Robert Brian O'HAra

Aim: A comprehensive understanding of the spatial distribution of biodiversity is hindered by fragmented datasets, sampling biases, and inconsistent observation protocols. Here, we present a workflow that integrates disparate datasets to produce large scale maps of biodiversity metrics as a basis for management‐relevant information tools. We use integrated Species Distribution modelling (iSDM) to account for sampling biases and disparate data collection techniques, taking advantage of the vast numbers of open datasets available in data aggregators like GBIF. Location: Norway (excluding Svalbard and Jan Mayen). Taxon: Vascular plants. Methods: The workflow consists of four main steps: data acquisition, data integration, integrated species distribution modelling (iSDM), and the production of derived outputs. Input data include structured surveys, opportunistic observations, and environmental covariates. These are standardized and integrated into a point‐processed based iSDM framework to produce species richness maps, associated uncertainties, and sampling effort maps. The outputs are further processed to identify biodiversity hotspots or to summarize species–environment relationships. The workflow used vascular plant data from Norway, combining occurrence‐only and detection/non‐detection datasets with environmental covariates. Outputs were generated at a spatial resolution of 500 × 500 m, balancing accuracy, computational feasibility and relevance for management decisions. High‐performance computing resources were utilized for model fitting and predictions. A subset of available data were used to validate the species richness maps. Results: We produced detailed maps of species richness, uncertainties and sampling intensity across Norway's heterogeneous landscape, incorporating 1218 species in our final results. The species richness patterns display patterns consistent with previous mapping efforts, highlighting higher richness in the southern and coastal regions of Norway and distinctly lower richness in the north. Validation showed a modest increase in model accuracy, improving on past modelling efforts while integrating a much wider variety of datasets through the iSDM framework. The workflow highlights limitations in the infrastructure of the currently openly accessible data, particularly the need for more structured detection/non‐detection datasets and standardized metadata. Main Conclusions: This study underscores the potential of workflows that integrate disparate datasets for biodiversity modelling. To maximize accuracy and utility, future efforts should focus on improving data standardization, the publication and collection of more structured data, and fostering data‐sharing collaborations. Advances in the workflow itself, including optimizing modelling covariates and integrating more comprehensive spatio‐temporal aspects, will also increase the relevance of the outputs. These advances will increase our ability to estimate species richness with a precision and accuracy that can reliably inform conservation and management decisions.

In this article, we present the case for Generalized Linear Latent Variable Models (GLLVMs) as a go-to choice of statistical method for any community ecologist wanting to tackle a range of present-day ecological research questions. GLLVMs bring tools and capabilities from classic (mixed-effects) regression models to multivariate community analysis, providing a number of novel ways to tailor models specifically to one’s study questions and data properties not available when using non-model-based multivariate methods. In order to facilitate further adoption of these methods by community ecologists, we provide 1) a practitioner-focused and practical overview of the advantages the GLLVM framework brings to the table when addressing different core ecological questions, 2) a number of concrete suggestions for how GLLVMs best can be incorporated into the analytical workflow of community ecologists, and 3) two illustrative worked examples of this workflow in action on real-world data.

Summary: Ecologists are being challenged to predict how ecosystems will respond to climate changes. According to the Multi‐Colored World (MCW) hypothesis, climate impacts may not manifest because consumers such as fire and herbivory can override the influence of climate on ecosystem state. One MCW interpretation is that climate determinism fails because alternative ecosystem states (AES) are possible at some locations in climate space. We evaluated theoretical and empirical evidence for the proposition that forest and savanna are AES in Africa. We found that maps which infer where AES zones are located were contradictory. Moreover, data from longitudinal and experimental studies provide inconclusive evidence for AES. That is, although the forest‐savanna AES proposition is theoretically sound, the existing evidence is not yet convincing. We conclude by making the case that the AES proposition has such fundamental consequences for designing management actions to mitigate and adapt to climate change in the savanna‐forest domain that it needs a more robust evidence base before it is used to prescribe management actions.

Species distribution models and maps from large‐scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi‐species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi‐species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold‐out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Aim: Climate change and habitat loss or degradation are some of the greatest threats that species face today, often resulting in range shifts. Species traits have been discussed as important predictors of range shifts, with the identification of general trends being of great interest to conservation efforts. However, studies reviewing relationships between traits and range shifts have questioned the existence of such generalized trends, due to mixed results and weak correlations, as well as analytical shortcomings. The aim of this study was to test this relationship empirically, using analytical approaches that account for common sources of bias when assessing range trends.Location: Tanzania, East Africa.Time period: 1980–1999 and 2000–2020.Major taxa studied: 57 savannah specialist birds found in Tanzania, belonging to 26 families and 11 orders.Methods: We applied recently developed integrated spatio‐temporal species distribution models in R‐INLA, combining citizen science and bird Atlas data to estimate ranges of species, quantify range shifts, and test the predictive power of traditional trait groups, as well as exposure‐related and sensitivity traits. We based our study on 40 years of bird observations in East African savannahs, a biome that has experienced increasing climatic and non‐climatic pressures over recent decades. We correlated patterns of change with species traits using linear regression models.Results: We find indications of relationships identified by previous research, but low average explanatory power of traits from an ecological perspective, confirming the lack of meaningful general associations. However, our analysis finds compelling species‐specific results.Main conclusions: We highlight the importance of individual assessments while demonstrating the usefulness of our analytical approach for analyses of range shifts.

Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species‐level dataset of presence–absence records with a co‐located dataset of group‐level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM.

The ecological niche is a fundamental concept in ecology that can be used in order better understand species relationships. The overlap in species niches provides a measure of the likelihood for species to co‐occur. Most approaches that quantify niche overlap have been based on distance and similarity indices, for pairwise combinations of species. In this paper, we suggest that niche overlap can be calculated from the predictions of a model. Using a statistical model to predict niche overlap provides various benefits, includes the possibility to adjust the model to properties of the data. We demonstrate this using an example dataset of an ecological community of Foraminifera species, to which we fit a generalized linear latent variable model (GLLVM). GLLVMs are a flexible class of models that allow to estimate the distribution of species using both measured environmental predictors and residual covariation between species. We demonstrate how to calculate niche overlap from GLLVMs for any combination of species, and separately for different environments. Predicting niche overlap from a model further expands the toolset available to ecologists for the exploration of species co‐occurrence patterns.

Ordination methods have been used by community ecologists to describe and explore the communities they sample by reducing this variation down to a small number of dimensions. More recently, Joint Species Distribution Models have been developed to model and predict the distributions of several species simultaneously. Contemporary models for the data for both of these problems are essentially the same, called Generalised Linear Latent Variable Models (GLLVMs). Based on this we suggest some avenues of cross-fertilisation between the two areas of research. We also describe some of the extensions to GLLVMs, and from this suggest the development of Hierarchical Ordination, as a way of efficiently modelling communities of species in space.