Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference

Toxoplasma gondii DNA in Tissues of Anadromous Arctic Charr, Salvelinus alpinus, Collected From Nunavik, Québec, Canada

BACKGROUND

Toxoplasma gondii is a very common zoonotic parasite in humans and animals worldwide. Human seroprevalence is high in some regions of Canada's North and is thought to be associated with the consumption of traditionally prepared country foods, such as caribou, walrus, ringed seal and beluga. While numerous studies have reported on the prevalence of T. gondii in these animals, in the general absence of felid definitive hosts in the North there has been considerable debate regarding the source of infection, particularly in marine mammals. It has been proposed that fish could be involved in this transmission.

AIMS

The objectives of the present study were to perform a targeted survey to determine the prevalence of T. gondii DNA in various tissues of anadromous Arctic charr sampled in Nunavik, Québec, and to investigate the possible role of this commonly consumed fish in the transmission of infection to humans and marine mammals in Canada's North.

METHODS AND RESULTS

A total of 126 individual Arctic charr were sampled from several sites in Nunavik, and various tissues were tested for the presence of T. gondii DNA using PCR. Overall, 12 out of 126 (9.5%) Arctic charr tested in the present study were PCR-positive, as confirmed by DNA sequencing. Brain tissue was most commonly found to be positive, followed by heart tissue, while none of the dorsal muscle samples tested were positive.

CONCLUSIONS

Although the presence of T. gondii DNA in brain and heart tissues of Arctic charr is very intriguing, infection in these fish, and their possible role in the transmission of this parasite to humans and marine mammals, will need to be confirmed using mouse bioassays. Arctic charr are likely exposed to T. gondii through the ingestion of oocysts transported by surface water and ocean currents from more southerly regions where the definitive felid hosts are more abundant. If infection in Arctic charr can be confirmed, it is possible that these fish could play an important role in the transmission of toxoplasmosis to Inuit, either directly through the consumption of raw fish or indirectly through the infection of fish-eating marine mammals harvested as country foods.

Pushing the limits: expanding the temperature tolerance of a coral photosymbiont through differing selection regimes

Coral thermal bleaching resilience can be improved by enhancing photosymbiont thermal tolerance via experimental evolution. While successful for some strains, selection under stable temperatures was ineffective at increasing the thermal threshold of an already thermo-tolerant photosymbiont (Durusdinium trenchii). Corals from environments with fluctuating temperatures tend to have comparatively high heat tolerance. Therefore, we investigated whether exposure to temperature oscillations can raise the upper thermal limit of D. trenchii. We exposed a D. trenchii strain to stable and fluctuating temperature profiles, which varied in oscillation frequency. After 2.1 yr (54-73 generations), we characterised the adaptive responses under the various experimental evolution treatments by constructing thermal performance curves of growth from 21 to 31°C for the heat-evolved and wild-type lineages. Additionally, the accumulation of extracellular reactive oxygen species, photophysiology, photosynthesis and respiration rates were assessed under increasing temperatures. Of the fluctuating temperature profiles investigated, selection under the most frequent oscillations (diurnal) induced the greatest widening of D. trenchii's thermal niche. Continuous selection under elevated temperatures induced the only increase in thermal optimum and a degree of generalism. Our findings demonstrate how differing levels of thermal homogeneity during selection drive unique adaptive responses to heat in a coral photosymbiont.

Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management

The Precautionary Approach to Fisheries Management requires an assessment of the impact of uncertainty on the risk of achieving management objectives. However, the main quantities, such as spawning stock biomass (SSB) and fish mortality (F), used in management metrics cannot be directly observed. This requires the use of models to provide guidance, for which there are three paradigms: the best assessment, model ensemble, and Management Strategy Evaluation (MSE). It is important to validate the models used to provide advice. In this study, we demonstrate how stock assessment models can be validated using a diagnostic toolbox, with a specific focus on prediction skill. Prediction skill measures the precision of a predicted value, which is unknown to the model, in relation to its observed value. By evaluating the accuracy of model predictions against observed data, prediction skill establishes an objective framework for accepting or rejecting model hypotheses, as well as for assigning weights to models within an ensemble. Our analysis uncovers the limitations of traditional stock assessment methods. Through the quantification of uncertainties and the integration of multiple models, our objective is to improve the reliability of management advice considering the complex interplay of factors that influence the dynamics of fish stocks.

Which distribution to choose for deriving a species sensitivity distribution? Implications from analysis of acute and chronic ecotoxicity data

Species sensitivity distributions (SSDs) estimated by fitting a statistical distribution to ecotoxicity data are indispensable tools used to derive the hazardous concentration for 5 % of species (HC5) and thereby a predicted no-effect concentration in environmental risk assessment. Whereas various statistical distributions are available for SSD estimation, the fundamental question of which statistical distribution should be used has received limited systematic analysis. We aimed to address this knowledge gap by applying four frequently used statistical distributions (log-normal, log-logistic, Burr type III, and Weibull distributions) to acute and chronic SSD estimation using aquatic toxicity data for 191 and 31 chemicals, respectively. Based on the differences in the corrected Akaike's information criterion (AICc) as well as visual inspection of the fitting of the lower tails of SSD curves, the log-normal SSD was generally better or equally good for the majority of chemicals examined. Together with the fact that the ratios of HC5 values of other alternative SSDs to those of log-normal SSDs generally fell within the range 0.1-10, our findings indicate that the log-normal distribution can be a reasonable first candidate for SSD derivation, which does not contest the existing widespread use of log-normal SSDs.

Multimodel Approaches Are Not the Best Way to Understand Multifactorial Systems

Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of artificially simplified models. (2) MMA improves on IT model selection by implementing a simple form of shrinkage estimation (a way to make accurate predictions from a model with many parameters relative to the amount of data, by "shrinking" parameter estimates toward zero). However, other shrinkage estimators such as penalized regression or Bayesian hierarchical models with regularizing priors are more computationally efficient and better supported theoretically. (3) In general, the procedures for extracting confidence intervals from MMA are overconfident, providing overly narrow intervals. If researchers want to use limited data sets to accurately estimate the strength of multiple competing ecological processes along with reliable confidence intervals, the current best approach is to use full (maximal) statistical models (possibly with Bayesian priors) after making principled, a priori decisions about model complexity.

