scholarly journals The prevalence and impact of transient species in ecological communities

2017 ◽  
Author(s):  
Sara Snell ◽  
Brian S. Evans ◽  
Ethan P. White ◽  
Allen H. Hurlbert
Keyword(s):  
Spatial Scales ◽  
Species Abundance ◽  
Careful Consideration ◽  
Ecological Communities ◽  
Transient Species ◽  
Ecological Patterns ◽  
Species Abundance Distributions ◽  
Species Area ◽  
Using Data ◽  
Taxonomic Groups

AbstractTransient species occur infrequently in a community over time and do not maintain viable local populations. Because transient species interact differently than non-transients with their biotic and abiotic environment, it is important to characterize the prevalence of these species and how they impact our understanding of ecological systems. We quantified the prevalence and impact of transient species in communities using data on over 17,000 community time series spanning an array of ecosystems, taxonomic groups, and spatial scales. We found that transient species are a general feature of communities regardless of taxa or ecosystem. The proportion of these species decreases with spatial scale leading to a need to control for scale in comparative work. Removing transient species from analyses influences the form of a suite of commonly studied ecological patterns including species-abundance distributions, species-energy relationships, species-area relationships, and temporal turnover. Careful consideration should be given to whether transient species are included in analyses depending on the theoretical and practical relevance of these species for the question being studied.

scholarly journals A new method to analyze species abundances in space using generalized dimensions

2015 ◽  
Author(s):  
Leonardo A Saravia
Keyword(s):  
Species Abundance ◽  
Abundance Distribution ◽  
Community Patterns ◽  
Information Dimension ◽  
Generalized Dimension ◽  
Species Abundances ◽  
Species Abundance Distributions ◽  
Species Area ◽  
Generalized Dimensions ◽  
Relationship Of

Species-area relationships (SAR) and species abundance distributions (SAD) are among the most studied patterns in ecology, due to their application to both theoretical and conservation issues. One problem with these general patterns is that different theories can generate the same predictions, and for this reason they cannot be used to detect different mechanisms of community assembly. A solution is to search for more sensitive patterns, for example by extending the SAR to the whole species abundance distribution. A generalized dimension ($D_q$) approach has been proposed to study the scaling of SAD, but to date there has been no evaluation of the ability of this pattern to detect different mechanisms. An equivalent way to express SAD is the rank abundance distribution (RAD). Here I introduce a new way to study SAD scaling using a spatial version of RAD: the species-rank surface (SRS), which can be analyzed using $D_q$. Thus there is an old $D_q$ based on SAR ($D_q^{SAD}$), and a new one based on SRS ($D_q^{SRS}$). I perform spatial simulations to examine the relationship of $D_q$ with SAD, spatial patterns and number of species. Finally I compare the power of both $D_q$, SAD, SAR exponent, and the fractal information dimension to detect different community patterns using a continuum of hierarchical and neutral spatially explicit models. The SAD, $D_q^{SAD}$ and $D_q^{SRS}$ all had good performance in detecting models with contrasting mechanisms. $D_q^{SRS}$, however, had a better fit to data and allowed comparisons between hierarchical communities where the other methods failed. The SAR exponent and information dimension had low power and should not be used. SRS and $D_q^{SRS}$ could be interesting methods to study community or macroecological patterns.

scholarly journals The concerted emergence of well-known spatial and temporal ecological patterns in an evolutionary food web model in space

2019 ◽  
Author(s):  
Michaela Hamm ◽  
Barbara Drossel
Keyword(s):  
Food Web ◽  
Trophic Position ◽  
Species Abundance ◽  
Trophic Levels ◽  
Geographic Ranges ◽  
Space And Time ◽  
Spatially Extended ◽  
Ecological Patterns ◽  
Species Abundance Distributions ◽  
Food Web Model

ABSTRACTEcological systems show a variety of characteristic patterns of biodiversity in space and time. It is a challenge for theory to find models that can reproduce and explain the observed patterns. Since the advent of island biogeography these models revolve around speciation, dispersal, and extinction, but they usually neglect trophic structure. Here, we propose and study a spatially extended evolutionary food web model that allows us to study large spatial systems with several trophic layers. Our computer simulations show that the model gives rise simultaneously to several biodiversity patterns in space and time, from species abundance distributions to the waxing and waning of geographic ranges. We find that trophic position in the network plays a crucial role when it comes to the time evolution of range sizes, because the trophic context restricts the occurrence and survival of species especially on higher trophic levels.

