Robust steady states in ecosystems with symmetries

Steady states of dynamical systems, whether stable or unstable, are critical for understanding future evolution. Robust steady states, ones that persist under small changes in the model parameters, are desired when modelling ecological systems, where it is common for accurate and detailed information on functional form and parameters to be unavailable. Previous work by Jahedi et al. [Robustness of solutions of almost every system of equations, SIAM J. Appl. Math. 82(5) (2022), pp. 1791-1807; Structured systems of nonlinear equations, SIAM J. Appl. Math. 83(4) (2023), pp. 1696-1716.] has established criteria to imply the prevalence of robust steady states for systems with minimal predetermined structure, including conventional structured systems. We review that work and extend it by allowing symmetries in the system structure, which present added obstructions to robustness.

Erratum: Publisher Correction: wMel replacement of dengue-competent mosquitoes is robust to near-term climate change

[This corrects the article DOI: 10.1038/s41558-023-01746-w.].

wMel replacement of dengue-competent mosquitoes is robust to near-term change

Rising temperatures are impacting the range and prevalence of mosquito-borne diseases. A promising biocontrol technology replaces wild mosquitoes with those carrying the virus-blocking Wolbachia bacterium. Because the most widely used strain, wMel, is adversely affected by heat stress, we examined how global warming may influence wMel-based replacement. We simulated interventions in two locations with successful field trials using Coupled Model Intercomparison Project Phase 5 climate projections and historical temperature records, integrating empirical data on wMel's thermal sensitivity into a model of Aedes aegypti population dynamics to evaluate introgression and persistence over one year. We show that in Cairns, Australia, climatic futures necessitate operational adaptations for heatwaves exceeding two weeks. In Nha Trang, Vietnam, projected heatwaves of three weeks and longer eliminate wMel under the most stringent assumptions of that symbiont's thermal limits. We conclude that this technology is generally robust to near-term (2030s) climate change. Accelerated warming may challenge this in the 2050s and beyond.

Ecological indicators reveal historical regime shifts in the Black Sea ecosystem

Background

The Black Sea is one of the most anthropogenically disturbed marine ecosystems in the world because of introduced species, fisheries overexploitation, nutrient enrichment via pollution through river discharge, and the impacts of climate change. It has undergone significant ecosystem transformations since the 1960s. The infamous anchovy and alien warty comb jelly Mnemiopsis leidyi shift that occurred in 1989 is the most well-known example of the drastic extent of anthropogenic disturbance in the Black Sea. Although a vast body of literature exists on the Black Sea ecosystem, a holistic look at the multidecadal changes in the Black Sea ecosystem using an ecosystem- and ecology-based approach is still lacking. Hence, this work is dedicated to filling this gap.

Methods

First, a dynamic food web model of the Black Sea extending from 1960 to 1999 was established and validated against time-series data. Next, an ecological network analysis was performed to calculate the time series of synthetic ecological indicators, and a regime shift analysis was performed on the time series of indicators.

Results

The model successfully replicated the regime shifts observed in the Black Sea. The results showed that the Black Sea ecosystem experienced four regime shifts and was reorganized due to effects instigated by overfishing in the 1960s, eutrophication and establishment of trophic dead-end organisms in the 1970s, and overfishing and intensifying interspecies trophic competition by the overpopulation of some r-selected organisms (i.e., jellyfish species) in the 1980s. Overall, these changes acted concomitantly to erode the structure and function of the ecosystem by manipulating the food web to reorganize itself through the introduction and selective removal of organisms and eutrophication. Basin-wide, cross-national management efforts, especially with regard to pollution and fisheries, could have prevented the undesirable changes observed in the Black Sea ecosystem and should be immediately employed for management practices in the basin to prevent such drastic ecosystem fluctuations in the future.

Using a dynamic energy budget model to investigate the physiological mode of action of lead (Pb) to Lymnaea stagnalis

Lymnaea stagnalis is a notably sensitive species for a variety of metals, including lead (Pb). However, the mechanism(s) of lead toxicity to L. stagnalis currently remain incompletely understood. Under dynamic energy budget (DEB) theory, different physiological modes of action (PMoAs) result in the emergence of distinct changes to the life histories of exposed organisms. This work aims to better understand the PMoA of lead toxicity to L. stagnalis by applying DEB modeling to previously published datasets. After calibration, the model was utilized to evaluate the relative likelihood of several PMoAs. Assuming decreased assimilation, the L. stagnalis DEB model was able to capture most, but not all, trends in experimentally observed endpoints, including growth, reproduction, and food ingestion. The weight-of-evidence suggests that decreased assimilation via a decrease in food ingestion is the most plausible PMoA for chronic lead toxicity in L. stagnalis. Collectively, our results illustrate how mechanistic modeling can create added value for conventional individual-level toxicity test data by enabling inferences about potential physiological mechanisms of toxicity.

Modifying and parameterizing the individual-based model inSTREAM for Atlantic salmon and brown trout in the regulated Gullspång River, Sweden

We modified, parameterized, and applied the individual-based model inSTREAM version 6.1 for lake-migrating populations of landlocked Atlantic salmon (Salmo salar) and brown trout (S. trutta) in a residual flow stretch of the hydropower-regulated Gullspång River, Sweden. This model description is structured according to the TRACE model description framework. Our aim was to model responses in salmonid recruitment to alternative scenarios of flow release and other environmental alterations. The main response variable was the number of large out-migrating juvenile fish per year, with the assumption that individuals are more inclined to out-migrate the larger they get, and that migration is an obligatory strategy. Population and species-specific parameters were set based on local electrofishing surveys, redd surveys, physical habitat surveys, broodstock data as well as scientific literature.•Simulations were set to run over 10 years, with sub-daily time steps, in this spatially and temporally explicit model.•Model calibration and validation of fish growth was done using data on juvenile fish from electrofishing.•The results were found to be sensitive to parameter values for aggregated fish, i.e., "superindividuals" and for the high temperature limit to spawning.

