#DensityDependence
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jpecol.bsky.social
【🎉Latest accepted article】
Fine-scale #densitydependence in a #spatiallyheterogeneous plant population

#intraspecificcompetition | #ecologicalneighborhoods | #asymmetriccompetition | #sizeinequalities | #juvenilesurvival

@temple.edu @mapjournals.bsky.social

doi.org/10.1093/jpe/...
September 13, 2026 at 9:59 AM
oikosjournal.bsky.social
Female-biased spontaneous dispersal in Drosophila melanogaster and sex-specific effect of nutrition and density therein

https://doi.org/10.1111/oik.10920

#Drosophilamelanogaster #SexDifference #NutritionalDeprivation #SexRatio #DensityDependence
March 3, 2025 at 5:06 PM
plosbiology.org
Sublinear #DensityDependence is widely reported empirically, but is at odds with theory. @jamesaorr.bsky.social &co use a tightly controlled test in a microbial model system to reveal superlinear density dependence, so aligning empirical evidence with theory @plosbiology.org 🧪 plos.io/4vwAmEZ
Top: Schematic of the empirical application of growth-density inversion, where the classic density dependence perspective (blue) is inverted (green) to overcome the challenges of manipulating densities. The authors first parametrised a consumer-resource model using batch culture growth assays (Approach 1) and performed growth-density inversion analytically (Box 1). Then, in a model-free empirical implementation of growth-density inversion, they experimentally manipulated harvest rate and quantified densities at equilibrium by varying the dilution rate in a chemostat system (Approach 2). Bottom left: Parametrised Monod growth function with 100 posterior draws representing uncertainty. The inset shows the analytical prediction for the density dependence of this parametrised consumer–resource model. Bottom right: Inverse-logistic model fitted to the experimental data with 95% credible intervals. Inset shows the same data and model with axes inverted to give the classic density dependence perspective.
July 20, 2026 at 12:55 PM
plosbiology.org
Sublinear #DensityDependence is widely reported empirically, but is at odds with theory. @jamesaorr.bsky.social &co use a tightly controlled test in a microbial model system to reveal superlinear density dependence, so aligning empirical evidence with theory @plosbiology.org 🧪 plos.io/4vwAmEZ
Top: Schematic of the empirical application of growth-density inversion, where the classic density dependence perspective (blue) is inverted (green) to overcome the challenges of manipulating densities. The authors first parametrised a consumer-resource model using batch culture growth assays (Approach 1) and performed growth-density inversion analytically (Box 1). Then, in a model-free empirical implementation of growth-density inversion, they experimentally manipulated harvest rate and quantified densities at equilibrium by varying the dilution rate in a chemostat system (Approach 2). Bottom left: Parametrised Monod growth function with 100 posterior draws representing uncertainty. The inset shows the analytical prediction for the density dependence of this parametrised consumer–resource model. Bottom right: Inverse-logistic model fitted to the experimental data with 95% credible intervals. Inset shows the same data and model with axes inverted to give the classic density dependence perspective.
July 20, 2026 at 8:00 AM
plosbiology.org
Sublinear #DensityDependence is widely reported empirically, but is at odds with theory. @jamesaorr.bsky.social &co use a tightly controlled test in a microbial model system to reveal superlinear density dependence, so aligning empirical evidence with theory @plosbiology.org 🧪 plos.io/4vwAmEZ
Top: Schematic of the empirical application of growth-density inversion, where the classic density dependence perspective (blue) is inverted (green) to overcome the challenges of manipulating densities. The authors first parametrised a consumer-resource model using batch culture growth assays (Approach 1) and performed growth-density inversion analytically (Box 1). Then, in a model-free empirical implementation of growth-density inversion, they experimentally manipulated harvest rate and quantified densities at equilibrium by varying the dilution rate in a chemostat system (Approach 2). Bottom left: Parametrised Monod growth function with 100 posterior draws representing uncertainty. The inset shows the analytical prediction for the density dependence of this parametrised consumer–resource model. Bottom right: Inverse-logistic model fitted to the experimental data with 95% credible intervals. Inset shows the same data and model with axes inverted to give the classic density dependence perspective.
July 17, 2026 at 4:25 PM
niooknaw.bsky.social
New publication: Linking Individual Performance to Density-Dependent Population Dynamics to Understand Temperature-Mediated Genotype #Coexistence. #climatechange #densitydependence
doi.org/10.1111/ele....
Figure 1 in Bruijning et al. (2025): "Conceptual overview of our experiments and modelling framework to study competitive interactions."
September 26, 2025 at 11:56 AM
conservbytes.bsky.social
Our latest paper out in @Ecol_Evol

‘Logistic-growth models measuring density feedback are sensitive to population declines, but not fluctuating carrying capacity’

https://t.co/OpITJ3QgJ7

#densitydependence #populationdynamics #populationecology #ecology #ecologicalmodelling
November 20, 2024 at 5:15 AM
niooknaw.bsky.social
https://pure.knaw.nl/portal/en/publications/densitydependence-in-an-agestructured-population-of-great-tits-identifying-the-critical-age-classes(d3ade937-1f0e-4572-bce8-a95d5baf6bf4).html
Density dependence in an age-structured population of gre...
pure.knaw.nl
December 4, 2024 at 9:39 AM