Joachim Paul Töpper

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.

Ecological data are increasingly collected by networks of collaborators using replicated designs and methods, which can significantly improve the quality and quantity of data throughout the ecological niche and geographic range of species or communities. The coordinated generation and management of data is critical for producing datasets (Standard Data Products) across multiple sites that can be used by different researchers, over extended time periods and for multiple purposes. We describe and use a Quality Assurance framework for the design, collection and production of reproducible Standard Data Products for distributed ecology projects. We identified six critical project elements of a Quality Assurance framework (QA1‐6) to produce ecological Standard Data Products with high immediate and future value. We applied the Quality Assurance framework to the Plantpopnet project as a case‐study. Plantpopnet is a coordinated distributed system for population macroecology using the model species Plantago lanceolata . We mapped Plantpopnet activities to the Quality Assurance Framework as follows: (QA1) Measurable objectives : research project objectives with data requirements, (QA2) Process control : governance policies, (QA3) Project specific procedures : model organism selection and data collection protocol, (QA4) Supporting production of high quality data : recruitment, retention and engagement of participants, (QA5) Data management : data management plan and reproducible data cleaning workflow, (QA6) Production and management of outputs : Standard Data Products and papers. Explicit use of Quality Assurance, project and data management tools together with standardised ecological methods facilitated the design, collection, maintenance and sustainability of high‐quality data products. We provide a Quality Assurance framework together with governance documents, code and data for a reproducible Standard Data Product. This framework supports a distributed funding model which can be sustainably applied to facilitate future research and applications of coordinated distributed ecology projects.

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.

1. Ecological data are increasingly collected by networks of collaborators using replicated designs and methods, which can significantly improve the quality and quantity of data collected throughout the ecological niche and geographic range of a species. The coordinated generation and management of data is critical to producing coherent datasets (Standard Data Products) across multiple sites that can be used by different researchers, over extended time periods and for multiple purposes. 2. Here, we describe and use a Quality Assurance framework for the design, collection and production of reproducible Standard Data Products for population ecology. We identified six critical project elements of a Quality Assurance framework (QA1-6) to produce ecological Standard Data Products with high immediate and future value. 3. We applied the Quality Assurance framework to the Plantpopnet project as a case-study. Plantpopnet is a coordinated distributed system for demography and population macroecology which uses the model species Plantago lanceolata. We mapped Plantpopnet activities to the Quality Assurance Framework as: QA1) Measurable objectives: research project objectives with data requirements, QA2) Process control: governance policies, QA3) Project specific procedures: model organism selection and data collection protocol, QA4) Supporting production of high quality data: recruitment, retention and engagement of participants, QA5) Data management: data management plan and reproducible data cleaning workflow, QA6) Production and management of outputs: Standard Data Products and papers. The framework allows for flexibility and adaptation to changing circumstances. 4. Explicit use of Quality Assurance, project and data management tools together with standardised ecological methods enabled the design, collection, maintenance and sustainability of high-quality data products. We provide a Quality Assurance framework together with governance documents, code and data for a reproducible Standard Data Product. This framework can be applied to the goals of the Plantpopnet project as well as facilitate future research and applications of coordinated distributed ecology projects more generally.

A number of modelling frameworks exist to estimate resilience from ecological datasets. A subset of these frameworks seeks to estimate the whole ‘stability landscape', which can be used to calculate resilience and identify stable states and tipping points. These methods provide opportunities for insights into possible causes and consequences of variation in ecosystem resilience and dynamics. However, because such models can be complex to implement, there has so far been a substantial barrier to their application in ecological research. Here, we present the ‘mixglm' package for R software, which parametrizes stability landscapes using a mixture model approach. It provides tools for the calculation of resilience, identification of stable states and tipping points, as well as visualization functions. Flexible model specification allows the mean, precision, and probability of each mixture component to be linked to multiple predictors, such as environmental covariates. ‘mixglm' is based on Bayesian inference via NIMBLE and supports normal, beta, gamma, and negative binomial distributed response variables. We illustrate the use of ‘mixglm' with a published case of tree cover in South America, which reports a stability landscape with distinct stable states. Using ‘mixglm', we replicated the identification of these states. Moreover, we quantified the uncertainty of our estimates, and computed resilience estimates of South America's forests. We also conducted a power analysis to provide guidance regarding required sample sizes. ‘mixglm' can be readily used to describe stability landscapes and identify stable states in most spatial datasets, and it is accompanied by tools for the calculation of resilience estimates.

