Ecography

1. Regional species distribution models (SDM) calibrated over spatially restricted extents tend to truncate species' ecological niches. Existing nested SDM workflows integrate multi-scale information through sequential combination, which limits formal uncertainty propagation across scales and prevents regional predictions from being explicitly constrained within globally-informed niche boundaries. 2. We introduce sabinaMBM, an R package implementing joint multiscale Bayesian SDM within a unified probabilistic framework built on inlabru/R-INLA. It propagates uncertainty across scales without the computational bottlenecks of MCMC-based approaches. The framework offers multiple coupling architectures ranging from complete independence to hierarchical constraint that can be configured independently for intercepts and covariates. 3. In a range-margin population, hierarchical constraint most improves out-of-sample discrimination where regional data were scarcest, while leaving predictions unchanged where they already suffice, delivering gains precisely where sequential approaches are expected to struggle most. Applied to Quercus petraea across its Iberian trailing-edge, including a spatial field produced the largest single performance gain, consistent across every coupling configuration, and covariate responses diverged by scale for at least one climatic predictor. Under future climate, the constrained model yielded lower suitable habitat estimates and redistributed uncertainty in proportion to cross-scale agreement rather than uniformly. 4. sabinaMBM makes multiscale Bayesian inference accessible without specialist programming. This framework provides robust value for trailing-edge populations and spatial (invasive species) or temporal (climate change) projections where niche truncation risks ecologically implausible outcomes, while simultaneously delivering fine-resolution predictions with properly propagated uncertainty whenever global and regional covariates offer complementary information.

Retrospective ecological niche modeling (rENM) combines historical species occurrence records with historical environmental data to reconstruct the spatio-temporal dynamics of species-environment relationships under changing conditions. Despite growing recognition that those relationships can be nonstationary, time-series approaches to ecological niche modeling remain uncommon, and the tools to support them at scale are limited. Here, we describe the rENM Framework, an experimental, open-source suite of R packages that automates a complete rENM workflow spanning data preparation, ensemble time-series construction, trend analysis, AI interpretation, and report generation. The framework integrates eBird occurrence records with environmental variables derived from NASAs MERRA-2 reanalysis across a 45-year study period (1980-2024) and executes a complete analysis for any species with eBird data through a single function call. By treating climatic suitability as a dynamic ecological response surface rather than a static baseline, the framework produces the following analytical products that complement conventional ecological niche modeling approaches: suitability time series, long-term trend and acceleration maps, centroid displacement estimates, bioclimatic velocity metrics, variable contribution trajectories, and hotspot analyses identifying areas of accelerating suitability decline. We illustrate the frameworks outputs with a representative run for Cassins Sparrow (Peucaea cassinii), a grassland species of conservation concern in the arid southwestern United States and the focal species throughout our development work. The frameworks automated, unsupervised pipeline makes systematic application across large numbers of species tractable, with direct implications for conservation assessments, such as State Wildlife Action Plans, where species-specific analytical capacity is often limited by available resources. The rENM Framework is openly available on GitHub and archived on Zenodo.

Species distribution models are widely used for the evaluation and analysis of species spatial distributions and responses to environmental changes. Due to this, there's a wide diversity of tools and algorithms to perform these types of analyses. However, despite being a mature field of study, SDM still requires a large amount of research intervention and decision-making to produce reliable models. This is a natural consequence of each species unique response to environmental factors, but in practice it hinders the potential of SDMs to be used effectively and with confidence across large sets of species. Here we present the AutoMaxEnt repository, a collection of functions and tools for the automation of maximum entropy species distribution models. AutoMaxEnt offers a flexible environment to model species distribution models, automating most of the data pre-processing and preparation and giving the user the possibility of exploring multiple model scenarios automatically or with control over how those scenarios are generated. AutoMaxent offers model fit control, multiple background points generation algorithms, automatic model and variable selection, and study area configuration, among other parameters. AutoMaxEnt allows for the automation of model fitting, evaluation and model selection across different species, study areas and time periods.

