No species left behind: borrowing strength to map data-deficient species - Shubhi Sharma; Kevin Winner; Laura J. Pollock; James T. Thorson; Jussi Mäkinen; Cory Merow; Eric J. Pedersen; Kalkidan F. Chefira; Julia M. Portmann; Fabiola Iannarilli; Sara Beery; Riccardo de Lutio; et al. (Trends in Ecology & Evolution, 2025)

Dominance and rarity in tree communities across the globe: Patterns, predictors and threats - Iris Hordijk; Lalasia Bialic‐Murphy; Thomas Lauber; Devin Routh; Lourens Poorter; Malin C. Rivers; Hans ter Steege; Jingjing Liang; Peter B. Reich; Sergio de‐Miguel; Gert‐Jan Nabuurs; Javier G. P. Gamarra; et al. (Global Ecology and Biogeography, 2024)
Aim Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species? Location Global. Time period 1990–2017. Major taxa studied Trees. Methods We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large‐scale land degradation. Main conclusions By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large‐scale management and conservation practices.

occTest : An integrated approach for quality control of species occurrence data - Josep M. Serra‐Diaz; Jeremy Borderieux; Brian Maitner; Coline C. F. Boonman; Daniel Park; Wen‐Yong Guo; Arnaud Callebaut; Brian J. Enquist; Jens‐C. Svenning; Cory Merow (Global Ecology and Biogeography, 2024)
Aim Species occurrence data are valuable information that enables one to estimate geographical distributions, characterize niches and their evolution, and guide spatial conservation planning. Rapid increases in species occurrence data stem from increasing digitization and aggregation efforts, and citizen science initiatives. However, persistent quality issues in occurrence data can impact the accuracy of scientific findings, underscoring the importance of filtering erroneous occurrence records in biodiversity analyses. Innovation We introduce an R package, occTest, that synthesizes a growing open‐source ecosystem of biodiversity cleaning workflows to prepare occurrence data for different modelling applications. It offers a structured set of algorithms to identify potential problems with species occurrence records by employing a hierarchical organization of multiple tests. The workflow has a hierarchical structure organized in test Phases (i.e. cleaning vs. testing) that encompass different testBlocks grouping different testTypes (e.g. environmental outlier detection ), which may use different testMethods (e.g. Rosner test, jacknife, etc.). Four different testBlocks characterize potential problems in geographic, environmental, human influence and temporal dimensions. Filtering and plotting functions are incorporated to facilitate the interpretation of tests. We provide examples with different data sources, with default and user‐defined parameters. Compared to other available tools and workflows, occTest offers a comprehensive suite of integrated tests, and allows multiple methods associated with each test to explore consensus among data cleaning methods. It uniquely incorporates both coordinate accuracy analysis and environmental analysis of occurrence records. Furthermore, it provides a hierarchical structure to incorporate future tests yet to be developed. Main conclusions occTest will help users understand the quality and quantity of data available before the start of data analysis, while also enabling users to filter data using either predefined rules or custom‐built rules. As a result, occTest can better assess each record's appropriateness for its intended application.

Integrated species distribution models to account for sampling biases and improve range‐wide occurrence predictions - Jussi Mäkinen; Cory Merow; Walter Jetz (Global Ecology and Biogeography, 2024)
Aim Species distribution models (SDMs) that integrate presence‐only and presence–absence data offer a promising avenue to improve information on species' geographic distributions. The use of such ‘integrated SDMs’ on a species range‐wide extent has been constrained by the often limited presence–absence data and by the heterogeneous sampling of the presence‐only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map‐based evaluation. We build new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs. Location South and Central America. Time Period 1979–2017. Major Taxa Studied Hummingbirds. Methods We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: (i) cross‐validation with presence–absence data and (ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set. Results The integrated SDM accounting for the spatially varying sampling intensity of the presence‐only data was one of the top performing models in both model validation schemes. Presence‐only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence‐only data for species which had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction. Main Conclusions Integrated SDMs combining presence‐only and presence–absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence–absence data.