Aims: Rising ambient air temperatures may have adverse effects on alpine plant communities. To avoid extinction and to mitigate the demographic impacts of climate warming, migration and the ability to adapt become increasingly important. Experimental studies that simulate warming help test the extent and direction of functional adaptation. We ask: (1) To what extent does experimental warming drive intraspecific trait shifts in alpine species? (2) Do these trait responses vary across a precipitation gradient? (3) Do responses vary between forbs and graminoids? Location: Alpine grasslands along a precipitation gradient in south‐western Norway. Methods: At three alpine sites spanning 1315–3601 mm of annual precipitation, we measured 10 plant functional traits across 17 species of graminoids, perennial forbs and dwarf shrubs. We compared the traits of plants in open‐top warming chambers with those under ambient temperature conditions. Effect sizes were estimated using Cohen's d and analysed with respect to precipitation regimes and growth form. Results: Plant height generally increased in response to warming across all sites for both growth forms. For other traits, warming effects were context‐dependent and varied among species. At the medium precipitation site, plants showed shifts toward more conservative resource‐use strategies in response to warming, characterised by increased leaf area and leaf dry matter content, along with reduced species leaf area and leaf nitrogen concentration. The enrichment in heavier nitrogen and carbon isotopes with warming is consistent with the expectation that warming selects for individuals with high water‐use efficiency. Forbs generally exhibited stronger but more variable responses to warming than graminoids. Conclusion: Warming induces trait shifts in alpine species, but responses depend on local conditions and growth form. Predicting alpine community responses to climate change requires trait‐based approaches and research designs that allow assessing and exploring patterns in both taxonomic and environmental context dependencies.

Question: How do vascular plant and cryptogam cover, bare peat area and species composition in different microforms on a boreal raised bog change over a 34‐year period (1988–2022)? We discuss the observed patterns in the light of ongoing climate change. Location: Rønnåsmyra Nature Reserve, south‐eastern Norway. Methods: We recorded total vascular plant and cryptogam cover, bare peat area and species composition in 51 permanent 0.5 × 0.5 m plots in 1988, 2004 and 2022. The plots were assigned to microform classes (carpet, lawn, hummock) based on their characteristics in 1988 and 2022. We analysed changes over time and explored the relationship between change in species composition and change in the relative distance from the bog surface to the groundwater table (rDWT) in each microform using linear mixed‐effect models and ordination (GNMDS). Results: No shifts between microforms had occurred during the 34‐year period, but the number of observed taxa had decreased from 51 to 38. While the vegetation changed very little between 1988 and 2004, substantial changes occurred from 2004 to 2022. During this period, carpets showed a substantial increase in bare peat at the cost of cryptogam and vascular plant cover. Lawns showed a similar but less strong trend. Hummocks showed no such changes. The species composition of all microforms changed towards vegetation typical of wetter bog surfaces. In hummocks, this implied a shift from dominance by lichens to dominance by strongly peat‐producing Sphagnum species of section Acutifolia , coupled with an increase in rDWT. Conclusion: We demonstrate that bog vegetation can change substantially within two decades. The observed, divergent successions—retrogressive in carpets and lawns and progressive in hummocks—may result from the responses of the cryptogam layer to a combination of increased temperature and increased precipitation. Extreme weather events may have contributed to the increase in bare peat.