A century of statistical Ecology

As data and computing power have surged in recent decades, statistical modeling has become an important tool for understanding ecological patterns and processes. Statistical modeling in ecology faces two major challenges. First, ecological data may not conform to traditional methods, and second, professional ecologists often do not receive extensive statistical training. In response to these challenges, the journal Ecology has published many innovative statistical ecology papers that introduced novel modeling methods and provided accessible guides to statistical best practices. In this paper, we reflect on Ecology's history and its role in the emergence of the subdiscipline of statistical ecology, which we define as the study of ecological systems using mathematical equations, probability, and empirical data. We showcase 36 influential statistical ecology papers that have been published in Ecology over the last century and, in so doing, comment on the evolution of the field. As data and computing power continue to increase, we anticipate continued growth in statistical ecology to tackle complex analyses and an expanding role for Ecology to publish innovative and influential papers, advancing the discipline and guiding practicing ecologists.

Early life conditions reduce similarity between reproductive partners in HPA axis response to stress

Social environments modulate endocrine function, yet it is unclear whether individuals can become like their social partners in how they physiologically respond to stressors. This social transmission of hypothalamic-pituitary-adrenal (HPA) axis reactivity could have long-term consequences for health and lifespan of individuals if their social partners react to stressors with an exaggerated HPA axis response. We tested whether glucocorticoid levels in response to stress of breeding partners changes after breeding depending on whether partners had similar or dissimilar postnatal conditions. We manipulated postnatal conditions by mimicking early life stress in zebra finch chicks (Taeniopygia guttata) via postnatal corticosterone exposure. When they reached adulthood, we created breeding pairs where the female and male had experienced either the same or different early life hormonal treatment (corticosterone or control). Before and after breeding, we obtained blood samples within 3 min and after 10 min or 30 min of restraint stress (baseline, cort10, cort30). We found that corticosterone levels of individuals in response to restraint were affected by their own and their partner's early life conditions, but did not change after breeding. However, across all pairs, partners became more similar in cort30 levels after breeding, although differences between partners in cort10 remained greater in pairs with a corticosterone-treated female. Thus, we show that HPA axis response to stressors in adulthood can be modulated by reproductive partners and that similarity between partners is reduced when females are postnatally exposed to elevated glucocorticoids.

Climate as a Predictive Factor for Invasion: Unravelling the Range Dynamics of Carpomya vesuviana Costa

Invasive alien species (IAS) significantly affect global native biodiversity, agriculture, industry, and human health. Carpomya vesuviana Costa, 1854 (Diptera: Tephritidae), a significant global IAS, affects various date species, leading to substantial economic losses and adverse effects on human health and the environment. This study employed biomod2 ensemble models, multivariate environmental similarity surface and most dissimilar variable analyses, and ecological niche dynamics based on environmental and species data to predict the potential distribution of C. vesuviana and explore the environmental variables affecting observed patterns and impacts. Compared to native ranges, ecological niche shifts at invaded sites increased the invasion risk of C. vesuviana globally. The potential geographical distribution was primarily in Asia, Africa, and Australia, with a gradual increase in suitability with time and radiation levels. The potential geographic distribution centre of C. vesuviana is likely to shift poleward between the present and the 2090s. We also show that precipitation is a key factor influencing the likely future distribution of this species. In conclusion, climate change has facilitated the expansion of the geographic range and ecological niche of C. vesuviana, requiring effective transnational management strategies to mitigate its impacts on the natural environment and public health during the Anthropocene. This study aims to assess the potential threat of C. vesuviana to date palms globally through quantitative analytical methods. By modelling and analysing its potential geographic distribution, ecological niche, and environmental similarities, this paper predicts the pest's dispersal potential and possible transfer trends in geographic centres of mass in order to provide prevention and control strategies for the global date palm industry.

Alien range size, habitat breadth, origin location, and domestication of alien species matter to their impact risks

Invasive alien species are a major driver of biodiversity loss. Currently, the process of biological invasions is experiencing a constant acceleration, foreshadowing a future increase in the threat posed by invasive alien species to global biodiversity. Therefore, it is necessary to assess the impact risks of invasive alien species and related factors. Here, we constructed a dataset of negative environmental impact events to evaluate the impact risks of alien species. We collected information on 1071 established alien terrestrial vertebrates and then gathered negative environmental impacts for 108 of those species. Generalized linear mixed-effects model and phylogenetic generalized least-squares regression model were used to examine the characteristic (including life-history traits, characteristics related to distribution, and introduction event characteristics) correlates of species' impact risks at the global scale for 108 established alien terrestrial vertebrates (mammals, birds, reptiles and amphibians). Our results showed that a total of 3158 negative environmental impacts were reported for 108 harmful species across 71 countries worldwide. Factors associated with impact risks varied slightly among taxa, but alien range size, habitat breadth, origin location, and domestication were significantly correlated with impact risks. Our study aims to identify the characteristics of alien species with high-impact risks to facilitate urgent assessment of alien species and to protect the local ecological environment and biodiversity.

Seroprevalence and renal carriage of pathogenic Leptospira in livestock in Cotonou, Benin

BACKGROUND

Leptospirosis is a zoonotic disease. It is particularly prevalent in tropical countries and has major consequences for human and animal health. In Benin, the disease's epidemiology remains poorly understood, especially in livestock, for which data are lacking.

OBJECTIVES

To characterise Leptospira seroprevalence and locally circulating serogroups in livestock from Cotonou and to estimate the prevalence of Leptospira renal carriage in cattle.

