodiversity at different scales and organizational levels, hence progress in understanding biodiversity dynamics requires the integration of these underlying processes. Here we present BioGEEM (BioGeographical Eco-Evolutionary Model), a spatially-explicit, process-based model that integrates all processes hypothesized to be relevant for biodiversity dynamics and that can be used to evaluate their relative roles.\n\nLocationHypothetical oceanic islands\n\nMethodsThe model is stochastic, grid-based, and integrates ecological (metabolic constraints, demography, dispersal, and competition), evolutionary (mutation and speciation), and environmental (geo-climatic dynamics) processes. Plants on oceanic islands served as model system. We used the full model to test hypotheses about emergent patterns at different spatio-temporal scales and organizational levels (populations, species, communities, and assemblages), switching off processes to assess the importance 1) of competition for realistic population and range dynamics; 2) metabolic constraints for endemism and community composition; 3) environmental dynamics and 4) speciation for biogeographical patterns.\n\nResultsThe full model generated multiple patterns matching empirical and theoretical expectations. For example, populations were largest on young, species-poor islands. Species, particularly endemics, were better able to fill their potential range on small, species-poor islands. Richness gradients peaked at mid-elevations. The proportion of endemics was highest on old, large, and isolated environments within the islands. Species and trait richness showed unimodal temporal trends. Switching off selected processes affected these patterns, and we found most of our hypotheses supported.\n\nMain conclusionsIntegrating ecological, evolutionary, and environmental processes is essential to simultaneously generate realistic spatio-temporal dynamics at population, species, community, and assemblage level. Finally, large-scale biodiversity dynamics emerged directly from biological processes which make this mechanistic model a valuable virtual long-term field station to study the linkages between biogeography and ecology.

ecology↗

bioRxiv2017

Effects of time and isolation on plant diversity: testing island biogeography theory with an eco-evolutionary model

AimsThe General Dynamic Model of oceanic island biogeography (GDM) predicts how biogeographical rates, species richness, and endemism vary depending on island age, area, and isolation, based on the interplay of colonization, extinction, and speciation. Here, we used a simulation model to test whether GDM predictions may arise from individual- and population-level processes.\n\nLocationHypothetical hotspot islands.\n\nMethodsOur model (i) considers an idealized island ontogeny, (ii) metabolic constraints, and (iii) stochastic, spatially-explicit, and niche-based processes at the level of individuals and populations (plant demography, dispersal, competition, mutation, and speciation). Isolation scenarios involved varying dispersal ability and distances to mainland.\n\nResultsHumped temporal trends were obtained for species richness, endemic richness, proportion of cladogenetic endemic species, number of radiating lineages, number of species per radiating lineage, and biogeographical rates. The proportion of anagenetic endemics and of all endemics steadily increased over time. Extinction rates of endemic species peaked later than for non-endemic species. Species richness and the number of anagenetic endemics decreased with isolation as did rates of colonization, anagenesis, and extinction. The proportion of all endemics and of cladogenetic endemics, the number of cladogenetic endemics, of radiating lineages, and of species per radiating lineage, and the cladogenesis rate all increased with isolation.\n\nMain conclusionsThe results confirm most GDM predictions related to island ontogeny and isolation, but predict an increasing proportion of endemics throughout the experiment: a difference attributable to diverging assumptions on late island ontogeny. New insights regarding the extinction trends of endemics further demonstrate how simulation models focusing on low ecological levels provide tools to test biogeographical-scale predictions and to develop more detailed predictions for further empirical tests.

ecology↗