Measuring ecosystem gas fluxes is crucial to understanding the dynamics of ecosystem water and energy cycling. A common method to measure ecosystem gas fluxes (CO ₂ , CH ₄ , N ₂ O) and to compare experimental treatments is the use of closed‐loop chambers. However, the output data require extensive processing before analysis, making the workflow prone to bias and limiting comparability between studies. While there are various methods to process flux data, they lack reproducibility and automation. The fluxible R package provides a reproducible way to process raw gas concentration data from closed‐loop chambers into an analysis‐ready dataset of ecosystem gas fluxes. The processing steps include (1) separating the measurements, (2) fitting a linear, quadratic, or exponential model, (3) assessing the quality of the fit, (4) plotting the fluxes for visual inspection and (5) calculating the fluxes. In addition to reproducibility, fluxible focuses on homogeneity and automation in data processing, thus reducing the time investment for users while improving comparability across studies.

Individual plant size often determines the vital rates of growth, survival and reproduction. However, size can be measured in several ways (e.g. height, biomass, leaf length). There is no consensus on the best size metric for modelling vital rates in plants. Demographic datasets are expanding in geographic extent, leading to choices about how to represent size for the same species in multiple ecological contexts. If the choice of size variable varies among locations, inter‐population comparative demography increases in complexity. Here, we present a framework to perform size metric selection in large‐scale demographic studies. We highlight potential pitfalls and suggest methods applicable to diverse study organisms. We assessed the performance of five different size metrics for the perennial herb Plantago lanceolata , across 55 populations on three continents within its native and non‐native ranges, using the spatially replicated demographic dataset PlantPopNet. We compared the performance of each candidate size metric for four vital rates (growth, survival, flowering probability and reproductive output) using generalized linear mixed models. We ranked the candidate size metrics based on their overall performance (highest generalized R ² ) and homogeneity of performance across populations (lowest total magnitude of, and variance in, population‐level error). While all size variables performed well for modelling vital rates, the number of leaves (modelled as a discrete variable, without transformation) was selected as the best size metric, followed by leaf length. We show how to interrogate potential trade‐offs between overall explanatory power and homogeneity of predictions across populations in any organism. Synthesis . Size is an important determinant of vital rates. Using a dataset of unprecedented spatial extent, we find (a) consistent size‐based models of growth, survival and reproduction across native and non‐native populations of this cosmopolitan plant species and (b) that several tested size metrics perform similarly well. This is encouraging for large‐scale demographic studies and for comparative projects using different size metrics, as they may be robust to this methodological difference.

General ecosystem models (GEMs) are process‐based simulations of ecosystems functioning worldwide. They represent all essential processes in an ecosystem, going beyond a focus on specific biomes or organismal groups. GEMs are a comparably young development, and currently, there is still only one GEM available: the Madingley model, which was first announced in 2013. GEMs allow studying ecosystem mechanisms that are difficult or impossible to investigate empirically, and to perform simulation experiments to assess impacts of human pressures on ecosystems. As such, GEMs can be seen as the nature equivalent to ‘climate models'. As managers and policymakers urgently need good information on nature values and human impacts on them for policy design and decision‐making, they have much to gain from GEMs, which allow understanding current and predicting future outcomes of human actions on biodiversity and ecosystem functioning. To increase the applicability of GEMs for informing management and policy, we argue for an acceleration and diversification in model development, and we suggest a range of developments to propel GEMs forward not only towards both improved global representativeness but also towards applicability for more landscape‐level problems, a scale at which actual decision‐making often takes place. In addition, we argue for more accessible implementations and better documentation to increase both the transparency and credibility of GEMs, as well as the user basis applying GEMs. Practical implication. While some modelling developments target the global level, others are computationally only feasible for landscape‐level applications. We suggest that the development of GEMs should follow design principles employed in climate science and be structured so that they are modular and compatible in a standardized workflow.