Understanding the ecological determinants of species ranges is a central goal of ecology. Novel tools like global datasets and machine learning models allow us to describe species ranges with increasing scope and accuracy, and to develop and test ecological hypotheses about their determinants. Here, I focus on the widespread nuisance mosquito and dog heartworm vector Aedes sierrensis, a tree hole-breeding mosquito that is widespread and abundance within its native range in western North America, and develop species distribution models (SDMs) to characterize the species range and its environmental determinants. I find that the species range is highly predictable from long-term average bioclimatic variables. Temperature and precipitation were the primary determinants: suitability was highest at wet-season average temperatures of 0 - 10{degrees}C, minimum temperatures of -5 - 5{degrees}C, and summer temperatures of 8 - 22{degrees}C in environments with adequate seasonal rainfall concentrated in the winter. After accounting for climate, land cover variables showed minimal importance for prediction, but suitability was higher in forests and outside of urban areas. The results are consistent with ecological knowledge of the Ae. sierrensis life cycle from field observations and previous laboratory experiments, suggesting that individual physiological constraints scale up to determine species distributional limits.

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009-2018 Major Taxa Studied: Whooping Crane (Grus americana) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

Climate affects biological processes and organisms. It is extensively recorded, and its effects are frequently studied. Climatic variables are recorded as time series of measurements, but these are often summarized as long-term averages, throwing away biologically relevant information. Subsequent analyses can therefore miss important climate effects or suffer from low predictive power. Here, we propose a new method, StatClim, inferring the most informative climatic summary from the data. Instead of taking a fixed arbitrary summary of a time series, StatClim automatically estimates the beginning and width of a climatic window and simultaneously calculates data aggregation, which best predicts a response variable. We implemented the proposed approach as a Bayesian hierarchical model in Stan. We illustrated the potential of StatClim on published data on mast seeding of Fagus sylvatica. StatClim correctly identified known climatic windows relevant for this tree species, but also uncovered that mast seeding is triggered by high temperatures within these windows. We demonstrated flexibility of the model to account for complex data structures, we ran power analysis showing the ability of the model to identify climatic effects even with modest datasets, and we evaluated its false positive rate, which is lower than 0.01. We discuss the advantages and limitations of StatClim. Compared to existing alternatives, it is suitable for hypothesis testing and offers important advances, such as estimation of the type of aggregation. In combination with existing methods, StatClim will facilitate data-driven use of climatic and microclimatic data and therefore discovering so far unknown climatic effects on biological processes and ecosystem functioning.

Climate influences community assembly by constraining the conditions under which species can persist, but the consistency of macroecological processes across different ecosystems remains unclear. Interspecific variation in climatic niche properties that govern community assembly across climate gradients can be gleaned from species distributions. Species-climate associations, niche properties, and resulting assembly remains poorly understood for plants in tropical dry ecosystems, and among different life-forms. We examined how species-climate associations governed community assembly of four plant life-forms across 60,000 km2 of tropical dry ecosystem in peninsular India. In the 1750 km long Eastern Ghats mountain range, we surveyed vegetation in 2500 20 x 50 m plots. Across the north-south climatic gradient from cool, wet and seasonal sites to warm, dry and less-seasonal sites, from presence-absence data of 1601 species in four life forms (trees, shrubs, herbs, climbers), we performed Bayesian Hierarchical Modelling of Species Communities to predict conditions of occurrence for each species. From this, we derived species climatic niche optima and niche width, and examined the local-level prevalence of niche properties across the climate gradient to glean assembly. Most tree, shrub and climber species associated with warm-dry conditions had wider niches than species associated with cool-wet conditions, which also resulted in assemblages having wider niches at warmer sites. Herbaceous species, by contrast, had narrower niches when associated with warmer conditions, where narrow-niche species dominated the assemblage. In all life-forms, however, assemblages in both warm-dry and cool-humid conditions consisted of species primarily associated with and preferring (having their optima in) those conditions, suggestive of stress dominance. Further evidence for stress dominance in shrubs and climbers came from assemblages in the warmest and coolest sites having low richness and comprising mainly of species with wider niches. Tree richness increased at either end of the gradient, while herb richness increased in warm-dry conditions. Overall, our results suggest that plant life-forms differ in the processes driving assembly across broad climate gradients in a tropical dry ecosystem, but many herbs were specialized to warm-dry climates. To understand life-form-specific responses to environmental gradients in tropical dry ecosystems, future work should incorporate traits and evolutionary history.