METHODS

We conducted a cross-sectional study in February 2020 during which livestock were sampled at an abattoir and in an impoverished city district. We analysed blood samples from 279 livestock animals (i.e. cattle, sheep, goats and pigs) using the microscopic agglutination test. Additionally, samples of renal tissue from 100 cattle underwent 16s rRNA (rrs) real-time PCR analysis.

RESULTS

For the 131 cattle, 85 sheep, and 50 goats tested, seroprevalence was 18% (95% confidence interval [CI] [12%, 26%]), 9% (95% CI [4%, 17%] and 2% (95% CI [0%, 9%]), respectively, and most of the seropositive animals were associated with 1:100 titres. All 13 pigs were seronegative. Leptospira DNA was found in the renal tissue of 10% (95% CI [5%, 18%]) of the cattle tested (n = 100). Leptospira borgpetersenii was the main species present (n = 7), but Leptospira interrogans (n = 2) and Leptospira kirschneri (n = 1) were also detected. Various serogroups (Canicola, Grippotyphosa, Sejroe, Icterohaemorrhagiae, Pomona, Pyrogenes, Australis and Autumnalis) were detected using microscopic agglutination test without a clear predominance of any of them.

CONCLUSIONS

These results suggest that abattoir workers and people living in close contact with livestock in poor urban areas are exposed to the risk of Leptospira infection.

RIDGE, a tool tailored to detect gene flow barriers across species pairs

Characterizing the processes underlying reproductive isolation between diverging lineages is central to understanding speciation. Here, we present RIDGE-Reproductive Isolation Detection using Genomic polymorphisms-a tool tailored for quantifying gene flow barrier proportion and identifying the relevant genomic regions. RIDGE relies on an Approximate Bayesian Computation with a model-averaging approach to accommodate diverse scenarios of lineage divergence. It captures heterogeneity in effective migration rate along the genome while accounting for variation in linked selection and recombination. The barrier detection test relies on numerous summary statistics to compute a Bayes factor, offering a robust statistical framework that facilitates cross-species comparisons. Simulations revealed RIDGE's efficiency in capturing signals of ongoing migration. Model averaging proved particularly valuable in scenarios of high model uncertainty where no migration or migration homogeneity can be wrongly assumed, typically for recent divergence times <0.1 2Ne generations. Applying RIDGE to four published crow data sets, we first validated our tool by identifying a well-known large genomic region associated with mate choice patterns. Second, while we identified a significant overlap of outlier loci using RIDGE and traditional genomic scans, our results suggest that a substantial portion of previously identified outliers are likely false positives. Outlier detection relies on allele differentiation, relative measures of divergence and the count of shared polymorphisms and fixed differences. Our analyses also highlight the value of incorporating multiple summary statistics including our newly developed outlier ones that can be useful in challenging detection conditions.

Intrinsic anticipatory motives in non-human primate food consumption behavior

Future-oriented behavior is regarded as a cornerstone of human cognition. One key phenomenon through which future orientation can be studied is the delay of gratification, when consumption of an immediate reward is withstood to achieve a larger reward later. The delays used in animal delay of gratification paradigms are rather short to be considered relevant for studying human-like future orientation. Here, for the first time, we show that rhesus macaques exhibit human-relevant future orientation downregulating their operant food consumption in anticipation of a nutritionally equivalent but more palatable food with an unprecedentedly long delay of approximately 2.5 h. Importantly, this behavior is not a result of conditioning but intrinsic to the animals. Our results show that the cognitive time horizon of primates, when tested in ecologically valid foraging-like experiments, extends much further into the future than previously considered, opening up new avenues for translational biomedical research.

Agave distribution and floral display influence foraging rates of an endangered pollinating bat and implications for conservation

Wildlife conservation involves making management decisions with incomplete knowledge of ecological relationships. Efforts to augment foraging resources for the endangered Mexican long-nosed bat (Leptonycteris nivalis) are progressing despite limited knowledge about the species' foraging behavior and requirements. This study aimed to understand L. nivalis responses to floral resource availability, focusing on individual agave- and local-scale characteristics influencing visitation rates to flowering agaves. We observed bat visitation at 62 flowering agaves around two roosts in northeast Mexico on 46 nights in the summers of 2017 and 2018. We found visitation rate had positive relationships with two agave-scale characteristics: the number of umbels with open flowers and the lower vertical position on the stalk of those umbels (i.e., earlier phenological stages of flowering). However, these factors exhibited strong negative interaction: with few umbels with open flowers, the position of flowering umbels had little effect on visitation rate, but when umbels with open flowers were abundant, visitation rate was more strongly related to the lower flowering umbel position. We also found relationships between visitation rate and two local-scale characteristics: negative for the density of flowering conspecifics within 30 m of the focal agave and positive for the density of dead standing agave stalks within 30 m. Our findings suggest opportunities to augment foraging resources for L. nivalis in ways that are consistent with their foraging behavior, including: increasing the supply of simultaneously blooming flowers by planting agave species that tend to have more umbels with simultaneously open flowers; planting multiple species of agaves with different flowering times to increase the availability of agaves with open flowers on lower-positioned umbels throughout the period when bats are present in the region; planting agaves in clusters; and keeping dead standing agave stalks on the landscape. Our study points to useful management strategies that can be implemented and monitored as part of an adaptive management approach to aid in conservation efforts.

Warming and top-down control of stage-structured prey: Linking theory to patterns in natural systems

Warming has broad and often nonlinear impacts on organismal physiology and traits, allowing it to impact species interactions like predation through a variety of pathways that may be difficult to predict. Predictions are commonly based on short-term experiments and models, and these studies often yield conflicting results depending on the environmental context, spatiotemporal scale, and the predator and prey species considered. Thus, the accuracy of predicted changes in interaction strength, and their importance to the broader ecosystems they take place in, remain unclear. Here, we attempted to link one such set of predictions generated using theory, modeling, and controlled experiments to patterns in the natural abundance of prey across a broad thermal gradient. To do so, we first predicted how warming would impact a stage-structured predator-prey interaction in riverine rock pools between Pantala spp. dragonfly nymph predators and Aedes atropalpus mosquito larval prey. We then described temperature variation across a set of hundreds of riverine rock pools (n = 775) and leveraged this natural gradient to look for evidence for or against our model's predictions. Our model's predictions suggested that warming should weaken predator control of mosquito larval prey by accelerating their development and shrinking the window of time during which aquatic dragonfly nymphs could consume them. This was consistent with data collected in rock pool ecosystems, where the negative effects of dragonfly nymph predators on mosquito larval abundance were weaker in warmer pools. Our findings provide additional evidence to substantiate our model-derived predictions while emphasizing the importance of assessing similar predictions using natural gradients of temperature whenever possible.

