scholarly journals The Subhabitat Dependence of Biogeographic Pattern

2020 ◽  
Vol 8 ◽  
Author(s):  
Ricardo A. Scrosati ◽  
Matthew J. Freeman ◽  
Julius A. Ellrich
Keyword(s):  
Intertidal Zone ◽  
Abiotic Factors ◽  
Species Abundance ◽  
Biological Surveys ◽  
Biogeographic Patterns ◽  
Abundance Data ◽  
Biogeographic Pattern ◽  
Sessile Invertebrates ◽  
High Intertidal Zone ◽  
Species Abundance Data

We introduce and test the subhabitat dependence hypothesis (SDH) in biogeography. This hypothesis posits that biogeographic pattern within a region differs when determined with species abundance data from different subhabitat types. It stems from the notion that the main abiotic factors that drive species distribution in different subhabitat types across a biogeographic region often vary differently across space. To test the SDH, we measured the abundance of algae and sessile invertebrates in two different subhabitats (high intertidal zone and mid-intertidal zone) at eight locations along the Atlantic Canadian coast. We conducted multivariate analyses of the species abundance data to compare alongshore biogeographic pattern between both zones. For both subhabitat types, location groupings based on community similarity not always responded to geographic proximity, leading to biogeographic patchiness to some extent. Nonetheless, both biogeographic patterns were statistically unrelated, thus supporting the SDH. This lack of concordance was most evident for southern locations, which clustered together based on high-intertidal data but showed considerable alongshore patchiness based on mid-intertidal data. We also found that the ordination pattern of these eight locations based on sea surface temperature data was significantly related to biogeographic pattern for the mid-intertidal zone but not for the high intertidal zone. This finding supports the rationale behind the SDH due to the longer periods of submergence experienced by the mid-intertidal zone. Overall, we conclude that biogeographic pattern within a region can depend on the surveyed subhabitat type. Thus, biological surveys restricted to specific subhabitats may not properly reveal biogeographic pattern for a biota as a whole or even just for other subhabitats. As many studies generate biogeographic information with data only for specific subhabitats, we recommend testing the SDH in other systems to determine its domain of application.

scholarly journals Measuring β-diversity with species abundance data

2015 ◽  
Vol 84 (4) ◽  
pp. 1112-1122 ◽  
Author(s):  
Louise J. Barwell ◽  
Nick J. B. Isaac ◽  
William E. Kunin
Keyword(s):  
Species Abundance ◽  
Abundance Data ◽  
Β Diversity ◽  
Species Abundance Data

scholarly journals NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data

2021 ◽  
Author(s):  
Samantha J Gleich ◽  
Jacob A Cram ◽  
Jake L Weissman ◽  
David A Caron
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Species Abundance ◽  
Covariance Structure ◽  
Additive Models ◽  
Series Data ◽  
Ecological Network ◽  
Network Analyses ◽  
Abundance Data ◽  
Species Abundance Data

Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets with and without our GAM transformation, and comparing the network outputs to the known covariance structure of the simulated data. The results revealed that seasonal abundance patterns substantially decreased the accuracy of the inferred networks. Additionally, the GAM transformation increased the F1 score of inferred ecological networks on average and improved the ability of network inference methods to capture important features of network structure. This study underscores the importance of considering temporal features when carrying out network analyses and describes a simple, effective tool that can be used to improve results.