Island biogeography is the theoretical and empirically validated expectation that an islands biodiversity is ultimately limited by its size and isolation, with larger and less isolated (from mainland communities) islands supporting greater diversity. Island biogeography is generally applied to simple metrics of diversity such as species richness (number of different species); however, fewer studies have used traits to measure biodiversity. An organisms traits -- e.g., body mass, age at sexual maturity, trophic level -- can be used to measure biodiversity and understand how ecological communities function. We quantified species richness for birds, mammals, reptiles, and amphibians, and functional diversity for birds and mammals (for which sufficient trait data were available), and tested whether this diversity can be predicted using the theory of island biogeography. We identified 9,103 Australian islands, of which 1,661 had at least one (native and/or non-native) non-marine species present according to the Atlas of Living Australia. As expected, tetrapod species richness (S) increased with island area (A) (z = 0.299 {+/-} 0.012) following a typical power-law relationship (i.e., S = cAz, where z = 0.2-0.4), but was not predicted by distance from mainland -- consistent with the pattern observed on other recently (< 10,000 years) isolated continental islands. We found that trait richness increased at the same rate with island area as species richness for mammals, but for birds, trait richness increased more slowly than species richness. Trait turnover increased modestly with inter-island distance, whereas trait nestedness was unrelated to distance. Trait richness increased strongly with species richness in both birds and mammals, and island area and isolation explained no additional variation in functional richness after accounting for species richness. These results indicate that island geography influences functional diversity primarily through species accumulation, rather than through direct effects on occupied trait space. Overall, the trait space of smaller islands tended to be nested within that of larger, nearby islands; the main differences among similar-sized islands are due to turnover (change in species/trait combinations among assemblages), and the effects are more pronounced in mammals compared to birds. We also found evidence for an asymptotic relationship between trait and species turnover in both birds and mammals, suggesting close coupling between taxonomic and functional turnover, with some saturation of trait turnover at high species turnover. Large islands that are simultaneously more isolated might offer conservation advantages by reducing the influence of threatening processes on the mainland if distance limits access of people and invasive species.

Understanding the limits of species distributions is a central objective of biogeography and macroecology and has become increasingly important as climate change drives rapid shifts in geographic ranges. Species range sizes follow a highly skewed frequency distribution, with most species occupying ranges orders of magnitude smaller than those of the most widespread species. Range sizes also exhibit pronounced geographic patterns, with small-ranged species concentrated near continental margins and other geographic boundaries. No universally accepted explanation has been proposed for these patterns. Here we present a simple geometric model showing that species range size patterns emerge from the random placement of dispersal barriers within continental domains. The model predicts both the observed frequency distribution and the spatial distribution of range sizes across amphibians, birds, and mammals. It therefore provides a first-order explanation for global patterns of species rarity and can be refined by incorporating elevational barriers and spatial variation in species richness. Our findings suggest that species range size is constrained by the geometry of dispersal barriers and the geographic domain, with proximity to domain boundaries acting as a primary determinant of species rarity. These results have important implications for understanding species' evolutionary potential and vulnerability to extinction.

Evidence is accumulating that global change is altering species distributions. Yet, detailed knowledge is missing about the relative and joint contribution of different drivers to observed species responses. Here, we implemented an impact attribution framework based on counterfactual simulations to assess the impact of climate and land use change on occupancy dynamics of North American breeding birds. We used a Bayesian framework to fit process-explicit dynamic occupancy models to long-term survey data for 159 species from 1995 to 2019, and quantified predictive performance using spatial and temporal cross-validation. We then assessed the relative importance and effect direction of climate and land use change while accounting for model predictive accuracy. Results indicate that climate change negatively affected 90 % of the species and land use change negatively impacted 96 %. Climate change emerged as more important than land use change for driving changes in occupancy across species. Remarkably, the effects of both drivers were mostly antagonistic rather than acting additively or synergistically. Climate was the most important driver for bird communities in the western USA, while land use change dominated in the southeast, and combined climate and land use change in the northeast. Our analysis demonstrates that recent changes in North American bird distributions are shaped by multiple global change drivers acting in concert. The effect of recent climate and land use change were mostly antagonistic, and thus trends in bird occupancy dynamics could not be understood by studying the impact of those drivers in isolation. By disentangling the effects of climate and land use change on biodiversity trends, impact attribution approaches can improve our understanding of global change impacts and can support conservation planning and more accurate and realistic projections of biodiversity response to global change.