Global dominance of lianas over trees is driven by forest disturbance, climate and topography

Growing evidence suggests that liana competition with trees is threatening the global carbon sink by slowing the recovery of forests following disturbance. A recent theory based on local and regional evidence further proposes that the competitive success of lianas over trees is driven by interactions between forest disturbance and climate. We present the first global assessment of liana-tree relative performance in response to forest disturbance and climate drivers. Using an unprecedented dataset, we analysed 651 vegetation samples representing 26,538 lianas and 82,802 trees from 556 unique locations worldwide, derived from 83 publications. Results show that lianas perform better relative to trees (increasing liana-to-tree ratio) when forests are disturbed, under warmer temperatures and lower precipitation and towards the tropical lowlands. We also found that lianas can be a critical factor hindering forest recovery in disturbed forests experiencing liana-favourable climates, as chronosequence data show that high competitive success of lianas over trees can persist for decades following disturbances, especially when the annual mean temperature exceeds 27.8°C, precipitation is less than 1614 mm and climatic water deficit is more than 829 mm. These findings reveal that degraded tropical forests with environmental conditions favouring lianas are disproportionately more vulnerable to liana dominance and thus can potentially stall succession, with important implications for the global carbon sink, and hence should be the highest priority to consider for restoration management.

Methods for estimating no-effect toxicity concentrations in ecotoxicology

A range of new statistical approaches is being developed and/or adopted in ecotoxicology that, when combined, can greatly improve the estimation of no-effect toxicity values from concentration-response (CR) experimental data. In particular, we compare the existing no-effect-concentration (NEC) threshold-based toxicity metric with an alternative no-significant-effect-concentration (NSEC) metric suitable for when CR data do not show evidence of a threshold effect. Using a model-averaging approach, these metrics can be combined to yield estimates of N(S)EC and of their uncertainty within a single analysis framework. The outcome is a framework for CR analysis that is robust to uncertainty in the model formulation, and for which resulting estimates can be confidently integrated into risk assessment frameworks, such as the species sensitivity distribution (SSD). Integr Environ Assess Manag 2024;20:279-293. © 2023 Commonwealth of Australia and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Risk assessment framework for pine wilt disease: Estimating the introduction pathways and multispecies interactions among the pine wood nematode, its insect vectors, and hosts in China

Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), a destructive, invasive forest pathogen, poses a serious threat to global pine forest ecosystems. The global invasion of PWN has been described based on three successive phases, introduction, establishment, and dispersal. Risk assessments of the three successive PWN invasion phases can assist in targeted management efforts. Here, we present a risk assessment framework to evaluate the introduction, establishment, and dispersal risks of PWD in China using network analysis, species distribution models, and niche concepts. We found that >88 % of PWN inspection records were from the United States, South Korea, Japan, Germany, and Mexico, and 94 % of interception records were primarily from the Jiangsu, Shanghai, Shandong, Tianjin, and Zhejiang ports. Based on the nearly current climate, the areas of PWN overlap with its host Pinus species were primarily distributed in southern, eastern, Yangtze River Basin, central, and northeastern China regions. Areas of PWN overlap with its insect vector Monochamus alternatus were primarily distributed in southern, eastern, Yangtze River Basin, central, and northeastern China regions, and those of PWN overlap with the insect vector Monochamus saltuarius were primarily distributed in eastern and northeastern China. The niche between PWN and the insect vector M. alternatus was the most similar (0.68), followed by that between PWN and the insect vector M. saltuarius (0.47). Climate change will increase the suitable probabilities of PWN and its two insect vectors occurring at high latitudes, further increasing their threat to hosts in northeastern China. This risk assessment framework for PWD could be influential in preventing the entry of the PWN and mitigating their establishment and dispersal risks in China. Our study provides substantial clues for developing a framework to improve the risk assessment and surveillance of biological invasions worldwide.

A generalisation of the method of regression calibration and comparison with the Bayesian 2-dimensional Monte Carlo method

For many cancer sites it is necessary to assess risks from low-dose exposures via extrapolation from groups exposed at moderate and high levels of dose. Measurement error can substantially alter the shape of this relationship and hence the derived population risk estimates. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, much attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. In this paper we test a Bayesian model averaging method, the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, that has been fairly recently proposed against a very newly proposed modification of the regression calibration method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. We also compared both methods against standard regression calibration and Monte Carlo maximum likelihood. The Bayesian 2DMC method performs poorly, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5%, particularly when the magnitudes of classical and Berkson error are both moderate to large (20%-50%). The method also produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. By comparison the extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the Bayesian 2DMC and all other methods. The bias of the predicted relative risk at a variety of doses is generally smallest for extended regression calibration, and largest for the Bayesian 2DMC method (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between the other two methods.

