Outstanding Challenges in the Transferability of Ecological Models

Temporal and spatial equivalence in demographic responses of emperor penguins (Aptenodytes forsteri) to environmental change

Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce. Here, we present a case study demonstrating both space-for-time and time-for-space substitutions (TFSS) using emperor penguins (Aptenodytes forsteri) as the focal species. Using an abundance-based species distribution model (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long-term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average colony abundance distribution across 50 colonies. Our analysis reveals that the distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. Space-for-time substitution performance of the aSDM, as measured by the Pearson correlation coefficient, was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar TFSS performance when predicting the abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58. We posit that such space-time equivalence arises because: (1) emperor penguin colonies conform to their existing fundamental niche; (2) there is not yet any environmental novelty when comparing the spatial versus temporal variation of distance to the nearest open water; and (3) models of more specific components of life histories, such as fledgling abundance, rather than total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.

Climate mediates the predictability of threats to marine biodiversity

Anthropogenic climate change is driving rapid changes in marine ecosystems across the global ocean. The spatiotemporal footprints of other anthropogenic threats, such as infrastructure development, shipping, and fisheries, will also inevitably shift under climate change, but we find that these shifts are not yet accounted for in most projections of climate futures in marine systems. We summarise what is known about threat-shifting in response to climate change, and identify sources of predictability that have implications for ecological forecasting. We recommend that, where possible, the dynamics of anthropogenic threats are accounted for in nowcasts, forecasts, and projections designed for spatial management and conservation planning, and highlight key themes for future research into threat dynamics in a changing ocean.

Integrating multiple evidence streams to understand insect biodiversity change

Insects dominate animal species diversity yet face many threats from anthropogenic drivers of change. Many features of insect ecology make them a challenging group, and the fragmented state of knowledge compromises our ability to make general statements about their status. In this Review, we discuss the challenges of assessing insect biodiversity change. We describe how multiple lines of evidence-time series, spatial comparisons, experiments, and expert opinion-can be integrated to provide a synthesis overview of how insect biodiversity responds to drivers. Applying this approach will generate testable predictions of insect biodiversity across space, time, and changing drivers. Given the urgency of accelerating human impacts across the environment, this approach could yield a much-needed rapid assessment of insect biodiversity change.

Population Genetics Meets Ecology: A Guide to Individual-Based Simulations in Continuous Landscapes

Individual-based simulation has become an increasingly crucial tool for many fields of population biology. However, continuous geography is important to many applications, and implementing realistic and stable simulations in continuous space presents a variety of difficulties, from modeling choices to computational efficiency. This paper aims to be a practical guide to spatial simulation, helping researchers to implement individual-based simulations and avoid common pitfalls. To do this, we delve into mechanisms of mating, reproduction, density-dependent feedback, and dispersal, all of which may vary across the landscape, discuss how these affect population dynamics, and describe how to parameterize simulations in convenient ways (for instance, to achieve a desired population density). We also demonstrate how to implement these models using the current version of the individual-based simulator, SLiM. We additionally discuss natural selection-in particular, how genetic variation can affect demographic processes. Finally, we provide four short vignettes: simulations of pikas that shift their range up a mountain as temperatures rise; mosquitoes that live in rivers as juveniles and experience seasonally changing habitat; cane toads that expand across Australia, reaching 120 million individuals; and monarch butterflies whose populations are regulated by an explicitly modeled resource (milkweed).

Spatial Change of Dominant Baltic Sea Demersal Fish Across Two Decades

The arange and biomass distribution of marine fish species offer insights into their underlying niches. Quantitative data are rare compared to occurrences and remain underused in species distribution models (SDMs) to explore realized niches-the actual space occupied by a species shaped by abiotic and biotic factors. Local densities drive differences in species contributions to ecological processes and ecosystem function rather than through presence alone. If a species growth rate is strongly controlled by macro-environmental conditions, then predicting geographical abundance or densities should be possible. We collated 20 years (2001-2020) of standardized scientific bottom trawl data to fit several versions of hierarchical generalized additive models using biomass (kg km-2) of four dominant demersal species (Common dab, European flounder, European plaice, Atlantic cod) within yearly and seasonal (winter and autumn) time windows. Covariates were represented with trawl-level geographic information (position, depth) and high-resolution oceanographic features. This work illustrates species-specific spatiotemporal biomass patterns across two decades and demonstrates superior predictive performance with seasonally variable smoothing terms, revealing seasonally different responses to oceanographic predictors. Firstly, we find relative stasis in Common dab biomass which is linked to the macro-environmental salinity gradient in the western Baltic Sea but with different temperature responses across seasons. Secondly, we show both European flounder and plaice have increased in biomass in the western Baltic Sea with different seasonal relationships to bottom temperature, and that flounder switches between salinity conditions based on season during spawning/feeding periods. Lastly, both juvenile and adult Atlantic cod life stages are shown to have declined most significantly in the Bornholm Deeps and the Gdańsk Deeps. For cod, we conclude that biomass was less reliably predicted in comparison to the other major Baltic demersals studied here, warranting dynamic fishing covariates as a formerly major commercial fishing target. These models approach more dynamic species distribution models and are increasingly valuable to constrain uncertainties in biogeographic forecasting which often rely on annually-averaged response curves, occurrence data, and suitability maps which rarely discriminate between areas of high and low biomass areas in space and time.

Temporal adjustment approach for high-resolution continental scale modeling of soil organic carbon

Open-source legacy data available for training soil organic carbon (SOC) models are limited and not uniformly distributed in space or time. While some process-based models predict SOC changes, most of the large-scale data-driven SOC modeling efforts overlook temporal shifts. Accounting for the expected temporal drift allows us to increase the accuracy of dataset available for machine learning models. Here we present an approach for creating proximity-based distance matrices using the legacy data available in contiguous US (CONUS) and generating spatially resolved temporal shift projections that adjust observations to the target date. The approach was evaluated by comparing SOC observations projected to two reference years, SOC1980 and SOC2020 and without temporal adjustment (SOCno-adj). Stocks of SOC projections showed significant differences between SOCno-adj and SOC2020. Baseline estimate of SOC stocks in CONUS croplands (top 1 m) were higher based on SOCno-adj (14.49 Pg C) compared to SOC2020 (13.29 Pg C), for pasture lands 15.49 Pg (SOCno-adj) and 14.22 Pg C (SOC2020), for forest lands at 39.52 Pg C (SOCno-adj) and 40.83 Pg C (SOC2020). The study results confirmed the validity of our methodology, and its capability to enhance SOC stock projections effectively with temporal adjustments. Potential users of this study's outcomes include many stakeholders involved in carbon incentive programs, including farmers, scientists, policy makers, and industry partners.

Advancing terrestrial ecology by improving cross-temporal research and collaboration

Ecology spans spatial and temporal scales and is inclusive of the history of life on Earth. However, research that occurs at millennial timescales or longer has historically been defined as paleoecology and has not always been well integrated with modern (neo-) ecology. This bifurcation has been previously highlighted, with calls for improved engagement among the subdisciplines, but their priority research areas have not been directly compared. To characterize the research agendas for terrestrial ecological research across different temporal scales, we compared two previous studies, Sutherland and colleagues (2013; neoecology) and Seddon and colleagues (2014; paleoecology), that outlined priority research questions. We identified several themes with potential for temporal integration and explored case studies that highlight cross-temporal collaboration. Finally, a path forward is outlined, focusing on education and training, research infrastructure, and collaboration. Our aim is to improve our understanding of biodiversity patterns and processes by promoting an inclusive and integrative approach that treats time as a foundational concept in ecology.

