scholarly journals A new dispersal-informed null model for community ecology shows strong performance

2016 ◽  
Author(s):  
Eliot Miller
Keyword(s):  
Community Structure ◽  
Competitive Exclusion ◽  
Null Model ◽  
Error Rates ◽  
Null Models ◽  
Type I ◽  
Habitat Filtering ◽  
Exclusion Processes ◽  
Phylogenetic Community Structure ◽  
Structure Metrics

AbstractNull models in ecology have been developed that, by maintaining some aspects of observed communities and repeatedly randomizing others, allow researchers to test for the action of community assembly processes like habitat filtering and competitive exclusion. Such processes are often detected using phylogenetic community structure metrics. When biologically significant elements, such as the number of species per assemblage, break down during randomizations, it can lead to high error rates. Realistic dispersal probabilities are often neglected during randomization, and existing models make the oftentimes empirically unreasonable assumption that all species are equally probable of dispersing to a given site. When this assumption is unwarranted, null models need to incorporate dispersal probabilities. I do so here, and present a dispersal null model (DNM) that strictly maintains species richness, and approximately maintains species occurrence frequencies and total abundance. I tested its statistical performance when used with a wide breadth of phylogenetic community structure metrics across 3,000 simulated communities assembled according to neutral, habitat filtering, and competitive exclusion processes. The DNM performed well, exhibiting low error rates (both type I and II). I also implemented it in a re-analysis of a large empirical dataset, an abundance matrix of 696 sites and 75 species of Australian Meliphagidae. Although the overall signal from that study remained unchanged, it showed that statistically significant phylogenetic clustering could have been an artifact of dispersal limitations.

scholarly journals Phylogenetic community structure metrics and null models: a review with new methods and software

2015 ◽  
Author(s):  
Eliot T Miller ◽  
Damien R Farine ◽  
Christopher H Trisos
Keyword(s):  
Community Structure ◽  
Community Assembly ◽  
Competitive Exclusion ◽  
Null Model ◽  
Error Rates ◽  
Null Models ◽  
Type I ◽  
Habitat Filtering ◽  
Phylogenetic Community Structure ◽  
Structure Metrics

Competitive exclusion and habitat filtering are believed to have an important influence on the assembly of ecological communities, but ecologists and evolutionary biologists have not reached a consensus on how to quantify patterns that would reveal the action of these processes. No fewer than 22 phylogenetic community structure metrics and nine null models can be combined, providing 198 approaches to test for such patterns. Choosing statistically appropriate approaches is currently a daunting task. First, given random community assembly, we assessed similarities among metrics and among null models in their behavior across communities varying in species richness. Second, we developed spatially explicit, individual-based simulations where communities were assembled either at random, by competitive exclusion or by habitat filtering. Third, we quantified the performance (type I and II error rates) of all 198 approaches against each of the three assembly processes. Many metrics and null models are functionally equivalent, more than halving the number of unique approaches. Moreover, an even smaller subset of metric and null model combinations is suitable for testing community assembly patterns. Metrics like mean pairwise phylogenetic distance and phylogenetic diversity were better able to detect simulated community assembly patterns than metrics like phylogenetic abundance evenness. A null model that simulates regional dispersal pressure on the community of interest outperformed all others. We introduce a flexible new R package, metricTester, to facilitate robust analyses of method performance. The package is programmed in parallel to readily accommodate integration of new row-wise matrix calculations (metrics) and matrix-wise randomizations (null models) to generate expectations and quantify error rates of proposed methods.

scholarly journals Phylogenetic community structure metrics and null models: a review with new methods and software

Ecography ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 461-477 ◽  
Author(s):  
Eliot T. Miller ◽  
Damien R. Farine ◽  
Christopher H. Trisos
Keyword(s):  
Community Structure ◽  
Null Models ◽  
Phylogenetic Community Structure ◽  
New Methods ◽  
Structure Metrics

scholarly journals A new dynamic null model for phylogenetic community structure

2015 ◽  
Vol 18 (2) ◽  
pp. 153-163 ◽  
Author(s):  
Alex L. Pigot ◽  
Rampal S. Etienne
Keyword(s):  
Community Structure ◽  
Null Model ◽  
Phylogenetic Community Structure

scholarly journals Non-random seasonal variation in the structure of a Mediterranean snake community

Web Ecology ◽  
2007 ◽  
Vol 7 (1) ◽  
pp. 40-46
Author(s):  
E. Filippi ◽  
L. Luiselli
Keyword(s):  
Monte Carlo ◽  
Community Structure ◽  
Seasonal Variation ◽  
Central Italy ◽  
Habitat Type ◽  
Null Model ◽  
Niche Overlap ◽  
Null Models ◽  
Habitat Niche ◽  
Mediterranean Community