Integrating biogeographic approach into classical biological control: Assessing the climate matching and ecological niche overlap of two natural enemies against common ragweed in China

Plant invasion is considered a high priority threat to biodiversity, ecosystems, the environment, and human health worldwide. Classical biological control (biocontrol) is a generally safer and more environmentally benign measure than chemical controls in managing invasive alien plants (IAPs). However, the impacts of climate change and the importance of climate matching in ensuring the efficiency of biocontrol candidates in controlling IAPs are likely to be underestimated. Here, based on the ensemble model and n-dimensional hypervolumes concepts, we estimated the overlapping areas between Ambrosia artemisiifolia and its two most effective natural enemies (Ophraella communa and Epiblema strenuana) under climate change in China. Moreover, we compared their ecological niches, further assessing the impact of climate change on the efficiency of two natural enemies in controlling A. artemisiifolia in China. We found that the potentially suitable areas of the two natural enemies and A. artemisiifolia were primarily influenced by temperature and human influence index variables. Under near-current climate, the overlapping area between O. communa and A. artemisiifolia was the largest, followed by E. strenuana and A. artemisiifolia, and both two natural enemies and A. artemisiifolia. The ecological niche between A. artemisiifolia and O. communa was most similar (0.64), followed by A. artemisiifolia and E. strenuana (0.55). The separate control (the niche separation areas of the two natural enemies against A. artemisiifolia) and joint-control (the niche overlap areas of the two natural enemies against A. artemisiifolia) efficiencies of the two natural enemies against A. artemisiifolia will both increase in future climates (the 2030s and 2050s) in northern and northeastern China. Our findings demonstrate a new approach to assess control efficiency and screen potential release areas of two natural enemies against A. artemisiifolia in China without the need for actual field release or experimentation. Moreover, our findings provide important clues for ensuring the classical biocontrol of IAPs worldwide.

Retrospective evaluation of real-time estimates of global COVID-19 transmission trends and mortality forecasts

Since 8th March 2020 up to the time of writing, we have been producing near real-time weekly estimates of SARS-CoV-2 transmissibility and forecasts of deaths due to COVID-19 for all countries with evidence of sustained transmission, shared online. We also developed a novel heuristic to combine weekly estimates of transmissibility to produce forecasts over a 4-week horizon. Here we present a retrospective evaluation of the forecasts produced between 8th March to 29th November 2020 for 81 countries. We evaluated the robustness of the forecasts produced in real-time using relative error, coverage probability, and comparisons with null models. During the 39-week period covered by this study, both the short- and medium-term forecasts captured well the epidemic trajectory across different waves of COVID-19 infections with small relative errors over the forecast horizon. The model was well calibrated with 56.3% and 45.6% of the observations lying in the 50% Credible Interval in 1-week and 4-week ahead forecasts respectively. The retrospective evaluation of our models shows that simple transmission models calibrated using routine disease surveillance data can reliably capture the epidemic trajectory in multiple countries. The medium-term forecasts can be used in conjunction with the short-term forecasts of COVID-19 mortality as a useful planning tool as countries continue to relax public health measures.

A generalisation of the method of regression calibration

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient [Formula: see text] are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient [Formula: see text] are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for [Formula: see text] that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient [Formula: see text] are substantially upwardly biased.

Impact of climate change on the distribution and predicted habitat suitability of two fruit bats (Rousettus aegyptiacus and Epomophorus labiatus) in Ethiopia: Implications for conservation

Fruit bats serve as crucial bioindicators, seed dispersers, pollinators, and contributors to food security within ecosystems. However, their population and distribution were threatened by climate change and anthropogenic pressures. Understanding the impacts of these pressures through mapping distribution and habitat suitability is crucial for identifying high-priority areas and implementing effective conservation and management plans. We predicted the distribution and extent of habitat suitability for Rousettus aegyptiacus and Epomophorus labiatus under climate change scenarios using average predictions from four different algorithms to produce an ensemble model. Seasonal precipitation, population index, land-use land cover, vegetation, and the mean temperature of the driest quarter majorly contributed to the predicted habitat suitability for both species. The current predicted sizes of suitable habitats for R. aegyptiacus and E. labiatus were varied, on average 60,271.4 and 85,176.1 km2, respectively. The change in species range size for R. aegyptiacus showed gains in suitable areas of 24.4% and 22.8% in 2050 and 2070, respectively. However, for E. labiatus, suitable areas decreased by 0.95% and 2% in 2050 and 2070, respectively. The range size change of suitable areas between 2050 and 2070 for R. aegyptiacus and E. labiatus shows losses of 1.5% and 1.2%, respectively. The predicted maps indicate that the midlands and highlands of southern and eastern Ethiopia harbor highly suitable areas for both species. In contrast, the areas in the northern and central highlands are fragmented. The current model findings show that climate change and anthropogenic pressures have notable impacts on the geographic ranges of two species. Moreover, the predicted suitable habitats for both species are found both within and outside of their historical ranges, which has important implications for conservation efforts. Our ensemble predictions are vital for identifying high-priority areas for fruit bat species conservation efforts and management to mitigate climate change and anthropogenic pressures.

clusterBMA: Bayesian model averaging for clustering

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

Statistical methods to identify mechanisms in studies of eco-evolutionary dynamics

While the reciprocal effects of ecological and evolutionary dynamics are increasingly recognized as an important driver for biodiversity, detection of such eco-evolutionary feedbacks, their underlying mechanisms, and their consequences remains challenging. Eco-evolutionary dynamics occur at different spatial and temporal scales and can leave signatures at different levels of organization (e.g., gene, protein, trait, community) that are often difficult to detect. Recent advances in statistical methods combined with alternative hypothesis testing provides a promising approach to identify potential eco-evolutionary drivers for observed data even in non-model systems that are not amenable to experimental manipulation. We discuss recent advances in eco-evolutionary modeling and statistical methods and discuss challenges for fitting mechanistic models to eco-evolutionary data.

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.

Quantum computing: a new paradigm for ecology

A global technology arms race is underway to build evermore powerful and precise quantum computers. Quantum computers have the potential to tackle certain quantitative problems quicker than classical computers. The current focus of quantum computing is on pushing the boundaries of fundamental quantum information and commercial applications in industrial sectors, financial services, and other profit-led sectors, particularly where improvements in optimisation and sampling can improve increased economic return. We believe that ecologists could exploit the computational power of quantum computers because the statistical approaches commonly used in ecology already have proven pathways on quantum computers. Moreover, quantum computing could ultimately leapfrog our understanding of complex ecological systems, if the hardware, opportunity, and creativity of quantitative ecologists all align.