Predicting the impact of targeted fence removal on connectivity in a migratory ecosystem

Fencing is one of the most widely utilized tools for reducing human-wildlife conflict in agricultural landscapes. However, the increasing global footprint of fencing exceeds millions of kilometers and has unintended consequences for wildlife, including habitat fragmentation, movement restriction, entanglement, and mortality. Here, we present a novel and quantitative approach to prioritize fence removal within historic migratory pathways of white-bearded wildebeest (Connochaetes taurinus) across Kenya's Greater Masai Mara Ecosystem. Our approach first assesses historic and contemporary landscape connectivity of wildebeest between seasonal ranges by incorporating two sets of GPS tracking data and fine-scale fencing data. We then predict connectivity gains from simulated fence removal and evaluate the impact of different corridor widths and locations on connectivity and removal costs derived from locally implemented interventions. Within the study system, we found that modest levels of fence removal resulted in substantial connectivity gains (39%-54% improvement in connectivity for 15-140 km of fence line removed). By identifying the most suitable corridor site, we show that strategically placed narrow corridors outperform larger, more expensive interventions. Our results demonstrate how and where targeted fence removal can enhance connectivity for wildlife. Our framework can aid in identifying suitable and cost-effective corridor restoration sites to guide decision-makers on the removal of fences and other linear barriers. Our approach is transferable to other landscapes where the removal or modification of fences or similar barriers is a feasible mitigation strategy to restore habitat and migratory connectivity.

What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA

Modelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi-generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo-genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation-by-Resistance (IBR) patterns from either Isolation-by-Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross-validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross-validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.

Predicting habitat suitability for alien macroalgae in relation to thermal niche occupancy

Invasive species are a major threat to global diversity and can interact synergistically or antagonistically with various components of climate change. Using species distribution models (SDMs) at different spatial scales and resolutions, we determined the main variables affecting the distribution of six invasive macroalgae present on European coasts. We also studied occupation of the thermal realized niche and predicted areas potentially at risk of invasion. The climatic variables related to warming had a greater influence on distribution at large scales, while non-climatic variables related to river influence and maritime transport at regional scale. Invaders often seemed to occupy colder areas than in their native area. The combination of SDMs with thermal niche of species is a useful way of clarifying the invasion process. This approach will help in the development of preventive strategies whereby the responsible authorities can implement early detection systems and respond swiftly to the appearance of biopollutants.

Global Distribution of Alien Mammals Under Climate Change

The recent thematic Assessment Report on Invasive Alien Species and their Control of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services reaffirmed biological invasions as a major threat to biodiversity. Anticipating biological invasions is crucial for avoiding their ecological and socio-economic impacts, particularly as climate change may provide new opportunities for the establishment and spread of alien species. However, no studies have combined assessments of suitability and dispersal to evaluate the invasion by key taxonomic groups, such as mammals. Using species distribution models, we estimated the potential effect of climate change on the future distributions of 205 alien mammal species by the year 2050 under three different climatic scenarios. We used species dispersal ability to differentiate between suitable areas that may be susceptible to natural dispersal from alien ranges (Spread Potential, SP) and those that may be vulnerable to alien establishment through human-assisted dispersal (Establishment Potential, EP) across 11 zoogeographic realms. Establishment Potential was generally boosted by climate change, showing a clear poleward shift across scenarios, whereas SP was negatively affected by climate change and limited by alien species insularity. These trends were consistent across all realms. Insular ecosystems, while being vulnerable to invasion, may act as geographical traps for alien mammals that lose climatic suitability. In addition, our analysis identified the alien species that are expected to spread or decline the most in each realm, primarily generalists with high invasive potential, as likely foci of future management efforts. In some areas, the possible reduction in suitability for alien mammals could offer opportunities for ecosystem restoration, particularly on islands. In others, increased suitability calls for adequate actions to prevent their arrival and spread. Our findings are potentially valuable in informing synergistic actions addressing both climate change and biological invasion together to safeguard native biodiversity worldwide.

Process-Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond

Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typically sparse; and (ii) deep-learning models are black-box methods and inferring the processes they represent are non-trivial to elicit. Process-based (= mechanistic) models are not constrained by data sparsity or unclear processes and are thus important for building up our ecological knowledge and transfer to applications. In this work, we combine process-based models and neural networks into process-informed neural networks (PINNs), which incorporate the process knowledge directly into the neural network structure. In a systematic evaluation of spatial and temporal prediction tasks for C-fluxes in temperate forests, we show the ability of five different types of PINNs (i) to outperform process-based models and neural networks, especially in data-sparse regimes with high-transfer task and (ii) to inform on mis- or undetected processes.

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

Ecological niche modeling of Lactarius deliciosus using kuenm R package: Insights into habitat preferences

Understanding species habitat preferences is essential for conservation and management efforts, as it enables the identification of areas with a higher likelihood of species presence. Lactarius deliciosus (L.) Gray, an economically important edible mushroom, is influenced by various environmental variables, yet information regarding its ecological niche remains elusive. Therefore, in this study, we aim to address this gap by modeling the fundamental niche of L. deliciosus. Specifically, we explore its distribution patterns in response to large-scale environmental factors, including long-term temperature averages and topography. We employed 242 presence-only georeferenced points in Europe obtained from the Global Biodiversity Information Facility (GBIF). Utilizing the Kuenm R package, we constructed 210 models incorporating five sets of environmental variables, 14 regularization multiplier values, and three feature class combinations. Evaluation metrics included statistical significance, predictive power, and model complexity. The final model was transferred to Turkiye, with careful consideration of extrapolation risk using MESS (multivariate similarity surface) and MoD (most dissimilar variable) metrics. In alignment with all three evaluation criteria, the algorithm implemented in Kuenm identified the best model as the linear-quadratic combination with a regularization multiplier of 0.2, based on variables selected by the contribution importance method. Results underscore temperature-related variables as critical determinants of L. deliciosus habitat preferences within the calibration area, with solar radiation also playing a significant role in the final model. These results underscored the effectiveness of ecological niche modeling (ENM) in understanding how climatic patterns may alter the distribution of species like L. deliciosus. The findings contribute to the development of informed conservation strategies and decision-making in dynamic environments. Emphasizing a comprehensive approach to ecological modeling is crucial for promoting sustainable forest management.

Predicting future grizzly bear habitat use in the Bitterroot Ecosystem under recolonization and reintroduction scenarios

Many conservation actions must be implemented with limited data. This is especially true when planning recovery efforts for extirpated populations, such as grizzly bears (Ursus arctos) within the Bitterroot Ecosystem (BE), where strategies for reestablishing a resident population are being evaluated. Here, we applied individual-based movement models developed for a nearby grizzly bear population to predict habitat use in and near the BE, under scenarios of natural recolonization, reintroduction, and a combination. All simulations predicted that habitat use by grizzly bears would be higher in the northern half of the study area. Under the natural recolonization scenario, use was concentrated in Montana, but became more uniform across the northern BE in Idaho over time. Use was more concentrated in east-central Idaho under the reintroduction scenario. Assuming that natural recolonization continues even if bears are reintroduced, use remained widespread across the northern half of the BE and surrounding areas. Predicted habitat maps for the natural recolonization scenario aligned well with outlier and GPS collar data available for grizzly bears in the study area, with Spearman rank correlations of ≥0.93 and mean class values of ≥9.1 (where class 10 was the highest relative predicted use; each class 1-10 represented 10% of the landscape). In total, 52.4% of outlier locations and 79% of GPS collar locations were in class 10 in our predicted habitat maps for natural recolonization. Simulated grizzly bears selected habitats over a much larger landscape than the BE itself under all scenarios, including multiple-use and private lands, similar to existing populations that have expanded beyond recovery zones. This highlights the importance of recognizing and planning for the role of private lands in recovery efforts, including understanding resources needed to prevent and respond to human-grizzly bear conflict and maintain public acceptance of grizzly bears over a large landscape.