scholarly journals A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

2020 ◽  
Author(s):  
Isaac Overcast ◽  
Megan Ruffley ◽  
James Rosindell ◽  
Luke Harmon ◽  
Paulo A. V. Borges ◽  
...  
Keyword(s):  
Community Assembly ◽  
Island Biogeography ◽  
Spatial Scales ◽  
Mechanistic Model ◽  
Species Abundance ◽  
Real Data ◽  
Current Theory ◽  
Supervised Machine Learning ◽  
Ecological Communities ◽  
Trait Variation

AbstractBiodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Reconciling the relative importance of these processes is hindered by current theory, which tends to focus on a single spatial, temporal or taxonomic scale. We introduce a mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: i) species richness and abundances; ii) population genetic diversities; and iii) trait variation in a phylogenetic context. We demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. We combine our massive eco-evolutionary synthesis simulations (MESS) with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of spatial scales.

scholarly journals Inferring macro-ecological patterns from local species' occurrences

2018 ◽  
Author(s):  
Anna Tovo ◽  
Marco Formentin ◽  
Samir Suweis ◽  
Samuele Stivanello ◽  
Sandro Azaele ◽  
...  
Keyword(s):  
Species Richness ◽  
Negative Binomial ◽  
Spatial Scales ◽  
Species Abundance ◽  
Analytical Framework ◽  
Invariance Property ◽  
Practical Reasons ◽  
Conservation Practice ◽  
Unique Framework ◽  
Using Data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework to infer species richness and abundances at large spatial scales in biodiversity-rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale-invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well-known biodiversity patterns of ecological theory, such as the Species Accumulation Curve (SAC) and the Relative Species Abundance (RSA) as well as a new emergent pattern, which is the Relative Species Occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico-generated and real forests. We demonstrate the accuracy of our predictions using data from two well-studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence-absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.

scholarly journals The Dynamic Hypercube as a Niche Community Model

2021 ◽  
Vol 9 ◽  
Author(s):  
John M. Halley ◽  
Stuart L. Pimm
Keyword(s):  
Community Dynamics ◽  
Neutral Theory ◽  
Species Abundance ◽  
Ecological Communities ◽  
Evolutionary Time ◽  
Community Model ◽  
Theory Of Island Biogeography ◽  
Species Abundance Distributions ◽  
Hypercube Model ◽  
Zero Sum

Different models of community dynamics, such as the MacArthur–Wilson theory of island biogeography and Hubbell’s neutral theory, have given us useful insights into the workings of ecological communities. Here, we develop the niche-hypervolume concept of the community into a powerful model of community dynamics. We describe the community’s size through the volume of the hypercube and the dynamics of the populations in it through the fluctuations of the axes of the niche hypercube on different timescales. While the community’s size remains constant, the relative volumes of the niches within it change continuously, thus allowing the populations of different species to rise and fall in a zero-sum fashion. This dynamic hypercube model reproduces several key patterns in communities: lognormal species abundance distributions, 1/f-noise population abundance, multiscale patterns of extinction debt and logarithmic species-time curves. It also provides a powerful framework to explore significant ideas in ecology, such as the drift of ecological communities into evolutionary time.

The Ecology of Echo

1997 ◽  
Vol 3 (3) ◽  
pp. 165-190 ◽  
Author(s):  
Peter T. Hraber ◽  
Terry Jones ◽  
Stephanie Forrest
Keyword(s):  
Significant Contribution ◽  
Species Abundance ◽  
Ecosystem Model ◽  
Ecological Diversity ◽  
Good Qualitative Agreement ◽  
Simulation Experiments ◽  
Naturally Occurring ◽  
Resource Limited ◽  
Species Abundance Distributions ◽  
Species Area

Echo is a generic ecosystem model in which evolving agents are situated in a resource-limited environment. The Echo model is described, and the behavior of Echo is evaluated on two well-studied measures of ecological diversity: relative species abundance and the species-area scaling relation. In simulation experiments, these measures are used to compare the behavior of Echo with that of a neutral model, in which selection on agent genotypes is random. These simulations show that the evolutionary component of Echo makes a significant contribution to its behavior and that Echo shows good qualitative agreement with naturally occurring species abundance distributions and species-area scaling relations.

scholarly journals Efficient algorithms for sampling feasible sets of abundance distributions

2014 ◽  
Author(s):  
Kenneth J. Locey ◽  
Daniel J. McGlinn
Keyword(s):  
Species Abundance ◽  
Discrete Distributions ◽  
Ecological Communities ◽  
Feasible Set ◽  
As Species ◽  
Random Samples ◽  
Ecological Patterns ◽  
Feasible Sets ◽  
Sampling Algorithms ◽  
Zero Values