An integrated approach to assessing abiotic and biotic threats to post‐fire plant species recovery: Lessons from the 2019–2020 Australian fire season - Rachael V. Gallagher; Stuart P. Allen; Berin D. E. Mackenzie; David A. Keith; Rachael H. Nolan; Libby Rumpff; Carl R. Gosper; Geoffrey Pegg; Stephen van Leeuwen; Mark K. J. Ooi; Colin J. Yates; Cory Merow; et al. (Global Ecology and Biogeography, 2022)
Aim Existing abiotic and biotic threats to plant species (e.g., disease, drought, invasive species) affect their capacity to recover post‐fire. We use a new, globally applicable framework to assess the vulnerability of 26,062 Australian plant species to a suite of active threats after the 2019–2020 fires. Location Australia. Time period 2019–2020. Major species studied Plants. Methods Spatial data for existing threats and information on species‐level susceptibility were combined with estimates of the extent of range burnt in southern Australia (> 22°S) to assign species against 10 criteria into vulnerability categories ( high , medium , low , none , data deficient ). We explore in detail results for three threats (drought, disease, feral animals), highlighting where impacts from multiple threats ranked high vulnerability may compound to reduce post‐fire recovery. Results Analysis of the full suite of 10 vulnerability criteria, which encompass a broad range of threats, revealed large numbers of species vulnerable to poor post‐fire recovery from one or more different hazards ( high vulnerability: 1,243 species; medium vulnerability: 2,450 species). Collectively, 457 plant species that burnt extensively (> 50%) across their range are highly vulnerable to poor recovery due to exposure to pre‐fire drought conditions (235 species), disease (186 species), or feral animals (97 species). Of these 457 species, 61 are vulnerable to more than one of these three threats, highlighting how a suite of interacting hazards can impact plant recovery after fire. Main conclusions While fire can renew plant populations by stimulating recruitment and resetting competitive interactions, the presence of existing threats in post‐fire landscapes jeopardizes recovery. The simultaneous impact of multiple threats that impact recovery can create a suite of hazards that contribute to declines and, potentially, extinction. Our method for rapid post‐fire vulnerability assessment can be applied to large numbers of plant species or other biota in fire affected regions globally.

A review of the heterogeneous landscape of biodiversity databases: Opportunities and challenges for a synthesized biodiversity knowledge base - Xiao Feng; Brian J. Enquist; Daniel S. Park; Brad Boyle; David D. Breshears; Rachael V. Gallagher; Aaron Lien; Erica A. Newman; Joseph R. Burger; Brian S. Maitner; Cory Merow; Yaoqi Li; et al. (Global Ecology and Biogeography, 2022)
Aim Addressing global environmental challenges requires access to biodiversity data across wide spatial, temporal and taxonomic scales. Availability of such data has increased exponentially recently with the proliferation of biodiversity databases. However, heterogeneous coverage, protocols, and standards have hampered integration among these databases. To stimulate the next stage of data integration, here we present a synthesis of major databases, and investigate (a) how the coverage of databases varies across taxonomy, space, and record type; (b) what degree of integration is present among databases; (c) how integration of databases can increase biodiversity knowledge; and (d) the barriers to database integration. Location Global. Time period Contemporary. Major taxa studied Plants and vertebrates. Methods We reviewed 12 established biodiversity databases that mainly focus on geographic distributions and functional traits at global scale. We synthesized information from these databases to assess the status of their integration and major knowledge gaps and barriers to full integration. We estimated how improved integration can increase the data coverage for terrestrial plants and vertebrates. Results Every database reviewed had a unique focus of data coverage. Exchanges of biodiversity information were common among databases, although not always clearly documented. Functional trait databases were more isolated than those pertaining to species distributions. Variation and potential incompatibility of taxonomic systems used by different databases posed a major barrier to data integration. We found that integration of distribution databases could lead to increased taxonomic coverage that corresponds to 23 years’ advancement in data accumulation, and improvement in taxonomic coverage could be as high as 22.4% for trait databases. Main conclusions Rapid increases in biodiversity knowledge can be achieved through the integration of databases, providing the data necessary to address critical environmental challenges. Full integration across databases will require tackling the major impediments to data integration: taxonomic incompatibility, lags in data exchange, barriers to effective data synchronization, and isolation of individual initiatives.