Inferring ecological selection from multidimensional community trait distributions along environmental gradients

Understanding the drivers of community assembly is critical for predicting the future of biodiversity and ecosystem services. Ecological selection ubiquitously shapes communities by selecting for individuals with the most suitable trait combinations. Detecting selection types on key traits across environmental gradients and over time has the potential to reveal the underlying abiotic and biotic drivers of community dynamics. Here, we present a model-based predictive framework to quantify the multidimensional trait distributions of communities (community trait spaces), which we use to identify ecological selection types shaping communities along environmental gradients. We apply the framework to over 3600 boreal forest understory plant communities with results indicating that directional, stabilizing, and divergent selection all modify community trait distributions and that the selection type acting on individual traits may change over time. Our results provide novel and rare empirical evidence for divergent selection within a natural system. Our approach provides a framework for identifying key traits under selection and facilitates the detection of processes underlying community dynamics.

A flexible consistent framework for modelling multiple interacting environmental responses to management in space and time

Management of resources is often a large-scale task addressed using many small-scale interventions. The range of scales at which organisms respond to those interventions, along with the many outcomes which management aims to achieve can make determining the success of management complex. Environmental flow is an example of management where there is a recognized need for managers to demonstrate the impact of their actions by integrating different types of environmental responses. Here, we aim to support decision making in environmental management via the development of a new modelling framework (eFlowEval). It has the capacity to capture best-available knowledge, to scale it in space and time, explore interactions among species, compare scenarios, and account for uncertainty. Thus, it provides a basis for including multiple target groups in a common system. The framework is readily updatable as new information becomes available and can identify where data are insufficient to be scientifically robust. We demonstrate the eFlowEval framework using three very different environmental responses: 1) metabolism, which is a measure of the energy produced and then used in an ecosystem, 2) favorability for a bird species of interest (royal spoonbill Platalea regia), and 3) competing wetland plants (Centipeda cunninghamii and lippia Phyla canescens). These demonstrations illustrate the capability of the eFlowEval framework but the specific outputs shown here should not be used to assess environmental responses to management. Using these demonstrations, we illustrate the capacity of the eFlowEval framework to provide assessments across a range of scales (local to landscape) and from short time frames (weeks to months) to multi-year assessments. Further, we illustrate the ability to: i) scale responses from local to basin scales, ii) vary driver-response model types, iii) represent uncertainty, iv) compare scenarios, v) accommodate variable parameter values at different locations, and vi) incorporate spatial and temporal dependencies and dependencies among species. We also illustrate the framework's ability to capture inter- and intraspecific interactions and their impact in space and time. The eFlowEval framework extends the capacity of the component response models to provide novel modeling capabilities for management at scale. It allows for interactions among species or processes to be incorporated, as well as in space and time. A large degree of flexibility is offered by the framework, in terms of driver-response model types, input data, and aggregation methods. Thus, the eFlowEval framework provides a mechanism to enhance the transparency of environmental watering decision making, capture institutional knowledge, enhance adaptive management and undertake evaluation of the impact of environmental watering at a range of spatial and temporal scales.

Rethinking ecological niches and geographic distributions in face of pervasive human influence in the Anthropocene

Species are distributed in predictable ways in geographic spaces. The three principal factors that determine geographic distributions of species are biotic interactions (B), abiotic conditions (A), and dispersal ability or mobility (M). A species is expected to be present in areas that are accessible to it and that contain suitable sets of abiotic and biotic conditions for it to persist. A species' probability of presence can be quantified as a combination of responses to B, A, and M via ecological niche modeling (ENM; also frequently referred to as species distribution modeling or SDM). This analytical approach has been used broadly in ecology and biogeography, as well as in conservation planning and decision-making, but commonly in the context of 'natural' settings. However, it is increasingly recognized that human impacts, including changes in climate, land cover, and ecosystem function, greatly influence species' geographic ranges. In this light, historical distinctions between natural and anthropogenic factors have become blurred, and a coupled human-natural landscape is recognized as the new norm. Therefore, B, A, and M (BAM) factors need to be reconsidered to understand and quantify species' distributions in a world with a pervasive signature of human impacts. Here, we present a framework, termed human-influenced BAM (Hi-BAM, for distributional ecology that (i) conceptualizes human impacts in the form of six drivers, and (ii) synthesizes previous studies to show how each driver modifies the natural BAM and species' distributions. Given the importance and prevalence of human impacts on species distributions globally, we also discuss implications of this framework for ENM/SDM methods, and explore strategies by which to incorporate increasing human impacts in the methodology. Human impacts are redefining biogeographic patterns; as such, future studies should incorporate signals of human impacts integrally in modeling and forecasting species' distributions.

Isolation by environment and its consequences for range shifts with global change: Landscape genomics of the invasive plant common tansy

Invasive species are a growing global economic and ecological problem. However, it is not well understood how environmental factors mediate invasive range expansion. In this study, we investigated the recent and rapid range expansion of common tansy across environmental gradients in Minnesota, USA. We densely sampled individuals across the expanding range and performed reduced representation sequencing to generate a dataset of 3071 polymorphic loci for 176 individuals. We used non-spatial and spatially explicit analyses to determine the relative influences of geographic distance and environmental variation on patterns of genomic variation. We found no evidence for isolation by distance but strong evidence for isolation by environment, indicating that environmental factors may have modulated patterns of range expansion. Land use classification and soils were particularly important variables related to population structure although they operated on different spatial scales; land use classification was related to broad-scale patterns and soils were related to fine-scale patterns. All analyses indicated a distinctive genetic cluster in the most recently invaded portion of the range. Individuals from the far northwestern range margin were separated from the remainder of the range by reduced migration, which was associated with environmental resistance. This portion of the range was invaded primarily in the last 15 years. Ecological niche models also indicated that this cluster was associated with the expansion of the niche. While invasion is often assumed to be primarily influenced by dispersal limitation, our results suggest that ongoing invasion and range shifts with climate change may be strongly affected by environmental heterogeneity.

Biodiversity in changing environments: An external-driver internal-topology framework to guide intervention

Accompanying the climate crisis is the more enigmatic biodiversity crisis. Rapid reorganization of biodiversity due to global environmental change has defied prediction and tested the basic tenets of conservation and restoration. Conceptual and practical innovation is needed to support decision making in the face of these unprecedented shifts. Critical questions include: How can we generalize biodiversity change at the community level? When are systems able to reorganize and maintain integrity, and when does abiotic change result in collapse or restructuring? How does this understanding provide a template to guide when and how to intervene in conservation and restoration? To this end, we frame changes in community organization as the modulation of external abiotic drivers on the internal topology of species interactions, using plant-plant interactions in terrestrial communities as a starting point. We then explore how this framing can help translate available data on species abundance and trait distributions to corresponding decisions in management. Given the expectation that community response and reorganization are highly complex, the external-driver internal-topology (EDIT) framework offers a way to capture general patterns of biodiversity that can help guide resilience and adaptation in changing environments.

