Analyzing insect community data collected across multiple sites and time points involves several multivariate statistical techniques. Here's a step-by-step guide to help you get started:

1. Data Preparation

• Compile Data: Ensure your data is organized with species abundance or presence/absence data for each site and time point.
• Standardize Data: Normalize your data if necessary to account for differences in sampling effort.

2. Calculate Community Dissimilarities

• Calculate Dissimilarities: Use measures like Bray-Curtis dissimilarity or Jaccard index to quantify differences between insect communities at different sites and times.

3. Perform Ordination Analysis

• Non-metric Multidimensional Scaling (NMDS): This method helps visualize patterns in community composition without assuming linear relationships.
• Principal Coordinates Analysis (PCoA): Another ordination technique that can be used to visualize dissimilarities in multivariate data.
• Detrended Correspondence Analysis (DCA): Useful for analyzing species composition gradients.

4. Constrained Ordination

• Redundancy Analysis (RDA): A constrained ordination method that relates community composition to environmental variables.
• Canonical Correspondence Analysis (CCA): Similar to RDA but specifically designed for ecological data.

5. Environmental Interpretation

• Vector Fitting: Use vector fitting to interpret how environmental variables influence community composition.
• Surface Fitting: Visualize how species composition changes along environmental gradients.

6. Statistical Testing

• Permutation Tests: Perform permutation tests to assess the significance of patterns observed in ordination analyses.
• Multivariate Analysis of Variance (MANOVA): Use MANOVA to test for differences in community composition between sites or time points.

7. Visualization

• Biplot: Create biplots to visualize species and site relationships.
• Heatmaps: Use heatmaps to display species abundance across sites and time points.

Example Workflow in R

If you're using R, the vegan package is a powerful tool for multivariate analysis of ecological data. Here's a basic example:

R

# Load necessary libraries library(vegan) # Load your data data

Thanks for the answer, but I was thinking to tests that can account for the potential temporal correlation, and as far as I know the perMANOVA is not accounting for that, am I wrong?

To analyze insect community data across multiple sites and time points, consider these approaches:

1. Multivariate Time Series Modeling: Use models like VAR or state-space to capture temporal dynamics, though they require large datasets and may assume linearity.

2. Multivariate Mixed-Effect Models: Linear or generalized mixed models (LMM/GLMM) handle nested data (e.g., sites within treatments), but can be computationally intensive for multivariate responses.

3. Latent Models (GLLVM): Generalized Linear Latent Variable Models are ideal for high-dimensional count data, handling correlations among species.

4. Machine Learning: Use random forests, gradient boosting, or neural networks to model non-linear relationships and temporal patterns.

5. Ecological Analyses: Use PERMANOVA, ANOSIM, and ordination methods (e.g., NMDS) to test and visualize community differences between treatments.

Start with ordination for exploration, test with PERMANOVA, and apply GLLVMs or mixed models for deeper insights. Resources: R packages like vegan, gllvm, and tutorials on R-bloggers.

You can simply use presence-absence data and compare beta diversity, which is the difference in composition of species between sites. See my attached paper on how to do this :)

https://ecology.dlilab.com/lectures/09_communityecology/notes

Analysis of Multivariate Insect Community Data Collected Across Multiple Sites and Time Points

Introduction The analysis of multivariate insect community data collected across multiple sites and time points requires statistical approaches that account for the complexity of the data, such as spatial and temporal effects, as well as interactions among species. To compare community composition between Treatment and Control conditions, various methods can be used depending on the goals of the analysis, the structure of the data, and the assumptions of the model.

Here are several key approaches that can be used:

1. Multivariate Time Series Modeling

Relevance: Suitable for datasets with a strong temporal dimension, where changes in community composition at each site can be analyzed as a function of time.

Approach:

• Vector Autoregressive (VAR) Models: Used to model the relationships between community variables over time.
• State-Space Models: Useful for integrating time effects with environmental variables affecting the insect community.
• Dynamic Factor Analysis (DFA): Ideal for identifying key temporal patterns in community data.

Advantages:

• Can capture dynamic patterns over time.
• Useful for predicting changes in community composition.

Limitations:

• Requires complete and well-structured temporal data.
• Does not directly capture treatment effects without model modifications.

2. Multivariate Mixed-Effect Models

Relevance: Suitable for data that includes many locations and time points with a hierarchical structure, such as species repeated across different sites.

Approach:

• This model accounts for both fixed effects (e.g., treatment, time) and random effects (e.g., site, species).
• Use packages like lme4 in R to build mixed models.
• For multivariate community data, Multivariate Generalized Linear Mixed Models (MGLMMs) can be applied.

Advantages:

• Can capture variation between locations and species.
• Accommodates unbalanced or missing data.

Limitations:

• Complexity increases with the number of species or sites.
• Requires careful interpretation of interactions between effects.

3. Latent Models (Generalized Linear Latent Variable Models, GLLVM)

Relevance: Highly suitable for large ecological community datasets, where there are many species with complex relationships.

Approach:

• GLLVM models community data as a function of latent factors (e.g., environmental gradients).
• Use packages like boral or gllvm in R.

Advantages:

• Can identify underlying environmental gradients that explain community variation.
• Implicitly accounts for species interactions.

Limitations:

• Requires careful interpretation of the latent factors.
• Does not directly model temporal or spatial interactions explicitly.

4. Machine Learning Approaches

Relevance: Suitable for non-parametric analysis of complex datasets with nonlinear interactions.

Approach:

• Random Forests: For classifying sites based on community composition.
• Gradient Boosting Machines: To identify major factors influencing the community.
• Ordination Techniques with ML Algorithms: Used to reduce data dimensionality (e.g., kernel-based PCA).

Advantages:

• Does not require assumptions about the data distribution.
• Can capture nonlinear relationships between variables.

Limitations:

• Interpretation of the model is more difficult than parametric approaches.
• Does not provide direct parameter estimates for management decisions.

Integrated Approach Strategy

• Combining several methods can provide richer insights. For example:Use ordination (e.g., NMDS or PCA) to identify major patterns in the data. Apply multivariate mixed-effect models to evaluate the effects of Treatment vs Control. Use latent variable models to uncover hidden factors affecting the community.

This approach provides a comprehensive framework for analyzing multivariate insect community data, considering the temporal, spatial, and ecological complexities of the dataset.

4o mini

It depends on the variables you have in your data and the hypothesis you need to test. For example, network analysis is a plausible approach to handle the data about community, but is it reasonable in your case? Can you trace a single object across timepoints? Latent class models can give a qualitative structure, so it is possible to trace types of objects and make implications about its proportions, but is it reasonable?