Plant spectral diversity as a surrogate for species, functional and phylogenetic diversity across a hyper‐diverse biogeographic region - Henry A. Frye; Matthew E. Aiello‐Lammens; Douglas Euston‐Brown; Cynthia S. Jones; Hayley Kilroy Mollmann; Cory Merow; Jasper A. Slingsby; Helga van der Merwe; Adam M. Wilson; John A. Silander (Global Ecology and Biogeography, 2021)
Aim With plant biodiversity under global threat, there is an urgent need to monitor the spatial distribution of multiple axes of biodiversity. Remote sensing is a critical tool in this endeavour. One remote sensing approach for detecting biodiversity is based on the hypothesis that the spectral diversity of plant communities is a surrogate of multiple dimensions of biodiversity. We investigated the generality of this ‘surrogacy’ for spectral, species, functional and phylogenetic diversity across 1,267 plots in the Greater Cape Floristic Region (GCFR), a hyper‐diverse region comprising several biomes and two adjacent global biodiversity hotspots. Location The GCFR centred in south‐western and western South Africa. Time period All data were collected between 1978–2014. Major taxa studied Vascular plants within the GCFR. Methods Spectral diversity was calculated using leaf reflectance spectra (450–950 nm) and was related to other dimensions of biodiversity via linear models. The accuracy of different spectral diversity metrics was compared using 10‐fold cross‐validation. Results We found that a distance‐based spectral diversity metric was a robust predictor of species, functional and phylogenetic biodiversity. This result serves as a proof‐of‐concept that spectral diversity is a potential surrogate of biodiversity across a hyper‐diverse biogeographic region. While our results support the generality of spectral diversity as a biodiversity surrogate, we also find that relationships vary between different geographic subregions and biomes, suggesting that differences in broad‐scale community composition can affect these relationships. Main conclusions Spectral diversity was shown to be a robust surrogate of multiple dimensions of biodiversity across biomes and a widely varying biogeographic region. We also extend these surrogacy relationships to ecological redundancy to demonstrate the potential for additional insights into community structure based on spectral reflectance.

Climate change risk to southern African wild food plants - Carina Wessels; Cory Merow; Christopher H. Trisos (Regional Environmental Change, 2021)

Species' range model metadata standards: RMMS - Cory Merow; Brian S. Maitner; Hannah L. Owens; Jamie M. Kass; Brian J. Enquist; Walter Jetz; Rob Guralnick (Global Ecology and Biogeography, 2019)
Aim The geographic range and ecological niche of species are widely used concepts in ecology, evolution and conservation and many modelling approaches have been developed to quantify each. Niche and distribution modelling methods require a litany of design choices; differences among subdisciplines have created communication barriers that increase isolation of scientific advances. As a result, understanding and reproducing the work of others is difficult, if not impossible. It is often challenging to evaluate whether a model has been built appropriately for its intended application or subsequent reuse. Here, we propose a standardized model metadata framework that enables researchers to understand and evaluate modelling decisions while making models fully citable and reproducible. Such reproducibility is critical for both scientific and policy reports, while international standardization enables better comparison between different scenarios and research groups. Innovation Range modelling metadata (RMMS) address three challenges: they (a) are designed for convenience to encourage use, (b) accommodate a wide variety of applications, and (c) are extensible to allow the research community to steer them as needed. RMMS are based on a metadata dictionary that specifies a hierarchical structure to catalogue different aspects of the range modelling process. The dictionary balances a constrained, minimalist vocabulary to improve standardization with flexibility for users to modify and extend. To facilitate use, we have developed an R package, rangeModelMetaData , to build templates, automatically fill values from common modelling objects, check for inconsistencies with standards, and suggest values. Main conclusions Range Modelling Metadata tools foster cross‐disciplinary advances in biogeography, conservation and allied disciplines by improving evaluation, model sharing, model searching, comparisons and reproducibility among studies. Our initially proposed standards here are designed to be modified and extended to evolve with research trends and needs.