Globalization is redistributing species worldwide, yet whether the traits and origins of non-native fauna have changed through time remains unclear. We combined global first-record data for non-native species from 1800-2019 with harmonized information on body size, diet, habitat use, native range characteristics, and climatic niche characteristics for 1,910 non-native mammals, birds, reptiles, amphibians, and freshwater fishes. Across most groups, species recorded earlier originated disproportionately from higher latitudes, occupied broader native ranges, and had wider thermal niches. More recent first records increasingly involve species from warmer, lower-latitude regions with smaller and more restricted native distributions. Body size also declined through time in several groups, whereas shifts in diet and habitat use were more taxon-specific. Temporal changes in introduction pathways partly explained these patterns: declines in deliberate release and production-related pathways, together with increases in pet and ornamental pathways, were associated with shifts toward smaller-bodied, lower-latitude, and more range-restricted species. These results indicate that the functional and biogeographic composition of non-native vertebrates has been progressively reshaped over the past two centuries, weakening the historical dominance of widespread temperate species and increasingly incorporating tropical and range-restricted fauna into global redistribution. Anticipating future biological invasions will therefore require attention not only to the number of species being transported, but also to how the characteristics and pathways of transported species are changing through time.

AimTo test whether dispersal ability determines the relative importance of environmental filtering and distinct dispersal pathways in structuring stream macroinvertebrate metacommunities. LocationNzoia River catchment, Lake Victoria basin, western Kenya. TaxonBenthic macroinvertebrates (family level), grouped by dispersal ability into strong fliers, weak fliers, passive aerial dispersers, and obligate aquatics. MethodsMacroinvertebrates were sampled seasonally (2018 to 2021) at 55 sites. Using distance-based redundancy analysis and variation partitioning, we compared the influence of physicochemical water quality on community structure, representing environmental filtering, with the influence of three sets of spatial factors representing distinct dispersal pathways: Euclidean distance (overland dispersal pathway), land-use resistance (land-use mediated dispersal pathway), and Asymmetric Eigenvector Maps built on river-network connectivity (along-watercourse dispersal pathway). In addition, distance-decay relationships and turnover-nestedness partitioning were examined for the full assemblage and each dispersal group. ResultsCommunity assembly shifted predictably along the macroinvertebrate dispersal-ability gradient. Environmental filtering dominated in strong fliers and retained an independent influence in weak fliers and obligate aquatics, but contributed no independent influence in passive aerial dispersers. The overland dispersal pathway structured the three aerial groups. The land use mediated dispersal pathway constrained weak fliers and passive aerial dispersers, and the along-watercourse dispersal pathway structured passive aerial dispersers and obligate aquatics. Passive aerial dispersers showed the strongest dispersal signal, with the overland and along-watercourse pathways contributing equally, and in this group beta diversity reflected richness gradients (nestedness) rather than species replacement. Main conclusionsDispersal ability determined whether environmental filtering or a specific dispersal pathway governed community composition, and no single pathway captured assembly across the full range of dispersal abilities. Dispersal traits and connectivity must therefore be considered jointly when studying and managing aquatic biodiversity in human-modified Afrotropical catchments.

Equivalent numbers represent biodiversity as the effective number of equally distinct units, typically species, and can be partitioned across scales. In practice, they summarize each unit of biodiversity by a single value and compare units pairwise, misrepresenting units that are better described as distributions and the relationships between several units that share the same space. We introduce an equivalent-number index for assemblages of units represented as probability density functions (PDFs) over a continuous space, estimated as the integral of the pointwise maximum across abundance-weighted PDFs. Resulting equivalent PDF numbers fulfil elementary properties of classical equivalent numbers, and support additive partitioning across any number of nested scales. We illustrate the framework with case studies across three domains: (1) measuring trait diversity considering intraspecific variability in grasslands, (2) partitioning realized bioclimatic niches among clades of Carnivora, and (3) understanding seasonal changes in the partitioning of fish home ranges in geographic space.