Harmful Ostreopsis cf. ovata blooms could extend in time span with climate change in the Western Mediterranean Sea

Fast environmental changes and high coastal human pressures and impacts threaten the Mediterranean Sea. Over the last decade, recurrent blooms of the harmful dinoflagellate Ostreopsis cf. ovata have been recorded in many Mediterranean beaches. These microalgae produce toxins that affect marine organisms and human health. Understanding the environmental conditions that influence the appearance and magnitude of O. cf. ovata blooms, as well as how climate change will modify its future distribution and dynamics, is crucial for predicting and managing their effects. This study investigates whether the spatio-temporal distribution of this microalga and the frequency of its blooms could be altered in future climate change scenarios in the Mediterranean Western basin. For the first time, an ecological habitat model (EHM) is forced by physico-chemical climate change simulations at high-resolution, under the strong greenhouse gas emission trajectory (RCP8.5). It allows to characterize how O. cf. ovata may respond to projected conditions and how its distribution could shift over a wide spatial scale, in this plausible future. Before being applied to the EHM, future climate simulations are further refined by using a statistical adaptation method (Cumulative Distribution Function transform) to improve the predictions robustness. Temperature (optimum 23-26 °C), high salinity (>38 psu) and high inorganic nutrient concentrations (nitrate >0.25 mmol N·m-3 and phosphate >0.035 mmol P·m-3) drive O. cf. ovata abundances. High spatial disparities in future abundances are observed. Namely, O. cf. ovata abundances could increase on the Mediterranean coasts of France, Spain and the Adriatic Sea while a decrease is expected in the Tyrrhenian Sea. The bloom period could be extended, starting earlier and continuing later in the year. From a methodological point of view, this study highlights best practices of EHMs in the context of climate change to identify sensitive areas for current and future harmful algal blooms.

Ecological niche contributes to the persistence of the western × glaucous-winged gull hybrid zone

Hybrid zones occur in nature when populations with limited reproductive barriers overlap in space. Many hybrid zones persist over time, and different models have been proposed to explain how selection can maintain hybrid zone stability. More empirical studies are needed to elucidate the role of ecological adaptation in maintaining stable hybrid zones. Here, we investigated the role of exogenous factors in maintaining a hybrid zone between western gulls (Larus occidentalis) and glaucous-winged gulls (L. glaucescens). We used ecological niche models (ENMs) and niche similarity tests to quantify and examine the ecological niches of western gulls, glaucous-winged gulls, and their hybrids. We found evidence of niche divergence between all three groups. Our results support the bounded superiority model, providing further evidence that exogenous selection favoring hybrids may be an important factor in maintaining this stable hybrid zone.

Testing spatial transferability of species distribution models reveals differing habitat preferences for an endangered delphinid (Cephalorhynchus hectori) in Aotearoa, New Zealand

Species distribution models (SDMs) can be used to predict distributions in novel times or space (termed transferability) and fill knowledge gaps for areas that are data poor. In conservation, this can be used to determine the extent of spatial protection required. To understand how well a model transfers spatially, it needs to be independently tested, using data from novel habitats. Here, we test the transferability of SDMs for Hector's dolphin (Cephalorhynchus hectori), a culturally important (taonga) and endangered, coastal delphinid, endemic to Aotearoa New Zealand. We collected summer distribution data from three populations from 2021 to 2023. Using Generalised Additive Models, we built presence/absence SDMs for each population and validated the predictive ability of the top models (with TSS and AUC). Then, we tested the transferability of each top model by predicting the distribution of the remaining two populations. SDMs for two populations showed useful performance within their respective areas (Banks Peninsula and Otago), but when used to predict the two areas outside the models' source data, performance declined markedly. SDMs from the third area (Timaru) performed poorly, both for prediction within the source area and when transferred spatially. When data for model building were combined from two areas, results were mixed. Model interpolation was better when presence/absence data from Otago, an area of low density, were combined with data from areas of higher density, but was otherwise poor. The overall poor transferability of SDMs suggests that habitat preferences of Hector's dolphins vary between areas. For these dolphins, population-specific distribution data should be used for conservation planning. More generally, we demonstrate that a one model fits all approach is not always suitable. When SDMs are used to predict distribution in data-poor areas an assessment of performance in the new habitat is required, and results should be interpreted with caution.

Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

Modeling a hot, dry future: Substantial range reductions in suitable environment projected under climate change for a semiarid riparian predator guild

An understanding of species-environmental relationships is invaluable for effective conservation and management under anthropogenic climate change, especially for biodiversity hotspots such as riparian habitats. Species distribution models (SDMs) assess present species-environmental relationships which can project potential suitable environments through space and time. An understanding of environmental factors associated with distributions can guide conservation management strategies under a changing climate. We generated 260 ensemble SDMs for five species of Thamnophis gartersnakes (n = 347)-an important riparian predator guild-in a semiarid and biogeographically diverse region under impact from climate change (Arizona, United States). We modeled present species-environmental relationships and projected changes to suitable environment under 12 future climate scenarios per species, including the most and least optimistic greenhouse gas emission pathways, through 2100. We found that Thamnophis likely advanced northward since the turn of the 20th century and overwinter temperature and seasonal precipitation best explained present distributions. Future ranges of suitable environment for Thamnophis are projected to decrease by ca. -37.1% on average. We found that species already threatened with extinction or those with warm trailing-edge populations likely face the greatest loss of suitable environment, including near or complete loss of suitable environment. Future climate scenarios suggest an upward advance of suitable environment around montane areas for some low to mid-elevation species, which may create pressures to ascend. The most suitable environmental areas projected here can be used to identify potential safe zones to prioritize conservation refuges, including applicable critical habitat designations. By bounding the climate pathway extremes to, we reduce SDM uncertainties and provide valuable information to help conservation practitioners mitigate climate-induced threats to species. Implementing informed conservation actions is paramount for sustaining biodiversity in important aridland riparian systems as the climate warms and dries.

Ten Challenges to Understanding and Managing the Insect-Transmitted, Xylem-Limited Bacterial Pathogen Xylella fastidiosa

An unprecedented plant health emergency in olives has been registered over the last decade in Italy, arguably more severe than what occurred repeatedly in grapes in the United States in the last 140 years. These emergencies are epidemics caused by a stealthy pathogen, the xylem-limited, insect-transmitted bacterium Xylella fastidiosa. Although these epidemics spurred research that answered many questions about the biology and management of this pathogen, many gaps in knowledge remain. For this review, we set out to represent both the U.S. and European perspectives on the most pressing challenges that need to be addressed. These are presented in 10 sections that we hope will stimulate discussion and interdisciplinary research. We reviewed intrinsic problems that arise from the fastidious growth of X. fastidiosa, the lack of specificity for insect transmission, and the economic and social importance of perennial mature woody plant hosts. Epidemiological models and predictions of pathogen establishment and disease expansion, vital for preparedness, are based on very limited data. Most of the current knowledge has been gathered from a few pathosystems, whereas several hundred remain to be studied, probably including those that will become the center of the next epidemic. Unfortunately, aspects of a particular pathosystem are not always transferable to others. We recommend diversification of research topics of both fundamental and applied nature addressing multiple pathosystems. Increasing preparedness through knowledge acquisition is the best strategy to anticipate and manage diseases caused by this pathogen, described as "the most dangerous plant bacterium known worldwide."

