DOI: 10.5061/dryad.4qrfj6qjf
title: Data from: Individual energetics scale up to community coexistence: Movement, metabolism and biodiversity dynamics in fragmented landscapes

description: 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.
description: 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.
description: # Individual energetics scale up to community coexistence: Movement,
 metabolism and biodiversity dynamics in fragmented landscapes
 [https://doi.org/10.5061/dryad.4qrfj6qjf](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).