Model ensembles of ecosystem services fill global certainty and capacity gaps

Sustaining ecosystem services (ES) critical to human well-being is hindered by many practitioners lacking access to ES models ("the capacity gap") or knowledge of the accuracy of available models ("the certainty gap"), especially in the world's poorer regions. We developed ensembles of multiple models at an unprecedented global scale for five ES of high policy relevance. Ensembles were 2 to 14% more accurate than individual models. Ensemble accuracy was not correlated with proxies for research capacity, indicating that accuracy is distributed equitably across the globe and that countries less able to research ES suffer no accuracy penalty. By making these ES ensembles and associated accuracy estimates freely available, we provide globally consistent ES information that can support policy and decision-making in regions with low data availability or low capacity for implementing complex ES models. Thus, we hope to reduce the capacity and certainty gaps impeding local- to global-scale movement toward ES sustainability.

Testing multiple hypotheses on the colour change of treefrogs in response to various external conditions

Amphibians are famous for their ability to change colours. And a considerable number of studies have investigated the internal and external factors that affect the expression of this phenotypic plasticity. Evidence to date suggests that thermoregulation and camouflage are the main pressures that influence frogs' adaptive colour change responses. However, certain gaps in our knowledge of this phenomenon remain, namely: (i) how do frogs adjust their colour in response to continuously changing external conditions?; (ii) what is the direction of change when two different functions of colour (camouflage and thermoregulation) are in conflict?; (iii) does reflectance in the near-infrared region show thermally adaptive change?; and (iv) is the colour change ability of each frog an individual trait (i.e., consistent within an individual over time)? Using Dryophytes japonicus (Hylidae, Hyla), we performed a series of experiments to answer the above questions. We first showed that frogs' responses to continuously-changing external conditions (i.e., background colour and temperature) were not linear and limited to the range they experience under natural conditions. Second, when a functional conflict existed, camouflage constrained the adaptive response for thermoregulation and vice versa. Third, though both temperature and background colour induced a change in near-infrared reflectance, this change was largely explained by the high correlation between colour (reflectance in the visible spectrum) and near-infrared reflectance. Fourth, within-individual variation in colour change capacity (i.e., the degree of colour change an individual can display) was lower than inter-individual variation, suggesting individuality of colour change capacity; however, we also found that colour change capacity could change gradually with time within individuals. Our results collectively reveal several new aspects of how evolution shapes the colour change process and highlight how variation in external conditions restricts the extent of colour change in treefrogs.

Estimates of fine fuel litter biomass in the northern Great Basin reveal increases during short fire-free intervals associated with invasive annual grasses

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R² = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R² ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was <10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts.

Climate change has increased the global threats posed by three ragweeds (Ambrosia L.) in the Anthropocene

Invasive alien plants (IAPs) substantially affect the native biodiversity, agriculture, industry, and human health worldwide. Ambrosia (ragweed) species, which are major IAPs globally, produce a significant impact on human health and the natural environment. In particular, invasion of A. artemisiifolia, A. psilostachya, and A. trifida in non-native continents is more extensive and severe than that of other species. Here, we used biomod2 ensemble model based on environmental and species occurrence data to predict the potential geographical distribution, overlapping geographical distribution areas, and the ecological niche dynamics of these three ragweeds and further explored the environmental variables shaping the observed patterns to assess the impact of these IAPs on the natural environment and public health. The ecological niche has shifted in the invasive area compared with that in the native area, which increased the invasion risk of three Ambrosia species during the invasion process in the world. The potential geographical distribution and overlapping geographical distribution areas of the three Ambrosia species are primarily distributed in Asia, North America, and Europe, and are expected to increase under four representative concentration pathways in the 2050s. The centers of potential geographical distributions of the three Ambrosia species showed a tendency to shift poleward from the current time to the 2050s. Bioclimatic variables and the human influence index were more significant in shaping these patterns than other factors. In brief, climate change has facilitated the expansion of the geographical distribution and overlapping geographical distribution areas of the three Ambrosia species. Ecomanagement and cross-country management strategies are warranted to mitigate the future effects of the expansion of these ragweed species worldwide in the Anthropocene on the natural environment and public health.

Constructing model-averaging species sensitivity distributions of Phenanthrene based on reproductive fitness: Implications for assessing ecological risk in urban watershed

The challenge in optimizing the method of constructing species sensitivity distribution (SSD) remains. In this study, a model-averaging SSD was created to evaluate the ecological risk of Phenanthrene (PHE) in urban watershed based on reproductive fitness. Specifically, concentrations of PHE were measured in surface water samples collected from various watersheds of Wuhan, including five lake watersheds and the Wuhan reach of the Yangtze River and Han River. The reproductive endpoint of aquatic species was calculated to be most sensitive to PHE exposure, with the value of predict no-effect concentration (PNEC) at 0.19 μg/L. The results of probabilistic assessment methods, including joint probability curve (JPC), overall risk probability (ORP), and distribution-based quotient (DBQ), indicated that the ecological risks of PHE in large lakes have dropped significantly with distance from the downtown area of Wuhan, and the long-term effects of industrial activities may increase the risks in the lake watersheds. Basically, the ecological risks in Yangtze River are negligible; however, there is a relatively high risk of PHE in the Han River and some lake watersheds. The cos θ similarity analysis indicated the Yangtze River is strongly connected to the low-risk lake watersheds, and that in part reflects the risk in the Yangtze River being controlled by its surrounding these lake watersheds.