Potential impacts of climate change on the geographic distribution of Achillea eriophora DC., a medicinal species endemic to Iran in southwestern Asia

Climate change is considered to rank among the most important global issues affecting species' geographic distributions and biodiversity. Understanding effects of climate change on species can enhance conservation efficacy. In this study, we applied ecological niche modeling (ENM) using maximum entropy (MaxEnt) approaches to predict the potential geographic distribution of Achillea eriophora DC., a medicinal plant species to Iran in southwestern Asia, under current and future climate scenarios. We evaluated potential distributional areas of the species, under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) for the period 2041-2060. Most current potential suitable areas were identified for A. eriophora in montane regions. Our results anticipated that the potential distribution of A. eriophora will expand geographically toward higher elevations and northward. However, the species is expected to experience relatively high losses of suitability in its actual habitats under future climate scenarios. Consequently, we recommend regional-to-national conservation action plans for A. eriophora in its natural habitats.

Thresholds of seascape fauna composition along gradients of human pressures and natural conditions to inform marine spatial planning

Knowledge about the cumulative impacts of anthropogenic activities and environmental conditions on marine ecosystems is incomplete and details are lacking. Compositional community changes can occur along gradients, and community data can be used to assess the state of community resilience against combined impacts of variables representing human pressures and environmental conditions. Here we use a machine learning approach, i.e., Gradient Forest, to identify explanatory variable thresholds and select relevant epibenthic fauna and demersal fish species, which can be used to inform an integrated management of multiple human pressures and conservation planning in the southern North Sea. We show that a broad selection of anthropogenic and environmental variables, such as natural disturbance of the seafloor and euphotic depth, determined community composition thresholds of 67 epibenthic fauna and 39 demersal fish species along environmental conditions and human pressure gradients in the southern North Sea between 2010 and 2020. This has the potential to inform resilience assessments under the Marine Strategy Framework Directive to promote and retain a good environmental status of marine ecosystems.

Predicting the spatial distribution of wintering golden eagles to inform full annual cycle conservation in western North America

Wildlife conservation strategies focused on one season or population segment may fail to adequately protect populations, especially when a species' habitat preferences vary among seasons, age-classes, geographic regions, or other factors. Conservation of golden eagles (Aquila chrysaetos) is an example of such a complex scenario, in which the distribution, habitat use, and migratory strategies of this species of conservation concern vary by age-class, reproductive status, region, and season. Nonetheless, research aimed at mapping priority use areas to inform management of golden eagles in western North America has typically focused on territory-holding adults during the breeding period, largely to the exclusion of other seasons and life-history groups. To support population-wide conservation planning across the full annual cycle for golden eagles, we developed a distribution model for individuals in a season not typically evaluated-winter-and in an area of the interior western U.S. that is a high priority for conservation of the species. We used a large GPS-telemetry dataset and library of environmental variables to develop a machine-learning model to predict spatial variation in the relative intensity of use by golden eagles during winter in Wyoming, USA, and surrounding ecoregions. Based on a rigorous series of evaluations including cross-validation, withheld and independent data, our winter-season model accurately predicted spatial variation in intensity of use by multiple age- and life-history groups of eagles not associated with nesting territories (i.e., all age classes of long-distance migrants, and resident non-adults and adult "floaters", and movements of adult territory holders and their offspring outside their breeding territories). Important predictors in the model were wind and uplift (40.2% contribution), vegetation and landcover (27.9%), topography (14%), climate and weather (9.4%), and ecoregion (8.7%). Predicted areas of high-use winter habitat had relatively low spatial overlap with nesting habitat, suggesting a conservation strategy targeting high-use areas for one season would capture as much as half and as little as one quarter of high-use areas for the other season. The majority of predicted high-use habitat (top 10% quantile) occurred on private lands (55%); lands managed by states and the Bureau of Land Management (BLM) had a lower amount (33%), but higher concentration of high-use habitat than expected for their area (1.5-1.6x). These results will enable those involved in conservation and management of golden eagles in our study region to incorporate spatial prioritization of wintering habitat into their existing regulatory processes, land-use planning tasks, and conservation actions.

Comparative Study of Potential Habitats for Simulium qinghaiense (Diptera: Simuliidae) in the Huangshui River Basin, Qinghai-Tibet Plateau: An Analysis Using Four Ecological Niche Models and Optimized Approaches

The Huangshui River, a vital tributary in the upper reaches of the Yellow River within the eastern Qinghai-Tibet Plateau, is home to the endemic black fly species S. qinghaiense. In this study, we conducted a systematic survey of the distribution of the species in the Huangshui River basin, revealing its predominant presence along the river's main stem. Based on four ecological niche models-MaxEnt with parameter optimization; GARP; BIOCLIM; and DOMAIN-we conduct a comparative analysis; evaluating the accuracy of AUC and Kappa values. Our findings indicate that optimizing parameters significantly improves the MaxEnt model's predictive accuracy by reducing complexity and overfitting. Furthermore, all four models exhibit higher accuracy compared to a random model, with MaxEnt demonstrating the highest AUC and Kappa values (0.9756 and 0.8118, respectively), showcasing significant superiority over the other models (p < 0.05). Evaluation of predictions from the four models elucidates that potential areas of S. qinghaiense in the Huangshui River basin are primarily concentrated in the central and southern areas, with precipitation exerting a predominant influence. Building upon these results, we utilized the MaxEnt model to forecast changes in suitable areas and distribution centers during the Last Interglacial (LIG), Mid-Holocene (MH), and future periods under three climate scenarios. The results indicate significantly smaller suitable areas during LIG and MH compared to the present, with the center of distribution shifting southeastward from the Qilian Mountains to the central part of the basin. In the future, suitable areas under different climate scenarios are expected to contract, with the center of distribution shifting southeastward. These findings provide important theoretical references for monitoring, early warning, and control measures for S. qinghaiense in the region, contributing to ecological health assessment.

Potential sources of time lags in calibrating species distribution models

The Anthropocene is characterized by a rapid pace of environmental change and is causing a multitude of biotic responses, including those that affect the spatial distribution of species. Lagged responses are frequent and species distributions and assemblages are consequently pushed into a disequilibrium state. How the characteristics of environmental change-for example, gradual 'press' disturbances such as rising temperatures due to climate change versus infrequent 'pulse' disturbances such as extreme events-affect the magnitude of responses and the relaxation times of biota has been insufficiently explored. It is also not well understood how widely used approaches to assess or project the responses of species to changing environmental conditions can deal with time lags. It, therefore, remains unclear to what extent time lags in species distributions are accounted for in biodiversity assessments, scenarios and models; this has ramifications for policymaking and conservation science alike. This perspective piece reflects on lagged species responses to environmental change and discusses the potential consequences for species distribution models (SDMs), the tools of choice in biodiversity modelling. We suggest ways to better account for time lags in calibrating these models and to reduce their leverage effects in projections for improved biodiversity science and policy.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

Constructing indicator species distribution models to study the potential invasion risk of invasive plants: A case of the invasion of Parthenium hysterophorus in China

Aim

As invasive plants are often in a non-equilibrium expansion state, traditional species distribution models (SDMs) are likely underestimating their suitable habitat. New methods are necessary to identify potential invasion risk areas.

Location

Tropical monsoon rainforest and subtropical evergreen broad-leaved forest regions in China.

Methods

We took Parthenium hysterophorus as a case study to predict its potential invasion risk using climate, terrain, and human activity variables. First, a generalized joint attribute model (GJAM) was constructed using the occurrence of P. hysterophorus and its 27 closely related species in Taiwan, given it is widely distributed in Taiwan. Based on the output correlation values, two positively correlated species (Cardiospermum halicacabum and Portulaca oleracea) and one negatively correlated species (Crassocephalum crepidioides) were selected as indicator species. Second, the distributions of P. hysterophorus and its indicator species in the study area were predicted separately using an ensemble model (EM). Third, when selecting indicator species to construct indicator SDMs, two treatments (indicator species with positive correlation only, or both positive and negative correlation) were considered. The indicator species' EM predictions were overlaid using a weighted average method, and a better indicator SDMs prediction result was selected by comparison. Finally, the EM prediction result of P. hysterophorus was used to optimize the indicator SDMs result by a maximum overlay.