Serotiny in the South African shrub Protea repens is associated with gradients of precipitation, temperature, and fire intensity - Roland C. de Gouvenain; Jeremy J. Midgley; Cory Merow (Plant Ecology, 2019)

Integrating occurrence data and expert maps for improved species range predictions - Cory Merow; Adam M. Wilson; Walter Jetz (Global Ecology and Biogeography, 2017)
Aim Knowledge of species geographical distributions is critical for many ecological and evolutionary questions and underpins effective conservation decision‐making, yet it is usually limited in spatial resolution or reliability. Over large spatial extents, range predictions are typically derived from expert knowledge or, increasingly, species distribution models based on individual occurrence records. Expert maps are useful at coarse resolution, where they are suitable for delineating unoccupied regions. In contrast, point records typically provide finer‐scale occurrence information that can be characterized for its environmental association, but usually suffers from observer biases and does not representatively or fully address the geographical or environmental range occupied by a species. Innovation We develop a new modelling methodology to combine the complementary informative attributes of both data types to improve fine‐scale, large‐extent predictions. We use expert delineations to constrain predictions of a species distribution model parameterized with incidental point occurrence records. We introduce a maximum entropy approach for combining the two data types and generalize it to Poisson point process models. We illustrate critical decision making during model construction using two detailed case studies and illustrate features more generally with applications to species with vastly different range and data attributes. Our methods are illustrated in the Supporting Information and with a new R package, bossMaps, that integrates with existing generalized linear modelling and Maxent software. Main conclusions Our modelling strategy flexibly accommodates expert maps with different levels of bias and precision. The approach can also be useful with other coarse sources of spatially explicit information, including habitat associations, elevational bands or vegetation types. The flexible nature of this methodological innovation can support improved characterization of species distributions for a variety of applications and is being implemented as a standard element underpinning integrative species distribution predictions in the Map of Life ( https://mol.org/ ).

Improving niche and range estimates with Maxent and point process models by integrating spatially explicit information - Cory Merow; Jenica M. Allen; Matthew Aiello‐Lammens; John A. Silander (Global Ecology and Biogeography, 2016)
Aim Accurate spatial information on species occurrence is essential to address global change. Models for presence‐only data are central to predicting species distributions because these represent the only geographical information available for many species. In this paper we introduce extensions to incorporate a variety of types of additional spatially explicit sources of information in Maxent and Poisson point process models. This spatial information comes from the output of other statistical or conceptual models. Innovation Our approach relies on minimizing the relative (or cross) entropy (known as Minxent) between the predicted distribution and a prior distribution. In many scenarios, researchers have some additional information or expectations about the species distribution, such as outputs from previous models. Here, we show how to use this information to improve predictions of both niche models and spatial distributions, depending on what types of spatially explicit prior information is available and how it is incorporated in the model. Main conclusions We illustrate applications of Minxent that include models for sampling bias, explicitly incorporating dispersal/other ecological processes, combining native and invasive range data, incorporating expert maps, and borrowing strength across taxonomic relatives. These applications focus on addressing biological scenarios where range modelling is extremely challenging – non‐equilibrium species distributions and rare and narrowly distributed species – due to data limitations. When data are limited, we are typically forced to make informal assumptions or lean on predictions of other models in order to obtain useful predictions; our applications of Minxent provide a formal way of describing these assumptions and connections to other models.