Ecological variables such as species richness (S) and total abundance (N) can strongly influence ecological patterns. For example, the general form of the species abundance distribution (SAD) can often be explained by the majority of possible forms having the same N and S, i.e. the SAD feasible set. The feasible set reveals how variables determine observable variation, whether empirical patterns are exceptional to the majority of possible forms, and provides a constraint-based explanation for the ubiquity of hollow-curve SADs in nature. However, use of the feasible set has been limited to inefficient sampling algorithms that prevent large ecological communities and ecologically realistic combinations of N and S from being examined. This is the primary hindrance to using this otherwise novel perspective and theoretical framework. We developed efficient computational algorithms to generate random samples of the feasible set for the SAD and similar discrete distributions of abundance, including those that allow for zero-values, e.g., absences. We provide Python and R based implementations of our algorithms and tools for testing and using them. Our algorithms are often several orders of magnitude faster than a long-standing and recently used approach. This greatly increases the size and diversity of communities that can be examined with the feasible set approach and thus advances progress using constraint-based approaches to decipher ecological patterns.

scholarly journals A new method to analyze species abundances in space using generalized dimensions

2014 ◽  
Author(s):  
Leonardo A Saravia
Keyword(s):  
Species Abundance ◽  
Abundance Distribution ◽  
Community Patterns ◽  
Information Dimension ◽  
Spatially Explicit Models ◽  
Generalized Dimension ◽  
Species Abundances ◽  
Species Abundance Distributions ◽  
Species Area ◽  
Generalized Dimensions

Species-area relationships (SAR) and species abundance distributions (SAD) are among the most studied patterns in ecology, due to their application in both theoretical and conservation issues. One problem with these general patterns is that different theories can generate the same predictions, and for this reason they can not be used to detect different mechanisms. A solution for this is to search for more sensitive patterns. One possibility is to extend the SAR to the whole species abundance distribution. A generalized dimension (\(D_q\)) approach has been proposed to study the scaling of SAD, but there has been no evaluation of the ability of this pattern to detect different mechanisms. An equivalent way to express SAD is the rank abundance distribution (RAD). Here I introduce a new way to study scaling of SAD using a spatial version of RAD: the species-rank surface (SRS), which can be analyzed using \(D_q\). Thus there is an old \(D_q\) based on SAR (\(D_q^{SAD}\)), and a new one based on SRS (\(D_q^{SRS}\)). I perform spatial simulations to relate both \(D_q\) with SAD, spatial patterns and number of species. Finally I compare the power of both \(D_q\), SAD, SAR exponent, and the fractal information dimension to detect different community patterns using a continuum of hierarchical and neutral spatially explicit models. The SAD, \(D_q^{SAD}\) and \(D_q^{SRS}\) all had good performance in detecting models with contrasting mechanisms. \(D_q^{SRS}\) had a better fit to data and a strong ability to compare between hierarchical communities where the other methods failed. The SAR exponent and information dimension had low power and should not be used. SRS and \(D_q^{SRS}\) could be an interesting addition to study community or macroecological patterns.

scholarly journals A new method to analyze species abundances in space using generalized dimensions

2014 ◽  
Author(s):  
Leonardo A Saravia
Keyword(s):  
Species Abundance ◽  
Abundance Distribution ◽  
Community Patterns ◽  
Information Dimension ◽  
Spatially Explicit Models ◽  
Generalized Dimension ◽  
Species Abundances ◽  
Species Abundance Distributions ◽  
Species Area ◽  
Generalized Dimensions

Species-area relationships (SAR) and species abundance distributions (SAD) are among the most studied patterns in ecology, due to their application in both theoretical and conservation issues. One problem with these general patterns is that different theories can generate the same predictions, and for this reason they can not be used to detect different mechanisms. A solution for this is to search for more sensitive patterns. One possibility is to extend the SAR to the whole species abundance distribution. A generalized dimension ($D_q$) approach has been proposed to study the scaling of SAD, but there has been no evaluation of the ability of this pattern to detect different mechanisms. An equivalent way to express SAD is the rank abundance distribution (RAD). Here I introduce a new way to study scaling of SAD using a spatial version of RAD: the species-rank surface (SRS), which can be analyzed using $D_q$. Thus there is an old $D_q$ based on SAR ($D_q^{SAD}$), and a new one based on SRS ($D_q^{SRS}$). I perform spatial simulations to relate both $D_q$ with SAD, spatial patterns and number of species. Finally I compare the power of both $D_q$, SAD, SAR exponent, and the fractal information dimension to detect different community patterns using a continuum of hierarchical and neutral spatially explicit models. The SAD, $D_q^{SAD}$ and $D_q^{SRS}$ all had good performance in detecting models with contrasting mechanisms. $D_q^{SRS}$ had a better fit to data and a strong ability to compare between hierarchical communities where the other methods failed. The SAR exponent and information dimension had low power and should not be used. SRS and $D_q^{SRS}$ could be an interesting addition to study community or macroecological patterns.

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