scholarly journals Spatial patterns reveal negative density dependence and habitat associations in tropical trees

Ecology ◽  
2011 ◽  
Vol 92 (9) ◽  
pp. 1723-1729 ◽  
Author(s):  
Robert Bagchi ◽  
Peter A Henrys ◽  
Patrick E Brown ◽  
David F. R. P Burslem ◽  
Peter J Diggle ◽  
...  
Keyword(s):  
Density Dependence ◽  
Spatial Patterns ◽  
Tropical Trees ◽  
Habitat Associations ◽  
Negative Density Dependence

scholarly journals Relating the strength of density dependence and the spatial distribution of individuals

2019 ◽  
Author(s):  
Micah Brush ◽  
John Harte
Keyword(s):  
Density Dependence ◽  
Spatial Patterns ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Individual Species ◽  
Spatial Patterning ◽  
Random Placement ◽  
Negative Density Dependence ◽  
Underlying Mechanisms ◽  
Spatially Explicit Data

ABSTRACTSpatial patterns in ecology contain useful information about underlying mechanisms and processes. Although there are many summary statistics used to quantify these spatial patterns, there are far fewer models that directly link explicit ecological mechanisms to observed patterns easily derived from available data. We present a model of intraspecific spatial aggregation that quantitatively relates static spatial patterning to negative density dependence. Individuals are placed according to the colonization rule consistent with the Maximum Entropy Theory of Ecology (METE), and die with probability proportional to their abundance raised to a power α, a parameter indicating the degree of density dependence. Our model shows quantitatively and generally that increasing density dependence randomizes spatial patterning. α = 1 recovers the strongly aggregated METE distribution that is consistent with many ecosystems empirically, and as α → 2 our prediction approaches the binomial distribution consistent with random placement. In between our model predicts less aggregation than METE, but more than random placement. We additionally relate our mechanistic parameter α to the statistical aggregation parameter k in the negative binomial distribution, giving it an ecological interpretation in the context of density dependence. We use our model to analyze two contrasting datasets, a 50 ha tropical forest and a 64 m2 serpentine grassland plot. For each dataset, we infer α for individual species as well as a community α parameter. We find that α is generally larger in the tightly packed forest than the sparse grassland, and the degree of density dependence increases at smaller scales. These results are consistent with current understanding in both ecosystems, and we infer this underlying density dependence using only empirical spatial patterns. Our model can easily be applied to other datasets where spatially explicit data are available.

scholarly journals Interspecific Functional Convergence and Divergence and Intraspecific Negative Density Dependence Underlie the Seed-to-Seedling Transition in Tropical Trees

2016 ◽  
Vol 187 (1) ◽  
pp. 99-109 ◽  
Author(s):  
Maria Natalia Umaña ◽  
Jimena Forero-Montaña ◽  
Robert Muscarella ◽  
Christopher J. Nytch ◽  
Jill Thompson ◽  
...  
Keyword(s):  
Density Dependence ◽  
Tropical Trees ◽  
Functional Convergence ◽  
Negative Density Dependence ◽  
Convergence And Divergence

scholarly journals Relating the Strength of Density Dependence and the Spatial Distribution of Individuals

2021 ◽  
Vol 9 ◽  
Author(s):  
Micah Brush ◽  
John Harte
Keyword(s):  
Density Dependence ◽  
Spatial Patterns ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Individual Species ◽  
Spatial Patterning ◽  
Random Placement ◽  
Negative Density Dependence ◽  
Underlying Mechanisms ◽  
Spatially Explicit Data