Translating Tilman resource-ratio model (R) to the metacommunity scale bridges local plant competition and landscape dynamics, moving ecological niche theory beyond controlled experiments. By incorporating scale dependence, stochasticity, dispersal limitation, and the stress-disturbance dichotomy, this framework also accounts for the drivers of both alpha and beta diversity. To achieve this integration, this work presents the Metacommunity Resource-Ratio Model (MetcommR) and its graphical tool, the Patchwork Biplot. This approach adapts the traditional continuous plane with a granular patch space, where each patch represents a discrete community with its area and internal variability, tracking net vectors of resource consumption and release relative to species-limiting isoclines. MetcommR articulates two fundamental regimes. In disturbance-governed metacommunities, biomass loss releases resources, interrupting depletion and preventing competitive exclusion, which promotes coexistence via the competition-colonization trade-off and maintains alpha and beta diversity. Conversely, in stress-governed systems, biomass accumulation and severe resource scarcity push communities toward isoclines, favoring the monodominance of tolerant species through competitive exclusion under the tolerance-fecundity trade-off. We demonstrate the model's empirical utility through a Cerrado case study in the Paraopeba Reserve. Analyzing canopy openness (light) and soil nitrogen reveals that long-term fire suppression induces light stress, elevating competitive exclusion risks for shade-intolerant species. Consequently, the Patchwork Biplot serves as a tool for evaluating ecological dynamics and vulnerabilities to guide management and conservation strategies.

1. Large-scale community-composition datasets, especially from microbiome studies, increasingly provide opportunities to identify major community compositional types (e.g., enterotypes in human microbiomes) and potential transitions depending on environmental factors. Energy landscape analysis based on maximum entropy models has emerged as a promising framework for characterizing such multi-stability in ecological communities. However, its application to diverse, high-dimensional compositional datasets remains limited by computational inefficiency, insufficient evaluation of predictability, and lack of systematic assessment of uncertainty. 2. Here, we present a computationally tractable inference framework for energy landscape analysis of multispecies communities, implemented in the R package ELAplus. We introduce a framework combining cross-validation-based selection of optimization settings, enabling accurate and computationally efficient model fitting across a wide range of simulated community datasets. In addition, we incorporate a bootstrap-based approach to quantify the reliability of inferred stable states, providing a systematic measure of uncertainty in landscape structures. 3. Simulation analyses demonstrate improved predictive performance and robustness compared to existing implementations. Applications to empirical datasets further illustrate how the framework can reveal stable states, basins of attraction, and potential tipping points under varying environmental conditions. The package also provides visualization tools, including disconnectivity graphs and energy surface plots, to facilitate intuitive interpretation of complex ecological landscapes. 4. Our framework enables robust and computationally efficient inference of ecological stability from compositional and environmental data, expanding the applicability of energy landscape approaches in diverse natural communities.

Ecologists have long investigated how community complexity affects ecological stability, yet how community complexity influences multistability, defined as the presence of alternative stable states, remains poorly understood. We developed a novel framework integrating stochastic community assembly with stability landscape analysis to quantify multistability from species interaction matrices. Using this framework, we systematically explored how species interaction properties shape the relationship between species diversity (species pool size) and the number of alternative stable states. Mean interaction strength was the primary determinant: competitive interactions amplified the positive relationship between species diversity and the number of alternative stable states. In competitive communities, a greater number of alternative stable states was associated with lower community uncertainty, a measure of the long-term unpredictability of community assembly dynamics. These results highlight the importance of characterizing the entire stability landscape. Our framework provides a general approach for understanding and quantifying multistability in complex ecological communities.

Body size is the principal trait governing trophic interactions in freshwater fish communities, yet the body size data available for most species are a species-level maximum. Whether this global proxy can predict trophic links in systems the model has never seen is untested. We evaluated four models on 37 freshwater fish food webs under leave-one-study-out cross-validation (LOSO-CV), predicting each held-out network from species traits with no observed interactions. Locally measured body mass ratios predicted links accurately (median ROC-AUC 0.973). FishBase traits predicted fish-associated links nearly as well (0.887 against 0.912 on the same networks). Whole-network scores were lower (0.607); the shortfall was confined to pairs between non-fish taxa, which FishBase does not describe. A habitat overlap score from FishBase depth-stratum categories added no signal, and a graph attention network did not outperform the random forest. For held-out systems within the range of the training corpus, fish-associated links are recovered from globally available traits alone. This did not extend to the three out-of-region networks, where prediction dropped to near or below chance. Cross-region transfer will need a larger and more geographically varied set of fish food webs.