Constructing an Ensemble Model and Niche Comparison for the Management Planning of Eucalyptus Longhorned Borer Phoracantha semipunctata under Climate Change

Phoracantha semipunctata is a destructive invasive alien forest pest worldwide. It primarily damages the eucalyptus via adults, affecting almost all parts of the eucalyptus. Its larvae develop in almost all major tissues of the plant. Phoracantha semipunctata spreads both via the migration of adults and global trade in intercontinental translocation. Currently, this pest has spread to six continents worldwide, except Antarctica, resulting in substantial economic losses. Based on global occurrence data and environmental variables, the potential global geographical distribution of P. semipunctata was predicted using an ensemble model. The centroid shift, overlap, unfilling, and expansion scheme were selected to assess niche dynamics during the global invasion process. Our results indicated that the AUC and TSS values of the ensemble model were 0.993 and 0.917, respectively, indicating the high prediction accuracy of the model. The distribution pattern of P. semipunctata is primarily attributed to the temperature seasonality (bio4), mean temperature of the warmest quarter (bio10), and human influence index variables. The potential geographical distribution of P. semipunctata is primarily in western and southwestern Asia, western Europe, western and southern North America, southern South America, southern Africa, and eastern and southern Oceania. The potential geographical distribution of P. semipunctata showed a downward trend in the 2030s and the 2050s. The distribution centroid showed a general tendency to shift southward from the near-current to future climate. Phoracantha semipunctata has largely conserved its niche during the global invasion process. More attention should be paid to the early warning, prevention, and control of P. semipunctata in the countries and regions where it has not yet become invasive.

ALOS-2 L-band SAR backscatter data improves the estimation and temporal transferability of wildfire effects on soil properties under different post-fire vegetation responses

Remote sensing techniques are of particular interest for monitoring wildfire effects on soil properties, which may be highly context-dependent in large and heterogeneous burned landscapes. Despite the physical sense of synthetic aperture radar (SAR) backscatter data for characterizing soil spatial variability in burned areas, this approach remains completely unexplored. This study aimed to evaluate the performance of SAR backscatter data in C-band (Sentinel-1) and L-band (ALOS-2) for monitoring fire effects on soil organic carbon and nutrients (total nitrogen and available phosphorous) at short term in a heterogeneous Mediterranean landscape mosaic made of shrublands and forests that was affected by a large wildfire. The ability of SAR backscatter coefficients and several band transformations of both sensors for retrieving soil properties measured in the field in immediate post-fire situation (one month after fire) was tested through a model averaging approach. The temporal transferability of SAR-based models from one month to one year after wildfire was also evaluated, which allowed to assess short-term changes in soil properties at large scale as a function of pre-fire plant community type. The retrieval of soil properties in immediate post-fire conditions featured a higher overall fit and predictive capacity from ALOS-2 L-band SAR backscatter data than from Sentinel-1 C-band SAR data, with the absence of noticeable under and overestimation effects. The transferability of the ALOS-2 based model to one year after wildfire exhibited similar performance to that of the model calibration scenario (immediate post-fire conditions). Soil organic carbon and available phosphorous content was significantly higher one year after wildfire than immediately after the fire disturbance. Conversely, the short-term change in soil total nitrogen was ecosystem-dependent. Our results support the applicability of L-band SAR backscatter data for monitoring short-term variability of fire effects on soil properties, reducing data gathering costs within large and heterogeneous burned landscapes.

The Future of Invasion Science Needs Physiology

Incorporating physiology into models of population dynamics will improve our understanding of how and why invasions succeed and cause ecological impacts, whereas others fail or remain innocuous. Targeting both organismal physiologists and invasion scientists, we detail how physiological processes affect every invasion stage, for both plants and animals, and how physiological data can be better used for studying the spatial dynamics and ecological effects of invasive species. We suggest six steps to quantify the physiological functions related to demography of nonnative species: justifying physiological traits of interest, determining ecologically appropriate time frames, identifying relevant abiotic variables, designing experimental treatments that capture covariation between abiotic variables, measuring physiological responses to these abiotic variables, and fitting statistical models to the data. We also provide brief guidance on approaches to modeling invasions. Finally, we emphasize the benefits of integrating research between communities of physiologists and invasion scientists.

Artificial selection in human-wildlife feeding interactions

The artificial selection of traits in wildlife populations through hunting and fishing has been well documented. However, despite their rising popularity, the role that artificial selection may play in non‐extractive wildlife activities, for example, recreational feeding activities, remains unknown.

If only a subset of a population takes advantage of human‐wildlife feeding interactions, and if this results in different fitness advantages for these individuals, then artificial selection may be at work. We have tested this hypothesis using a wild fallow deer population living at the edge of a capital city as our model population.

In contrast to previous assumptions on the randomness of human‐wildlife feeding interactions, we found that a limited non‐random portion of an entire population is continuously engaging with people. We found that the willingness to beg for food from humans exists on a continuum of inter‐individual repeatable behaviour; which ranges from risk‐taking individuals repeatedly seeking and obtaining food, to shyer individuals avoiding human contact and not receiving food at all, despite all individuals having received equal exposure to human presence from birth and coexisting in the same herds together. Bolder individuals obtain significantly more food directly from humans, resulting in early interception of food offerings and preventing other individuals from obtaining supplemental feeding.

Those females that beg consistently also produce significantly heavier fawns (300–500 g heavier), which may provide their offspring with a survival advantage. This indicates that these interactions result in disparity in diet and nutrition across the population, impacting associated physiology and reproduction, and may result in artificial selection of the begging behavioural trait.

This is the first time that this consistent variation in behaviour and its potential link to artificial selection has been identified in a wildlife population and reveals new potential effects of human‐wildlife feeding interactions in other species across both terrestrial and aquatic habitats.