Spatial patterns in ecology contain useful information about underlying mechanisms and processes. Although there are many summary statistics used to quantify these spatial patterns, there are far fewer models that directly link explicit ecological mechanisms to observed patterns easily derived from available data. We present a model of intraspecific spatial aggregation that quantitatively relates static spatial patterning to negative density dependence. Individuals are placed according to the colonization rule consistent with the Maximum Entropy Theory of Ecology (METE), and die with probability proportional to their abundance raised to a power α, a parameter indicating the degree of density dependence. This model can therefore be interpreted as a hybridization of MaxEnt and mechanism. Our model shows quantitatively and generally that increasing density dependence randomizes spatial patterning. α = 1 recovers the strongly aggregated METE distribution that is consistent with many ecosystems empirically, and as α → 2 our prediction approaches the binomial distribution consistent with random placement. For 1 < α < 2, our model predicts more aggregation than random placement but less than METE. We additionally relate our mechanistic parameter α to the statistical aggregation parameter k in the negative binomial distribution, giving it an ecological interpretation in the context of density dependence. We use our model to analyze two contrasting datasets, a 50 ha tropical forest and a 64 m2 serpentine grassland plot. For each dataset, we infer α for individual species as well as a community α parameter. We find that α is generally larger in the tightly packed forest than the sparse grassland, and the degree of density dependence increases at smaller scales. These results are consistent with current understanding in both ecosystems, and we infer this underlying density dependence using only empirical spatial patterns. Our model can easily be applied to other datasets where spatially explicit data are available.

scholarly journals Conspecific negative density dependence and why its study should not be abandoned

Ecosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph A. LaManna ◽  
Scott A. Mangan ◽  
Jonathan A. Myers
Keyword(s):  
Density Dependence ◽  
Negative Density Dependence

scholarly journals Conspecific negative density dependence and why its study should not be abandoned

2020 ◽  
Author(s):  
Joseph A. LaManna ◽  
Scott A. Mangan ◽  
Jonathan A. Myers
Keyword(s):  
Density Dependence ◽  
Latitudinal Gradient ◽  
Null Model ◽  
Statistical Bias ◽  
Fitness Components ◽  
Density Dependent ◽  
Statistical Errors ◽  
Negative Density Dependence ◽  
Model Approach

AbstractRecent studies showing bias in the measurement of density dependence have the potential to sow confusion in the field of ecology. We provide clarity by elucidating key conceptual and statistical errors with the null-model approach used in Detto et al. (2019). We show that neither their null model nor a more biologically-appropriate null model reproduces differences in density-dependent recruitment between forests, indicating that the latitudinal gradient in negative density dependence is not an artefact of statistical bias. Finally, we suggest a path forward that combines observational comparisons of density dependence in multiple fitness components across localities with mechanistic and geographically-replicated experiments.

scholarly journals Are we missing the forest for the trees? Conspecific negative density dependence in a temperate deciduous forest

2021 ◽  
Author(s):  
Kathryn E. Barry ◽  
Stefan A. Schnitzer
Keyword(s):  
Life History ◽  
Density Dependence ◽  
Plant Community ◽  
Plant Diversity ◽  
Temperate Forest ◽  
Woody Plant ◽  
Community Level ◽  
Life History Strategies ◽  
Canopy Trees ◽  
Negative Density Dependence

AbstractOne of the central goals of ecology is to determine the mechanisms that enable coexistence among species. Evidence is accruing that conspecific negative density dependence (CNDD), the process by which plant seedlings are unable to survive in the area surrounding adults of their same species, is a major contributor to tree species coexistence. However, for CNDD to maintain diversity, three conditions must be met. First, CNDD must maintain diversity for the majority of the woody plant community (rather than merely specific groups). Second, the pattern of repelled recruitment must increase in with plant size. Third, CNDD must occurs across life history strategies and not be restricted to a single life history strategy. These three conditions are rarely tested simultaneously. In this study, we simultaneously test all three conditions in a woody plant community in a North American temperate forest. We examined whether the different woody plant growth forms (shrubs, understory trees, mid-story trees, canopy trees, and lianas) at different ontogenetic stages (seedling, sapling, and adult) were overdispersed – a spatial pattern indicative of CNDD – using spatial point pattern analysis across life history stages and strategies. We found that there was a strong signal of overdispersal at the community level. However, this pattern was driven by adult canopy trees. By contrast, understory plants, which can constitute up to 80% of temperate forest plant diversity, were not overdispersed as adults. The lack of overdispersal suggests that CNDD is unlikely to be a major mechanism maintaining understory plant diversity. The focus on trees for the vast majority of CNDD studies may have biased the perception of the prevalence of CNDD as a dominant mechanism that maintains community-level diversity when, according to our data, CNDD may be restricted largely to trees.

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