Devil in the details: how can we avoid potential pitfalls of CATS regression when our data do not follow a Poisson distribution?

Background Community assembly by trait selection (CATS) allows for the detection of environmental filtering and estimation of the relative role of local and regional (meta-community-level) effects on community composition from trait and abundance data without using environmental data. It has been shown that Poisson regression of abundances against trait data results in the same parameter estimates. Abundance data do not necessarily follow a Poisson distribution, and in these cases, other generalized linear models should be fitted to obtain unbiased parameter estimates. Aims This paper discusses how the original algorithm for calculating the relative role of local and regional effects has to be modified if Poisson model is not appropriate. Results It can be shown that the use of the logarithm of regional relative abundances as an offset is appropriate only if a log-link function is applied. Otherwise, the link function should be applied to the product of local total abundance and regional relative abundances. Since this product may be outside the domain of the link function, the use of log-link is recommended, even if it is not the canonical link. An algorithm is also suggested for calculating the offset when data are zero-inflated. The relative role of local and regional effects is measured by Kullback-Leibler R2. The formula for this measure presented by Shipley (2014) is valid only if the abundances follow a Poisson distribution. Otherwise, slightly different formulas have to be applied. Beyond theoretical considerations, the proposed refinements are illustrated by numerical examples. CATS regression could be a useful tool for community ecologists, but it has to be slightly modified when abundance data do not follow a Poisson distribution. This paper gives detailed instructions on the necessary refinement.

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM

We previously developed LDM for testing hypotheses about the microbiome that performs the test at both the community level and the individual taxon level. LDM can be applied to relative abundance data and presence-absence data separately, which work well when associated taxa are abundant and rare, respectively. Here we propose an omnibus test based on LDM that allows simultaneous consideration of data at different scales, thus offering optimal power across scenarios with different association mechanisms. The omnibus test is available for the wide range of data types and analyses that are supported by LDM. The omnibus test has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM .

Improved quantitative microbiome profiling for environmental antibiotic resistance surveillance

Background Understanding environmental microbiomes and antibiotic resistance (AR) is hindered by over reliance on relative abundance data from next-generation sequencing. Relative data limits our ability to quantify changes in microbiomes and resistomes over space and time because sequencing depth is not considered and makes data less suitable for Quantitative Microbial Risk Assessments (QMRA), critical in quantifying environmental AR exposure and transmission risks. Results Here we combine quantitative microbiome profiling (QMP; parallelization of amplicon sequencing and 16S rRNA qPCR to estimate cell counts) and absolute resistome profiling (based on high-throughput qPCR) to quantify AR along an anthropogenically impacted river. We show QMP overcomes biases caused by relative taxa abundance data and show the benefits of using unified Hill number diversities to describe environmental microbial communities. Our approach overcomes weaknesses in previous methods and shows Hill numbers are better for QMP in diversity characterisation. Conclusions Methods here can be adapted for any microbiome and resistome research question, but especially providing more quantitative data for QMRA and other environmental applications.

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

Matching Data Types to the Objectives of Species Distribution Modeling: An Evaluation With Marine Fish Species

Species distribution model (SDM) is a crucial tool for forecasting ranges of species and mirroring habitat references and quality. Different types of species distribution data have been commonly used in SDMs regarding different purposes and availability, whereas, the influences of data types on model performances have not been well understood. This study considered three data types characterized by different levels of organism information and cost in data acquisitions, namely presence/absence (P/A), ordinal data, and abundance data. We developed a range of distribution models for nine demersal species in the coastal waters of Shandong Peninsula, China, using two modeling algorithms [the Generalized Additive Model (GAM) and Random Forest]. Firstly, we evaluated the performances of all models on predicting species occurrence (i.e., habitat suitability or range boundaries), and then compared the models built with ordinal data and abundance data on projecting ordinal predictions (i.e., relative density or habitat quality). Their predictive abilities were assessed through cross-validation tests with diverse performance measurements. Overall, no data type is superior in all situations, but combined with two algorithms, the abundance data slightly outperformed the ordinal data and P/A data unexpectedly exerted reliable performances. Specifically, the effectiveness of data type for two application purposes of SDMs substantially varied with modeling algorithms, revealing that GAMs always benefit most from ordinal data and the opposite was true for Random Forest. For some small resident organisms with moderate prevalence, rough distribution data might be adopted for providing reliable projections. Our findings highlight the importance of clarifying the objectives of SDMs when choosing data types for species distribution modeling.