Results

The optimized indicator SDMs prediction showed an expanded range beyond the current geographic range compared to EM and the thresholds for predicting key environmental variables were wider. It also reinforced the human activities' influence on the potential distribution of P. hysterophorus.

Main Conclusions

For invasive plants with expanding ranges, information about indicator species distribution can be borrowed as a barometer for areas not currently invaded. The optimized indicator SDMs allow for more efficient potential invasion risk prediction. On this basis, invasive plants can be prevented earlier.

Filling the data gaps: Transferring models from data-rich to data-poor deep-sea areas to support spatial management

Spatial management of the deep sea is challenging due to limited available data on the distribution of species and habitats to support decision making. In the well-studied North Atlantic, predictive models of species distribution and habitat suitability have been used to fill data gaps and support sustainable management. In the South Atlantic and other poorly studied regions, this is not possible due to a massive lack of data. In this study, we investigated whether models constructed in data-rich areas can be used to inform data-poor regions (with otherwise similar environmental conditions). We used a novel model transfer approach to identify to what extent a habitat suitability model for Desmophyllum pertusum reef, built in a data-rich basin (North Atlantic), could be transferred usefully to a data-poor basin (South Atlantic). The transferred model was built using the Maximum Entropy algorithm and constructed with 227 presence and 3064 pseudo-absence points, and 200 m resolution environmental grids. Performance in the transferred region was validated using an independent dataset of D. pertusum presences and absences, with assessments made using both threshold-dependent and -independent metrics. We found that a model for D. pertusum reef fitted to North Atlantic data transferred reasonably well to the South Atlantic basin, with an area under the curve of 0.70. Suitable habitat for D. pertusum reef was predicted on 20 of the assessed 27 features including seamounts. Nationally managed Marine Protected Areas provide significant protection for D. pertusum reef habitat in the region, affording full protection from bottom trawling to 14 of the 20 suitable features. In areas beyond national jurisdiction (ABNJ), we found four seamounts that provided suitable habitat for D. pertusum reef to be at least partially protected from bottom trawling, whilst two did not fall within fisheries closures. There are factors to consider when developing models for transfer including data resolution and predictor type. Nevertheless, the promising results of this application demonstrate that model transfer approaches stand to provide significant contributions to spatial planning processes through provision of new, best available data. This is particularly true for ABNJ and areas that have previously undergone little scientific exploration such as the global south.

Fluctuation of ecological niches and geographic range shifts along chile pepper's domestication gradient

Domestication is an ongoing well-described process. However, while many have studied the changes domestication causes in plant genetics, few have explored its impact on the portion of the geographic landscape in which the plants exist. Therefore, the goal of this study was to understand how the process of domestication changed the geographic space suitable for chile pepper (Capsicum annuum) in its center of origin (domestication). C. annuum is a major crop species globally whose center of domestication, Mexico, has been well-studied. It provides a unique opportunity to explore the degree to which ranges of different domestication classes diverged and how these ranges might be altered by climate change. To this end, we created ecological niche models for four domestication classes (wild, semiwild, landrace, modern cultivar) based on present climate and future climate scenarios for 2050, 2070, and 2090. Considering present environment, we found substantial overlap in the geographic niches of all the domestication classes. Yet, environmental and geographic aspects of the current ranges did vary among classes. Wild and commercial varieties could grow in desert conditions, while landraces could not. With projections into the future, habitat was lost asymmetrically, with wild, semiwild, and landraces at greater risk of territorial declines than modern cultivars. Further, we identified areas where future suitability overlap between landraces and wilds is expected to be lost. While range expansion is widely associated with domestication, we found little support of a constant niche expansion (either in environmental or geographical space) throughout the domestication gradient in chile peppers in Mexico. Instead, particular domestication transitions resulted in loss, followed by capturing or recapturing environmental or geographic space. The differences in environmental characterization among domestication gradient classes and their future potential range shifts increase the need for conservation efforts to preserve landraces and semiwild genotypes.

Distribution patterns of Quercus ilex from the last interglacial period to the future by ecological niche modeling

The plants' geographic distribution is affected by natural or human-induced climate change. Numerous studies at both the global and regional levels currently focus on the potential changes in plant distribution areas. Ecological niche modeling can help predict the likely distribution of species according to environmental variables under different climate scenarios. In this study, we predicted the potential geographic distributions of Quercus ilex L. (holm oak), a keystone species of the Mediterranean ecosystem, for the Last Interglacial period (LIG: ~130 Ka), the Last Glacial Maximum (LGM: ~22 Ka), mid-Holocene (MH: ~6 Ka), and future climate scenarios (Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios) for 2050-2070 obtained from CCSM4 and MIROC-ESM global climate scenarios respectively. The models were produced with algorithms from the R-package "biomod2" and assessed by AUC of the receiver operating characteristic plot and true skill statistics. Aside from BIOCLIM (SRE), all model algorithms performed similarly and produced projections that are supported by good evaluation scores, although random forest (RF) slightly outperformed all the others. Additionally, distribution maps generated for the past period were validated through a comparison with pollen data acquired from the Neotoma Pollen Database. The results revealed that southern areas of the Mediterranean Basin, particularly coastal regions, served as long-term refugia for Q. ilex, which was supported by fossil pollen data. Furthermore, the models suggest long-term refugia role for Anatolia and we argue that Anatolia may have served as a founding population for the species. Future climate scenarios indicated that Q. ilex distribution varied by region, with some areas experiencing range contractions and others range expands. This study provides significant insights into the vulnerability of the Q. ilex to future climate change in the Mediterranean ecosystem and highlights the crucial role of Anatolia in the species' historical distribution.

Where to live? Landfast sea ice shapes emperor penguin habitat around Antarctica

Predicting species survival in the face of climate change requires understanding the drivers that influence their distribution. Emperor penguins (Aptenodytes forsteri) incubate and rear chicks on landfast sea ice, whose extent, dynamics, and quality are expected to vary substantially due to climate change. Until recently, this species' continent-wide observations were scarce, and knowledge on their distribution and habitat limited. Advances in satellite imagery now allow their observation and characterization of habitats across Antarctica at high resolution. Using circumpolar high-resolution satellite images, unique fast ice metrics, and geographic and biological factors, we identified diverse penguin habitats across the continent, with no significant difference between areas with penguins or not. There is a clear geographic partitioning of colonies with respect to their defining habitat characteristics, indicating possible behavioral plasticity among different metapopulations. This coincides with geographic structures found in previous genetic studies. Given projections of quasi-extinction for this species in 2100, this study provides essential information for conservation measures.