Species are dramatically shifting their geographical ranges, yet the consequences of these spatial redistributions on species interactions have not been examined at broad spatial and temporal scales. Here, we investigate how the redistribution of species abundance over the past 50 years affects the potential for predator-prey interactions among North American birds. We first predict potential predator-prey interactions based on existing data and functional traits, then construct a novel, abundance-weighted metric of interaction potential for each pair of predator and prey in local communities. Across North America, the potential for predator-prey interactions has been increasing over time. We further show that this widespread increasing trend in interaction potentials is mostly driven by higher abundances of predator species. The spatial redistributions of predators and prey lead to turnover of interactions within local communities, and the turnover is less associated with changes in climate but more associated with human-induced land use changes. Our novel approach and results highlight the importance of considering traits, abundance, and species interactions - including known and potential interactions - when studying the ecological consequences of species range shifts.

1. Occurrence records underpin biodiversity analyses, but their coordinates are often erroneous or missing. Misplaced coordinates distort environmental predictors and range estimates used in conservation assessment. Existing tools flag suspect records or georeference locality text, but rarely correct coordinates, provide calibrated uncertainty, or quantify downstream consequences. 2. We present GeoMend, a deterministic, rule-based method that flags and corrects suspect coordinates and georeferences records lacking coordinates from locality text, assigning each a precision tier or uncertainty radius. It uses no machine learning or artificial intelligence. We evaluate it against expert coordinates for 16,913 records and 736 species on Hispaniola, compare georeferencers using 5,000 localities from 72 countries, and measure changes in environmental predictors and the extent of occurrence. 3. On held-out Hispaniola records, GeoMend flags 92% of coordinates more than 5 km from expert truth, versus 27% for CoordinateCleaner. Among the accepted corrections with paired coordinates, the median error falls from 2.19 to 0.82 km, and 68% of the complete held-out pairs improve. On records placed by both GeoMend and GEOLocate that also have original coordinates, the median error is 0.67 km for GeoMend versus 10.69 km for GEOLocate. For records lacking coordinates, evaluated uncertainty radii contain expert truth about 92% of the time in well-supported gazetteer classes. Correction reduces the mean standardized environmental distance from expert truth by 38% and moves the extent of occurrence toward expert truth by a median of 8% (mean of 79%). Corrections and newly georeferenced records give 17 Hispaniola species their first range estimate. 4. On the global benchmark, GeoMend is more accurate than GPT-4o for two-thirds of the records placed by all four comparators, with median errors of 4.4 and 10.8 km, respectively. Applied to Global Biodiversity Information Facility (GBIF) records lacking coordinates, it places records from 82,180 previously unmapped species across 233 countries, ranging from precise locality interpretations to administrative centroids. Automated placement makes the global backlog more approachable, while explicit tiers distinguish uses supported by precise and coarse coordinates. Further georeferencing requires regional expertise to combine automated placement with local knowledge.

The ocean tides influence key Earth system processes at a range of spatial and temporal scales. It is known that the geometry of ocean basins is the leading controller of tidal energetics, so well-constrained palaeogeographic reconstructions and tidal properties for Earths past are imperative when investigating other Earth system processes. Here, we present a novel way to constrain both deep-time tidal model results and reconstructions, by combining palaeoecology with sedimentology. We compare new palaeo-tidal model simulations for the Cambrian period, significant for the early origin and radiation of major animal fauna, to tidal proxies. One of the most abundant soft-bodied organisms preserved during this time are cnidarian medusae (jellyfish). A total of 17 cnidarian medusae localities were obtained through the literature, which had an adequate global distribution and occurred at regular intervals throughout the period of study. In some locations there were also estimates of palaeo-tidal range. Our results show a good agreement between the simulations and proxy data. In the few locations where there is disagreement, it is proposed that the palaeogeographic reconstructions are missing details, e.g., island chains, and our results allow for the palaeogeographic reconstructions to be improved. The proxy method presented is promising and can be applied to other time-periods with different marine fossils, particularly at evolutionary and extinction periods where the marginal marine environment is of importance.