Arabidopsis thaliana computationally-generated next-state gene interaction models

The construction of gene interaction models must be a fully collaborative and intentional effort. All aspects of the research, such as growing the plants, extracting the mea-surements, refining the measured data, developing the statistical framework, and forming and applying the algorithmic techniques, must lend themselves to repeatable and sound practices. This paper holistically focuses on the process of producing gene interaction models based on transcript abundance data from Arabidopsis thaliana after stimulation by a plant hormone.

A multilevel dataset of microplastic abundance in the world’s upper ocean and the Laurentian Great Lakes

A total of 8218 pelagic microplastic samples from the world’s oceans were synthesized to create a dataset composed of raw, calibrated, processed, and gridded data which are made available to the public. The raw microplastic abundance data were obtained by different research projects using surface net tows or continuous seawater intake. Fibrous microplastics were removed from the calibrated dataset. Microplastic abundance which fluctuates due to vertical mixing under different oceanic conditions was standardized. An optimum interpolation method was used to create the gridded data; in total, there were 24.4 trillion pieces (8.2 × 104 ~ 57.8 × 104 tons) of microplastics in the world’s upper oceans.

SLAM Project - Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores: I - the spiders from native forests of Terceira and Pico Islands (2012-2019)

Long-term monitoring of invertebrate communities is needed to understand the impact of key biodiversity erosion drivers (e.g. habitat fragmentation and degradation, invasive species, pollution, climatic changes) on the biodiversity of these high diverse organisms. The data we present are part of the long-term project SLAM (Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores) that started in 2012, aiming to understand the impact of biodiversity erosion drivers on Azorean native forests (Azores, Macaronesia, Portugal). In this contribution, the design of the project, its objectives and the first available data for the spider fauna of two Islands (Pico and Terceira) are described. Passive flight interception SLAM traps (Sea, Land and Air Malaise traps) were used to sample native forest plots in several Azorean islands, with one trap being set up at each plot and samples taken every three months following the seasons. The key objectives of the SLAM project are: 1) collect long-term ecological data to evaluate species distributions and abundance at multiple spatial and temporal scales, responding to the Wallacean and Prestonian shortfalls, 2) identify biodiversity erosion drivers impacting oceanic indigenous assemblages under global change for conservation management purpose, 3) use species distribution and abundance data in model-based studies of environmental change in different islands, 4) contribute to clarifying the potential occurrence of an "insect decline" in Azores and identifying the spatial and temporal invasion patterns of exotic arthropod species, 5) contribute with temporal data to re-assess the Red-list status of Azorean endemic arthropods and 6) perform studies about the relationship between diversity (taxonomic, functional and phylogenetic) and ecosystem function. The project SLAM (Long Term Ecological Study of the Impacts of Climate Change in the natural forest of Azores) is described in detail. Seasonal distribution and abundance data of Azorean spiders, based on a long-term study undertaken between 2012 and 2019 in two Azorean Islands (Terceira and Pico), is presented. A total of 14979 specimens were collected, of which 6430 (43%) were adults. Despite the uncertainty of juvenile identification, juveniles are also included in the data presented in this paper, since the low diversity allows a relatively precise identification of this life-stage in Azores. A total of 57 species, belonging to 50 genera and 17 families, were recorded from the area, which constitutes baseline information of spiders from the studied sites for future long-term comparisons. Linyphiidae were the richest and most abundant family, with 19 (33%) species and 5973 (40%) specimens. The ten most abundant species are composed mostly of endemic or native non-endemic species and only one exotic species (Tenuiphantes tenuis (Blackwall, 1852)). Those ten most abundant species include 84% of all sampled specimens and are clearly the dominant species in the Azorean native forests. Textrix caudata L. Koch, 1872 was firstly reported from Terceira and Pico Islands, Araneus angulatus Clerck, 1757 was firstly reported from Terceira Island, Neriene clathrata (Sundevall, 1830) and Macaroeris diligens (Blackwall, 1867) were firstly reported from Pico Island. This publication contributes not only to a better knowledge of the arachnofauna present in native forests of Terceira and Pico, but also to understand the patterns of abundance and diversity of spider species, both seasonally and between years.

Abundance-based approaches

Across a wide range of different organisms, abundance data form one of the backbones for understanding the dynamics of populations. This type of data consists of measures of population size over time or space in the form of numbers of individuals, biomass, areal cover, or other measures. Abundance data contain no direct information about demographic processes but are available at larger scales or higher resolution in space and time than direct demographic data. This chapter introduces some of the basic statistical modeling strategies that can be used to learn about populations from abundance data in the absence of information about demographic details. These strategies include standard but flexible regression techniques, including mixed and additive models, time-series methods such as auto-regressive and state-space models, as well as simple population growth models derived from ecological theory.