Potential present and future distributions of the genus Atta of Mexico

Temperature and precipitation influence insect distribution locally and drive large-scale biogeographical patterns. We used current and future climate data from the CHELSA database to create ensemble species distribution models for three Atta leaf-cutting ant species (Atta cephalotes, A. mexicana, and A. texana) found in Mexico. These models were used to estimate the potential impact of climate change on the distribution of these species in the future. Our results show that bioclimatic variables influence the distribution of each Atta species occupying a unique climatic niche: A. cephalotes is affected by temperature seasonality, A. mexicana by isothermality, and A. texana by the minimum temperature of the coldest month. Atta texana and A. mexicana are expected to decline their range by 80% and 60%, respectively, due to rising temperatures, decreased rainfall, and increased drought. Due to rising temperatures and increased humidity, Atta cephalotes is expected to expand its range by 30%. Since Atta species are important pests, our coexistence with them requires knowledge of their ecological functions and potential future distribution changes. In addition, these insects serve as bioindicators of habitat quality, and they can contribute to the local economy in rural areas since they are eaten as food for the nutritional value of the queens. In this sense, presenting a future perspective of these species' distribution is important for forest and crop management. Education programs also are necessary to raise awareness of the importance of these ants and the challenges they face because of climate change. Our results offer a perspective of climate change studies to define conservation and adaptation strategies for protecting vulnerable areas such as high-elevation remnant forests.

Patterns and drivers of climatic niche dynamics during biological invasions of island-endemic amphibians, reptiles, and birds

Shifts between native and alien climatic niches pose a major challenge for predicting biological invasions. This is particularly true for insular species because geophysical barriers could constrain the realization of their fundamental niches, which may lead to underestimates of their invasion potential. To investigate this idea, we estimated the frequency of shifts between native and alien climatic niches and the magnitude of climatic mismatches using 80,148 alien occurrences of 46 endemic insular amphibian, reptile, and bird species. Then, we assessed the influence of nine potential predictors on climatic mismatches across taxa, based on species' characteristics, native range physical characteristics, and alien range properties. We found that climatic mismatch is common during invasions of endemic insular birds and reptiles: 78.3% and 55.1% of their respective alien records occurred outside of the environmental space of species' native climatic niche. In comparison, climatic mismatch was evident for only 16.2% of the amphibian invasions analyzed. Several predictors significantly explained climatic mismatch, and these varied among taxonomic groups. For amphibians, only native range size was associated with climatic mismatch. For reptiles, the magnitude of climatic mismatch was higher for species with narrow native altitudinal ranges, occurring in topographically complex or less remote islands, as well as for species with larger distances between their native and alien ranges. For birds, climatic mismatch was significantly larger for invasions on continents with higher phylogenetic diversity of the recipient community, and when the invader was more evolutionarily distinct. Our findings highlight that apparently common niche shifts of insular species may jeopardize our ability to forecast their potential invasions using correlative methods based on climatic variables. Also, we show which factors provide additional insights on the actual invasion potential of insular endemic amphibians, reptiles, and birds.

Eco-evolution from deep time to contemporary dynamics: The role of timescales and rate modulators

Eco-evolutionary dynamics, or eco-evolution for short, are often thought to involve rapid demography (ecology) and equally rapid heritable phenotypic changes (evolution) leading to novel, emergent system behaviours. We argue that this focus on contemporary dynamics is too narrow: Eco-evolution should be extended, first, beyond pure demography to include all environmental dimensions and, second, to include slow eco-evolution which unfolds over thousands or millions of years. This extension allows us to conceptualise biological systems as occupying a two-dimensional time space along axes that capture the speed of ecology and evolution. Using Hutchinson's analogy: Time is the 'theatre' in which ecology and evolution are two interacting 'players'. Eco-evolutionary systems are therefore dynamic: We identify modulators of ecological and evolutionary rates, like temperature or sensitivity to mutation, which can change the speed of ecology and evolution, and hence impact eco-evolution. Environmental change may synchronise the speed of ecology and evolution via these rate modulators, increasing the occurrence of eco-evolution and emergent system behaviours. This represents substantial challenges for prediction, especially in the context of global change. Our perspective attempts to integrate ecology and evolution across disciplines, from gene-regulatory networks to geomorphology and across timescales, from today to deep time.

Niche shifts and range expansions after invasions of two major pests: the Asian longhorned beetle and the citrus longhorned beetle

BACKGROUND

In recent years, the quarantine forestry pests the Asian longhorned beetle (ALB) Anoplophora glabripennis and the citrus longhorned beetle (CLB) Anoplophora chinensis have spread across the Northern Hemisphere, triggering concern about their potential distribution. However, little is known about the niche shifts of the pests during the invasion, making it difficult to assess their potential ranges. We thus employed two distinct approaches (i.e., ordination-based and reciprocal model-based) to compare the native and invaded niches of ALB and CLB after their spread to new continents based on global occurrence records. We further constructed models with pooled occurrences from both the native and invaded ranges to analyze the effects of occurrence partitioning on predicted ranges.

RESULTS

We detected expansions in the invaded niches of both pests, indicating that the niches shifted to varying extents after the invasion. Large shares of the native niches of ALB and CLB remained unfilled, revealing the potential for further invasion in new regions. The models calibrated with pooled occurrences clearly underestimated the potential ranges in invaded regions compared with the projections based on partitioned models considering native and invaded areas separately.

CONCLUSIONS

These results emphasize the importance of elucidating the niche dynamics of invasive species for obtaining accurately predicted ranges, which may help identify risk areas masked by the assumption of niche conservatism. Furthermore, prevention and quarantine measures for ALB and CLB are clearly needed to avoid future serious damage to forest ecosystems. © 2023 Society of Chemical Industry.

Coupling Plant Growth Models and Pest and Disease Models: An Interaction Structure Proposal, MIMIC

Coupling plant growth model with pests and diseases (P&D) models, with consideration for the long-term feedback that occurs after the interaction, is still a challenging task nowadays. While a number of studies have examined various methodologies, none of them provides a generic frame able to host existing models and their codes without updating deeply their architecture. We developed MIMIC (Mediation Interface for Model Inner Coupling), an open-access framework/tool for this objective. MIMIC allows to couple plant growth and P&D models in a variety of ways. Users can experiment with various interaction configurations, ranging from a weak coupling that is mediated by the direct exchange of inputs and outputs between models to an advanced coupling that utilizes a third-party tool if the models' data or operating cycles do not align. The users decide how the interactions operate, and the platform offers powerful tools to design key features of the interactions, mobilizing metaprogramming techniques. The proposed framework is demonstrated, implementing coffee berry borers' attacks on Coffea arabica fruits. Observations conducted in a field in Sumatra (Indonesia) assess the coupled interaction model. Finally, we highlight the user-centric implementation characteristics of MIMIC, as a practical and convenient tool that requires minimal coding knowledge to use.

Unveiling the Potential Distribution of the Highly Threatened Madeira Pipistrelle (Pipistrellus maderensis): Do Different Evolutionary Significant Units Exist?

The isolation of islands has played a significant role in shaping the unique evolutionary histories of many species of flora and fauna, including bats. One notable example is the Madeira pipistrelle (Pipistrellus maderensis), which inhabits the Macaronesian archipelagos of the Azores, Madeira, and the Canary Islands. Despite the high biogeographic and conservation importance of this species, there is limited information on its ecology and evolutionary history across different archipelagos. In our study, we employed species distribution models (SDMs) to identify suitable habitats for the Madeira pipistrelle and determine the environmental factors influencing its distribution. Additionally, we conducted molecular comparisons using mitochondrial DNA data from various Macaronesian islands. Molecular analyses provided compelling evidence for the presence of distinct Evolutionary Significant Units on the different archipelagos. We identified distinct haplotypes in the populations of Madeira and the Canary Islands, with a genetic distance ranging from a minimum of 2.4% to a maximum of 3.3% between samples from different archipelagos. In support of this, SDMs highlighted relevant dissimilarities between the environmental requirements of the populations of the three archipelagos, particularly the climatic niche. Our research demonstrates that deeper investigations that combine ecological, morphological, and genetic areas are necessary to implement tailored conservation strategies.