Exploring the Multiverse of Analytical Decisions in Scaling Educational Large-Scale Assessment Data: A Specification Curve Analysis for PISA 2018 Mathematics Data

In educational large-scale assessment (LSA) studies such as PISA, item response theory (IRT) scaling models summarize students' performance on cognitive test items across countries. This article investigates the impact of different factors in model specifications for the PISA 2018 mathematics study. The diverse options of the model specification also firm under the labels multiverse analysis or specification curve analysis in the social sciences. In this article, we investigate the following five factors of model specification in the PISA scaling model for obtaining the two country distribution parameters; country means and country standard deviations: (1) the choice of the functional form of the IRT model, (2) the treatment of differential item functioning at the country level, (3) the treatment of missing item responses, (4) the impact of item selection in the PISA test, and (5) the impact of test position effects. In our multiverse analysis, it turned out that model uncertainty had almost the same impact on variability in the country means as sampling errors due to the sampling of students. Model uncertainty had an even larger impact than standard errors for country standard deviations. Overall, each of the five specification factors in the multiverse analysis had at least a moderate effect on either country means or standard deviations. In the discussion section, we critically evaluate the current practice of model specification decisions in LSA studies. It is argued that we would either prefer reporting the variability in model uncertainty or choosing a particular model specification that might provide the strategy that is most valid. It is emphasized that model fit should not play a role in selecting a scaling strategy for LSA applications.

A comparison in species distribution model performance of succulents using key species and subsets of environmental predictors

Identifying the environmental drivers of the global distribution of succulent plants using the Crassulacean acid metabolism pathway of photosynthesis has previously been investigated through ensemble-modeling of species delimiting the realized niche of the natural succulent biome. An alternative approach, which may provide further insight into the fundamental niche of succulent plants in the absence of dispersal limitation, is to model the distribution of selected species that are globally widespread and have become naturalized far beyond their native habitats. This could be of interest, for example, in defining areas that may be suitable for cultivation of alternative crops resilient to future climate change. We therefore explored the performance of climate-only species distribution models (SDMs) in predicting the drivers and distribution of two widespread CAM plants, Opuntia ficus-indica and Euphorbia tirucalli. Using two different algorithms and five predictor sets, we created distribution models for these exemplar species and produced an updated map of global inter-annual rainfall predictability. No single predictor set produced markedly more accurate models, with the basic bioclim-only predictor set marginally out-performing combinations with additional predictors. Minimum temperature of the coldest month was the single most important variable in determining spatial distribution, but additional predictors such as precipitation and inter-annual precipitation variability were also important in explaining the differences in spatial predictions between SDMs. When compared against previous projections, an a posteriori approach correctly does not predict distributions in areas of ecophysiological tolerance yet known absence (e.g., due to biotic competition). An updated map of inter-annual rainfall predictability has successfully identified regions known to be depauperate in succulent plants. High model performance metrics suggest that the majority of potentially suitable regions for these species are predicted by these models with a limited number of climate predictors, and there is no benefit in expanding model complexity and increasing the potential for overfitting.

Sampling and modelling rare species: Conceptual guidelines for the neglected majority

Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

What Does the Future Hold for Páramo Plants? A Modelling Approach

Understanding the effects of climate change on tropicalpine biota remains a scientific challenge today. The Andean páramo is the largest and most diverse tropicalpine biogeographical region in the world, and also one of the most threatened as it is prone to accelerated environmental changes. My goal was to predict changes in the distribution ranges of the diverse and highly endemic páramo flora on the mid-term (50 years). First, I predicted distribution changes in páramo plant species under novel climates and considering dispersal constraints. Second, I looked for consensus areas of species losses vs. gains in the páramo, expecting to identify a gradient of increasing relative richness with elevation over time. Last, I evaluated the behavior of plant species regarding their climatic refugia since the Last Glacial Maximum (LGM) to establish if they likely remain or transcend them. Based on VegParamo vegetation data and CHELSA bioclimatic information, I performed species distribution models for a 664 species pool, that were then contrasted between the present, future (2070) and past (LGM). About 8.3% of the entire species pool (55 species) were predicted to be extirpated from the páramo by 2070, including 22 species endemics. On average, páramo plants gained 15.52% of additional distribution by 2070 (18.81% for endemics). Models predicted the most area gains for the northern páramos of Colombia and Venezuela, and the highest losses for the eastern Ecuadorian and Peruvian mountains. Moreover, area gains were more pronounced at high elevations, suggesting a future accelerated colonization process toward the northern Andean summits. Finally, only 21.41% of the species’ 2070 distribution coincided with their LGM (19.75% for endemics), and the largest climatic refugia since the LGM were found in southern Ecuador and Peru. This study is pioneer in predicting future distribution shifts for páramo plant species overall and provides solid bases to support climate change research and adaptation strategies in the tropical Andes.

Distribution and biomass of gelatinous zooplankton in relation to an oxygen minimum zone and a shallow seamount in the Eastern Tropical North Atlantic Ocean

Physical and topographic characteristics can structure pelagic habitats and affect the plankton community composition. For example, oxygen minimum zones (OMZs) are expected to lead to a habitat compression for species with a high oxygen demand, while upwelling of nutrient-rich deep water at seamounts can locally increase productivity, especially in oligotrophic oceanic waters. Here we investigate the response of the gelatinous zooplankton (GZ) assemblage and biomass to differing oxygen conditions and to a seamount in the Eastern Tropical North Atlantic (ETNA) around the Cape Verde archipelago. A total of 16 GZ taxa (>1100 specimens) were found in the upper 1000 m with distinct species-specific differences, such as the absence of deep-living species Atolla wyvillei and Periphylla periphylla above the shallow seamount summit. Statistical analyses considering the most prominent groups, present at all stations, namely Beroe spp., hydromedusae (including Zygocanna vagans, Halicreas minimum, Colobonema sericeum, Solmissus spp.) and total GZ, showed a strong positive correlation of abundance with temperature for all groups, whereas oxygen had a weak negative correlation only with abundances of Beroe spp. and hydromedusae. To account for size differences between species, we established length-weight regressions and investigated total GZ biomass changes in relation to physical (OMZ) and topographic characteristics. The highest GZ biomass was observed at depths of lowest oxygen concentrations and deepest depth strata at the southeastern flank of the seamount and at two stations south of the Cape Verde archipelago. Our data suggest that, irrespective of their patchy distribution, GZ organisms are ubiquitous food web members of the ETNA, and their habitat includes waters of low oxygen content.