Kwaku Peprah Adjei

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.

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 modeling (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 standardised 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 summarise species–environment relationships. The workflow used vascular plant data from Norway, combining occurrence-only and presence-absence datasets with environmental covariates. Outputs were generated at a spatial resolution of 500 x 500 meters, 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 was 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 highlight patterns consistent with previous mapping efforts. Validation showed an increase in model accuracy when compared to models which did not use an iSDM framework. The workflow highlights limitations in the infrastructure of the currently openly accessible data, particularly the need for more structured presence-absence datasets and standardized metadata. Main conclusions: This study underscores the potential of workflows that integrate disparate datasets for biodiversity modeling. 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 optimising 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.

One of the most important knowledge gaps for nature management is the species diversity in an area. In order to know the spatial distribution of richness of different species groups, observations of species are needed. This is challenging, because for many species groups it is resource-intensive to systematically map the occurrence of species over large areas. Instead, citizen science data has become more important, but as it is mostly collected opportunistically , there is a high geographical bias in the collection. If this is not taken into account, one gets the impression that the most species-rich areas are where there are the most people. This bias can be estimated and corrected if citizen science data is correctly combined with survey data, e.g. from national monitoring programs.Integrated species distribution models (iSDM) are relatively new statistical tools that combine opportunistically collected data with systematically collected data with information about collection intensity. These models have great potential to benefit from the strengths of different types of data, while at the same time being able to take into account some of the challenges, but the models have not been used to a large extent in large-scale modeling of species diversity. Large-scale data presents both opportunities and problems. It allows us to leverage information across species, but the size of the data makes it infeasible to model every species together.Here we will present our approach to this large-scale modelling, using iSDMs across species to produce fine-scale maps of communities over Norway, along with their associated uncertainties and biases. The model is based on a state space, using ideas from point processes, which has proved flexible for different data types and for which there are efficient and flexible fitting methods. We will outline the model and the methods we have used to overcome the problems of large data and the requirement for fine spatial scales. We will also outline how this is being extended to include time and topologies such as river networks.

Fragmented datasets, sampling bias and inconsistent observation protocols often limit the use of citizen science data for indicator development. Citizen science data are often collected opportunistically without a design for use in biodiversity metrics. However, the large volume of data, and the broad spatial and taxonomic coverage, provide an invaluable source of biodiversity information at scale.Here, we present a pipeline that integrates heterogeneous datasets to generate large scale maps of biodiversity metrics. These maps form a basis for management relevant information tools. We apply integrated species distribution modelling (iSDM) to correct for sampling bias and differences in data collection methods. We use the large number of open datasets available through aggregators such as GBIF.The workflow has four main steps. These are 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. We standardise these inputs and combine them in a common iSDM framework. This produces species intensity maps, associated uncertainty estimates and sampling effort maps. We further process these outputs to identify biodiversity hotspots and to summarise species environment relationships.We use Norway as a case study. Norway has extensive opportunistic citizen science programs. We produced detailed maps of species richness, biodiversity hotspots, uncertainty and sampling intensity. Our results show the potential of pipelines that integrate disparate datasets. Our example also reveals important limitations in the current body of data. In particular, it is not possible to infer and correct for sampling bias without data that allow estimation of the probability of occurrence. In practice this means data that include information on both what was observed and what was not observed. Our study therefore demonstrates a clear need to incorporate more structured approaches into citizen science data. This should not replace opportunistic, curiosity driven activity. It should add to it and support both the large data volumes and the high level of public engagement.

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.

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.