Abstract. The community structure in relation to habitat type was studied in a Mediterranean community of snakes from Canale Monterano, central Italy. Habitat data for snakes were analysed both overall and divided by season, i.e. spring (April–June) and summer (July–September). Community analyses were performed using null models (RA2 and RA3 algorithms) and Monte Carlo simulations on habitat niche overlap estimates. Null models suggested that the community was assembled non-randomly (according to RA2 but not RA3), indicating that the generalist-specialist nature (the number of resource states, but not necessarily the types) used by each species in the assemblage reduced ecological similarity. Similar results were reached also performing the same null model procedures on the spring datasets, whereas no structure emerged during summer either by RA2 or RA3 algorithms. In general, this study suggests that the community structure of snakes during spring may be shaped by the different eco-physiological needs of the various species (probably, the differential tolerance to cold and the consequent needs of finding suitable hibernacula), whereas the lack of structure during summer may be caused by the between-species similar foraging needs.

Relatedness and the composition of communities over time: Evaluating phylogenetic community structure in the late Cenozoic record of bivalves

Paleobiology ◽  
2020 ◽  
pp. 1-13
Author(s):  
Lucy M. Chang ◽  
Phillip L. Skipwith
Keyword(s):  
Community Structure ◽  
Competitive Exclusion ◽  
Critical Role ◽  
Environmental Filtering ◽  
Influential Factors ◽  
Phylogenetic Distance ◽  
Late Cenozoic ◽  
Phylogenetic Community Structure ◽  
Historical Perspectives ◽  
Over Time

Abstract Understanding the mechanisms that prevent or promote the coexistence of taxa at local scales is critical to understanding how biodiversity is maintained. Competitive exclusion and environmental filtering are two processes thought to limit which taxa become established in a community. However, determining the relative importance of the two processes is a complex task, especially when the critical initial stages of colonization cannot be directly observed. Here, we explore the use of phylogenetic community structure for identifying filtering mechanisms in a fossil community. We integrated a time-calibrated molecular phylogeny of bivalve genera with a spatial dataset of late Cenozoic bivalves from the Pacific coast of North America to characterize how the community that was present in the semirestricted San Joaquin Basin (SJB) embayment of present-day California was phylogenetically structured. We employed phylogenetic distance-based metrics across six time bins spanning 27–2.5 Ma and found no evidence of significant clustering or evenness in the SJB community when compared with communities randomly assembled from the regional source pool. Additionally, we found that new colonizers into the SJB were not significantly more or less closely related to native taxa than expected by chance. These findings suggest that neither competitive exclusion nor environmental filtering were overwhelmingly influential factors shaping the composition of the SJB community over time. We further discuss interpretations of these patterns in light of current understandings in community phylogenetics and reiterate the critical role historical perspectives play in how community assembly rules are assessed.

scholarly journals Null models for community dynamics: Beware of the cyclic shift algorithm

2019 ◽  
Author(s):  
Michael Kalyuzhny
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Null Model ◽  
Temporal Patterns ◽  
Null Models ◽  
Type I ◽  
Cyclic Shift ◽  
Initial State ◽  
Temporal Autocorrelation ◽  
Compositional Changes

AbstractAimTemporal patterns of community dynamics are drawing increasing interest due to their potential to shed light on assembly processes and anthropogenic effects. However, interpreting such patterns considerably benefits from comparing observed dynamics to the reference of a null model. For that aim, the cyclic shift permutations algorithm, which generates randomized null communities based on empirically observed time series, has recently been proposed. The use of this algorithm, which shifts each species time series randomly in time, has been justified by the claim that it preserves the temporal autocorrelation of single species. Hence it has been used to test the significance of various community patterns, in particular excessive compositional changes, biodiversity trends and community stability.InnovationHere we critically study the properties of the cyclic shift algorithm for the first time. We show that, unlike previously suggested, this algorithm does not preserve temporal autocorrelation due to the need to “wrap” the time series and assign the last observations to the first years. Moreover, this algorithm scrambles the initial state of the community, making any dynamics that results from deviations from equilibrium seem excessive. We exemplify that these two issues lead to a highly elevated type I error rate in tests for excessive compositional changes and richness trends.ConclusionsCaution is needed when using the cyclic shift permutation algorithm and interpreting results obtained using it. Interpretation is further complicated because the algorithm removes all correlations between species. We suggest guidelines for using this method and discuss several possible alternative approaches. More research is needed on the best practices for using null models for temporal patterns.

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