Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
Data files
Jun 03, 2024 version files 12.23 MB
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manuscript_fragmentation_community.csv
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manuscript_fragmentation_single.csv
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README.md
Abstract
Unraveling the intricate mechanisms that govern community coexistence remains a daunting challenge, particularly amidst ongoing environmental change. To understand the response of individual animals to environmental change, physiology and individual metabolism are often studied. However, this perspective is currently largely lacking in community ecology. We argue that the integration of individual metabolism into community theory can offer new insights into coexistence. We present the first individual-based metabolic community model for a terrestrial mammal community to simulate energy dynamics and home range behavior in different environments. Using this model, we investigate how ecologically similar species coexist and maintain their energy balance under food competition. Only if individuals of different species are able to balance their incoming and outgoing energy over the long-term will they be able to coexist. After thoroughly testing and validating the model against real-world patterns such as of home range dynamics and field metabolic rates, we applied it as a case study to scenarios of habitat fragmentation - a widely discussed topic in biodiversity research. First, comparing single-species simulations with community simulations, we find that the effect of habitat fragmentation on populations is strongly context-dependent. While populations of species living alone in the landscape were mostly positively affected by fragmentation, the diversity of a community of species was highest under medium fragmentation scenarios. Under medium fragmentation, energy balance and reproductive investment were also most similar among species. We therefore suggest that similarity in energy balance among species promotes coexistence. We argue that energetics should be part of community ecology theory, as the relative energetic status and reproductive investment can reveal why and under what environmental conditions coexistence is likely to occur. As a result, landscapes can potentially be protected and designed to maximize coexistence. The metabolic community model presented here can be a promising tool to investigate other scenarios of environmental change or other species communities to further disentangle global change effects and preserve biodiversity.
README: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes
https://doi.org/10.5061/dryad.4qrfj6qjf
We here provide the model code for our individual-based metabolic community model along with the resulting data for the simulated experiments and the code for producing the manuscript figures
Description of the data and file structure
For a detailed description of the functioning of the simulation model, please refer to our TRACE document included with the publication. The model is provided as a Netlogo program (mibcom_paper.nlogo) and a Python program (mibcom_mesa.py). We used the model to first simulate the population of different species (characterized by their body mass, defined by "specs-included") alone in landscapes of differently fragmented habitats, and finally we simulated the community of species together in differently fragmented habitats (fragmentation defined by "clump"). Simulation results are provided separately for single species simulations (manuscript_fragmentation_single.csv) and for community simulations (manuscript_fragmentation_community.csv). Output variables are population size over time (number), mean field metabolic rate (fmr), mean relative basal metabolic costs (basal), mean relative locomotion costs (loco), mean relative reproduction costs (repro), mean relative growth costs (grow), mean relative digestion costs (digest), mean energy intake (in), and mean lifetime reproductive success (rep_success_hr) for each species (0-9). The data was used for the figures in the manuscript. The code for the figures is also provided (Manuscript_Figures.ipynb).
Code/Software
We provide the metabolic individual-based model for a mammal community as a Netlogo program (mibcom_paper.nlogo) and a Python program (mibcom_mesa.py). For development, we used NetLogo version 6.1.0, and Python version 3.8. See the publication for a detailed model description (ODD) and full documentation of the modelling (TRACE). We used the NetLogo model for the main study, but programmed the same model in the second programming language Python to verify implementation. The Python version has less functionality, especially in output options. For scenario simulation and output in the NetLogo model, see the BehaviorSpace included in the Tools section of the model. Both models produce similar results. Additionally, we provide the Python notebook for the figures in the manuscript (Manuscript_Figures.ipynb).
Methods
In this study, we developed a novel dynamic individual-based metabolic simulation model for a mammal community. The model is based on allometric relationships and movement in home ranges, allowing for a variety of species. The model was thoroughly tested and validated using real-world patterns from the literature. An extensive model development description is available in the format of a TRACE document with the publication. We used the model to simulate scenarios of different habitat fragmentation and species presence (single species or community) and analyzed the resulting data.