INTRODUCTION Biodiversity data from surveys and other monitoring programs such as Citizen Science (called ‘CS’ hereafter) play a vital role in estimating species distributions and making conservation decisions . With an influx of data available to researchers through the various biodiversity databases such as iNaturalist , GBIF , Artsdatabanken (https://www.artsdatabanken.no/), eBird , amongst many others, there has been increased coverage and inferences about species distributions. However, these biodiversity data (especially those from CS) are subject to sampling and systematic biases, raising concerns about their use in scientific research . The sampling bias may be due to sampling variation in space and time, and the systematic bias could arise because of species misreporting and misidentification as well as imperfect detection, amongst many others . As discussed by , various statistical approaches have been used in analysing biodiversity data such as the generalised linear (mixed) models , hierarchical models such as N-Mixture models ( and references therein) and joint species distribution models ( and references therein). These methods depend on the type of response variable. Some modellers of biodiversity data use Maxent , a constrained optimisation approach that finds the optimal species density subject to constraints . Attaching any uncertainty to Maxent’s predictions is impossible as a non-stochastic approach. However, has shown that Maxent is equivalent to point process models and that the point process framework can be used to obtain standard errors on model coefficients and predictions. Biodiversity data have also been modelled as typical geostatistic data with a binary response by including pseudo-absences . These pseudo-absences are used to account for the biases in Maxent to generate reliable and unbiased species distribution models . However, this approach adds an arbitrary amount of data and ignores the spatial autocorrelation between absences, as pointed out in . However, some studies have explored different methods to control the selection of pseudo-absences to fit the species distribution models . Furthermore, other approaches propose modelling biodiversity data as a thinned point pattern . A point pattern is a collection of points with random locations. That is, the locations of the points are not fixed or previously chosen. A spatial point process is the model that determines the location and amount of points in an area. Thinning is an operation on point patterns that uses a specified rule to determine which points in the point pattern are deleted. For biodiversity data, the thinning of the point pattern of the true species occurrences is caused by inherent biases in them. For instance, proposed that a single covariate related to a source of bias (e.g. accessibility) should be included in the linear predictor as a log-linear function to determine the probability of retaining an occurrence (i.e. a variable restricted to ( − ∞, 0]) as described in ). and , however, proposed integrating extra sources of information, such as professional surveys, to account for these biases. These biases can be explicitly modelled as a function of known covariates if that information is available or as an extra random effect when data related to the biases is unavailable . However, these approaches do not consider how the various sources of biases discussed in the next paragraphs affect the observed data. Firstly, biodiversity data are typically affected by biased sampling and unbiased imperfect sampling processes. Observers, mostly from CS projects, usually choose where they go. This decision is frequently influenced by factors such as accessibility and where observers expect to find more occurrences (i.e. preferential sampling; ). This is known as the biased sampling process. On the other hand, some areas will be inaccessible due to government regulations or because they are protected areas or a sub-sample of biological materials are taken at a given location . This sampling incompleteness is an example of an unbiased imperfect sampling process. and have acknowledged the role of the sampling process as a thinning factor in the context of point patterns. explored a model that assumes the thinning process as a log-linear function that affects the intensity of the observed point pattern. Further work by and explored the implications of not properly accounting for the sampling bias in biodiversity data. This was done by comparing improvements in goodness-of-fit and ecological interpretability of a model that accounts for sampling bias to approaches such as Maxent or a model that does not account for the sampling bias at all. In both cases, modelling the sampling bias as a thinning operation on a point pattern reduced the bias in the estimated effect of ecological covariates on the spatial distribution of species occurrences. However, these developments have not explored other sources of bias that can affect the process of collecting biodiversity data. Secondly, imperfect detection is also another source of bias in biodiversity data . Imperfect detection occurs when the observer fails to detect the species even though it was present. Detectability must be accounted for in estimating species trend and abundance because some species are overlooked, and the detection of the species will depend on its behaviour . The detection and identification of these species are influenced by the observer’s attention to a particular species and place, time and factors determining visibility, such as weather conditions . Failure to account for the imperfect detection when analysing biodiversity data may bias parameter estimates \citep[especially when the detection probability varies systematically; ][]{welsh2013fitting} and thus make statistical inference less reliable . Integrated species distribution models (hereafter, iSDMs) have been proposed to analyse these...

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 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 IDMs to capture the true community 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.