Variation in mussel and barnacle recruitment parallels a shift in intertidal community structure in the Cook Strait region of New Zealand

2011 ◽  
Vol 62 (10) ◽  
pp. 1221 ◽  
Author(s):  
Rahul Demello ◽  
Nicole E. Phillips
Keyword(s):  
New Zealand ◽  
Intertidal Zone ◽  
Spatial Scales ◽  
South Coast ◽  
The South ◽  
Sessile Invertebrates ◽  
High Zone ◽  
Species Specific ◽  
High Intertidal Zone

Recruitment influences populations and communities of marine organisms to varying degrees and across a range of spatial scales. We hypothesised that recruitment plays a role in maintaining different intertidal invertebrate assemblages between two nearby locations in New Zealand (Wellington Harbour and the south coast), long reported to have dramatically different communities (with greater cover of sessile invertebrates in the Harbour). Sites in Wellington Harbour were hypothesised to have higher monthly recruitment rates of mussels and barnacles and greater barnacle colonisation after 1 year. Surveys were conducted to quantify community differences. In Wellington Harbour, the mid-intertidal zone was dominated by the mussel Mytilus galloprovincialis and the barnacle Chamaesipho columna and the high intertidal zone by C. columna. In contrast, on the south coast mussels were almost completely absent from both tidal heights and barnacles (predominantly Chamaesipho brunnea) were sparse. In the high zone, monthly recruitment and long term colonisation (over 1 year) of barnacles was much greater in the Harbour; in the mid-intertidal zone, mussel recruitment was up to two orders of magnitude greater in the Harbour than the south coast. Species-specific recruitment patterns differed between the locations, however and were consistent with those of adult abundance.

scholarly journals Matrix models for quantifying competitive intransitivity from species abundance data

Oikos ◽  
2014 ◽  
Vol 123 (9) ◽  
pp. 1057-1070 ◽  
Author(s):  
Werner Ulrich ◽  
Santiago Soliveres ◽  
Wojciech Kryszewski ◽  
Fernando T. Maestre ◽  
Nicholas J. Gotelli
Keyword(s):  
Matrix Models ◽  
Species Abundance ◽  
Abundance Data ◽  
Species Abundance Data

scholarly journals Functional rarefaction for species abundance data

2012 ◽  
Vol 3 (3) ◽  
pp. 519-525 ◽  
Author(s):  
Carlo Ricotta ◽  
Sandrine Pavoine ◽  
Giovanni Bacaro ◽  
Alicia T. R. Acosta
Keyword(s):  
Species Abundance ◽  
Abundance Data ◽  
Species Abundance Data

Does using species abundance data improve estimates of species diversity from remotely sensed spectral heterogeneity?

2010 ◽  
Vol 10 (2) ◽  
pp. 390-396 ◽  
Author(s):  
Jens Oldeland ◽  
Dirk Wesuls ◽  
Duccio Rocchini ◽  
Michael Schmidt ◽  
Norbert Jürgens
Keyword(s):  
Species Diversity ◽  
Species Abundance ◽  
Remotely Sensed ◽  
Abundance Data ◽  
Species Abundance Data

scholarly journals When Can Species Abundance Data Reveal Non-neutrality?

2015 ◽  
Vol 11 (3) ◽  
pp. e1004134 ◽  
Author(s):  
Omar Al Hammal ◽  
David Alonso ◽  
Rampal S. Etienne ◽  
Stephen J. Cornell
Keyword(s):  
Species Abundance ◽  
Abundance Data ◽  
Species Abundance Data

scholarly journals Speaking Stata: The Protean Quantile Plot

2005 ◽  
Vol 5 (3) ◽  
pp. 442-460 ◽  
Author(s):  
Nicholas J. Cox
Keyword(s):  
Standard Form ◽  
Species Abundance ◽  
Abundance Data ◽  
Quantile Plots ◽  
Cumulative Probabilities ◽  
Species Abundance Data

Quantile plots showing by default ordered values versus cumulative probabilities are both well known and also often neglected, considering their major advantages. Their flexibility and power is emphasized by using the qplot program to show several variants on the standard form, making full use of options for reverse, ranked, and transformed scales and for superimposing and juxtaposing quantile traces. Examples are drawn from the analysis of species abundance data in ecology. A revised version of qplot is formally released with this column. Distribution plots in which the axes are interchanged are also discussed briefly, in conjunction with a revised version of distplot, also released now.

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