Plant environmental memory: implications, mechanisms and opportunities for plant scientists and beyond

Plants are extremely plastic organisms. They continuously receive and integrate environmental information and adjust their growth and development to favour fitness and survival. When this integration of information affects subsequent life stages or the development of subsequent generations, it can be considered an environmental memory. Thus, plant memory is a relevant mechanism by which plants respond adaptively to different environments. If the cost of maintaining the response is offset by its benefits, it may influence evolutionary trajectories. As such, plant memory has a sophisticated underlying molecular mechanism with multiple components and layers. Nonetheless, when mathematical modelling is combined with knowledge of ecological, physiological, and developmental effects as well as molecular mechanisms as a tool for understanding plant memory, the combined potential becomes unfathomable for the management of plant communities in natural and agricultural ecosystems. In this review, we summarize recent advances in the understanding of plant memory, discuss the ecological requirements for its evolution, outline the multilayered molecular network and mechanisms required for accurate and fail-proof plant responses to variable environments, point out the direct involvement of the plant metabolism and discuss the tremendous potential of various types of models to further our understanding of the plant's environmental memory. Throughout, we emphasize the use of plant memory as a tool to unlock the secrets of the natural world.

Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data

Real-time monitoring using in-situ sensors is becoming a common approach for measuring water-quality within watersheds. High-frequency measurements produce big datasets that present opportunities to conduct new analyses for improved understanding of water-quality dynamics and more effective management of rivers and streams. Of primary importance is enhancing knowledge of the relationships between nitrate, one of the most reactive forms of inorganic nitrogen in the aquatic environment, and other water-quality variables. We analysed high-frequency water-quality data from in-situ sensors deployed in three sites from different watersheds and climate zones within the National Ecological Observatory Network, USA. We used generalised additive mixed models to explain the nonlinear relationships at each site between nitrate concentration and conductivity, turbidity, dissolved oxygen, water temperature, and elevation. Temporal auto-correlation was modelled with an auto-regressive-moving-average (ARIMA) model and we examined the relative importance of the explanatory variables. Total deviance explained by the models was high for all sites (99%). Although variable importance and the smooth regression parameters differed among sites, the models explaining the most variation in nitrate contained the same explanatory variables. This study demonstrates that building a model for nitrate using the same set of explanatory water-quality variables is achievable, even for sites with vastly different environmental and climatic characteristics. Applying such models will assist managers to select cost-effective water-quality variables to monitor when the goals are to gain a spatial and temporal in-depth understanding of nitrate dynamics and adapt management plans accordingly.

Peatland dynamics: A review of process-based models and approaches

Despite peatlands' important feedbacks on the climate and global biogeochemical cycles, predicting their dynamics involves many uncertainties and an overwhelming variety of available models. This paper reviews the most widely used process-based models for simulating peatlands' dynamics, i.e., the exchanges of energy and mass (water, carbon, and nitrogen). 'Peatlands' here refers to mires, fens, bogs, and peat swamps both intact and degraded. Using a systematic search (involving 4900 articles), 45 models were selected that appeared at least twice in the literature. The models were classified into four categories: terrestrial ecosystem models (biogeochemical and global dynamic vegetation models, n = 21), hydrological models (n = 14), land surface models (n = 7), and eco-hydrological models (n = 3), 18 of which featured "peatland-specific" modules. By analysing their corresponding publications (n = 231), we identified their proven applicability domains (hydrology and carbon cycles dominated) for different peatland types and climate zones (northern bogs and fens dominated). The studies range in scale from small plots to global, and from single events to millennia. Following a FOSS (Free Open-Source Software) and FAIR (Findable, Accessible, Interoperable, Reusable) assessment, the number of models was reduced to 12. Then, we conducted a technical review of the approaches and associated challenges, as well as the basic aspects of each model, e.g., spatiotemporal resolution, input/output data format and modularity. Our review streamlines the process of model selection and highlights: (i) standardization and coordination are required for both data exchange and model calibration/validation to facilitate intercomparison studies; and (ii) there are overlaps in the models' scopes and approaches, making it imperative to fully optimize the strengths of existing models rather than creating redundant ones. In this regard, we provide a futuristic outlook for a 'peatland community modelling platform' and suggest an international peatland modelling intercomparison project.

Individual based models for the simulation of fish movement near barriers: Current work and future directions

River fragmentation is an increasing issue for water managers and conservationists. Barriers such as dams interfere with freshwater fish migration, leading to drastic population declines. While there are a range of widely implemented mitigation approaches, e.g. fish passes, such measures are often inefficient due to suboptimal operation and design. There is increasing need to be able to assess mitigation options prior to implementation. Individual based models (IBMs) are a promising option. IBMs can simulate the fine-scale movement of individual fish within a population as they attempt to find a fish pass, incorporating movement processes themselves. Moreover, IBMs have high transferability to other sites or conditions (e.g. changing mitigation, change in flow conditions), making them potentially valuable for freshwater fish conservation yet their application to the fine-scale movement of fish past barriers is still novel. Here, we present an overview of existing IBMs for fine-scale freshwater fish movement, with emphasis on study species and the parameters driving movement in the models. In this review, we focus on IBMs suitable for the simulation of fish tracks as they approach or pass a single barrier. The selected IBMs for modelling fine-scale freshwater fish movement largely focus on salmonids and cyprinid species. IBMs have many applications in the context of fish passage, such as testing different mitigation options or understanding processes behind movement. Existing IBMs include movement processes such as attraction and rejection behaviours, as reported in literature. Yet some factors affecting fish movement e.g. biotic interactions are not covered by existing IBMs. As the technology available for fine scale data collection continues to advance, such as increasing data linking fish behaviour to hydraulics, IBMs could become a more common tool in the design and implementation of fish bypass structures.

Predicting present spatial distribution and habitat preferences of commercial fishes using a maximum entropy approach

The knowledge of the geographical distribution and habitat preferences of marine species is the key to protecting marine ecosystems. Modeling the distribution of marine species through environmental variables is an essential step to understanding and reducing climate change effects on marine biodiversity and related human populations. In this study, the present distributions of commercial fishes including Acanthopagrus latus, Planiliza klunzingeri, and Pomadasys kaakan were modeled using the maximum entropy (MaxEnt) modeling technique and a set of 22 environmental variables. In total, 1531 geographical records belonging to three species were extracted from online databases Ocean Biodiversity Information System (OBIS, 829 records, 54%), Global Biodiversity Information Facility (GBIF, 17 records, 1%), and literature (685 records, 45%) during September to December 2022. The findings showed the values of area under the receiver operating characteristic (ROC) curve (AUC) above 0.99 for all species indicating the high performance of this technique to reflect the actual distribution of species. Environmental factors such as depth (19.68%), sea surface temperature (SST) (19.40%), and wave height (20.71%) were the strongest environmental predictors determining the present distribution and habitat preferences of the three commercial fish species. The Persian Gulf, Iranian coasts of the Sea of Oman, North Arabian Sea, North-East areas of the Indian Ocean, and North coasts of Australia are among the areas with ideal environmental conditions for the species. For all species, the percentage of habitats with high suitability (13.35%) was higher compared to habitats with low suitability (6.56%). However, a high percentage of species occurrence habitats had unsuitable conditions (68.58%) showing the vulnerability of these commercial fishes. Significant management strategies are needed to protect preferred habitats to minimize the effect of fishery and climate change on the population stocks of these commercial fishes.

A quixotic view of spatial bias in modelling the distribution of species and their diversity

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.