Unlocking environmental contamination of animal tuberculosis hotspots with viable mycobacteria at the intersection of flow cytometry, PCR, and ecological modelling

Mycobacterium bovis, a member of the Mycobacterium tuberculosis complex (MTBC), circulates in multi-host mammal communities. While interactions between different host species are mainly indirect, current knowledge postulates interspecific transmission is favored by animal contact with natural substrates contaminated with droplets and fluids from infected animals. However, methodological constraints have severely hampered monitoring of MTBC outside its hosts and the subsequent validation of this hypothesis. In this work, we aimed to evaluate the extent to which environmental contamination with M. bovis occurs in an endemic animal TB setting, taking advantage of a new real-time monitoring tool we recently developed to quantify the proportion of viable and dormant MTBC cell fractions in environmental matrices. Sixty-five natural substrates were collected nearby the International Tagus Natural Park region, in the epidemiological TB risk area in Portugal. These included sediments, sludge, water, and food deployed at unfenced feeding stations. The tripartite workflow included detection, quantification, and sorting of different M. bovis cell populations: total, viable, and dormant. Real-time PCR targeting IS6110 to detect MTBC DNA was performed in parallel. The majority of samples (54 %) contained metabolically active or dormant MTBC cells. Sludge samples had a higher burden of total MTBC cells and a high concentration of viable cells (2.3 × 10⁴ cells/g). Ecological modelling informed by climate, land use, livestock and human disturbance data suggested eucalyptus forest and pasture cover as potential major factors affecting the occurrence of viable MTBC cells in natural matrices. Our study demonstrates, for the first time, the widespread environmental contamination of animal TB hotspots with viable MTBC bacteria and with dormant MTBC cells that are able to recover metabolic activity. Further, we show that viable MTBC cell load in natural substrates is superior to the estimated minimum infective dose, providing real-time insights into the potential magnitude of environmental contamination for indirect TB transmission.

Evaluating and explaining the variability of honey bee field studies across Europe using BEEHAVE

To assess the effect of plant protection products on pollinator colonies, the higher tier of environmental risk assessment (ERA) for managed honey bee colonies and other pollinators, is in need of a mechanistic effect model. Such models are seen as a promising solution to the shortcomings, which empirical risk assessment can only overcome to a certain degree. A recent assessment of 40 models conducted by EFSA revealed that BEEHAVE is currently the only publicly available mechanistic honey bee model that has the potential to be accepted for ERA purposes. A concern towards the use of this model is a lack of model validation against empirical data, spanning field studies conducted in different regions of Europe and covering the variability in colony conditions and environmental conditions. Here, we fill this gap with a BEEHAVE validation study against 66 control colonies of field studies conducted across Germany, Hungary, and the UK. Our study implements realistic initial colony size and landscape structure to consider foraging options. Overall, the temporal pattern of colony strength is predicted well. Some discrepancies between experimental data and prediction outcomes are explained by assumptions made for model parameterisation. Complementary to the recent EFSA study using BEEHAVE our validation covers a large variability in colony conditions and environmental impacts representing the Northern and Central European regulatory zone. Thus, we believe that BEEHAVE can be used to serve the development of Specific Protection Goals and the development of simulation scenarios for the European Regulatory Zone. Subsequently, the model can be applied as a standard tool for higher tier ERA of managed honey bees using the mechanistic ecotoxicological module for BEEHAVE: BEEHAVE_ecotox .

Setting sustainable limits on anchoring to improve the resilience of coral reefs

Boat anchoring is common at coral reefs that have high economic or social value, but anchoring has received relatively little attention in reef resilience studies. We developed an individual-based model of coral populations and simulated the effects of anchor damage over time. The model allowed us to estimate the carrying capacity of anchoring for four different coral assemblages and different starting levels of coral cover. The carrying capacity of small to medium-sized recreational vessels across these four assemblages was between 0 and 3.1 anchor strikes ha^-1 day^-1. In a case study of two Great Barrier Reef archipelagos, we modelled the benefits of anchoring mitigation under bleaching regimes expected for four climate scenarios. The partial mitigation of even a very mild anchoring incidence (1.17 strikes ha^-1 day^-1) resulted in median coral gains of 2.6-7.7 % absolute cover under RCP2.6, though benefits varied temporally and depended on the Atmosphere-Ocean General Circulation Model used.

The effects of intraspecific variation on forecasts of species range shifts under climate change

As global climate change is altering the distribution range of macroalgae across the globe, it is critical to assess its impact on species range shifts to inform the biodiversity conservation of macroalgae. Latitude/environmental gradients could cause intraspecific variability, which may result in distinct responses to climate change. It remains unclear whether geographical variation occurs in the response of species' populations to climate change. We tested this assumption using the brown alga Sargassum thunbergii, a habitat-forming macroalgae encompassing multiple divergent lineages along the Northwest Pacific. Previous studies revealed a distinct lineage of S. thunbergii in rear-edge populations. Given the phylogeographic structure and temperature gradients, we divided these populations into the southern and northern groups. We assessed the physiological responses of the two groups to temperature changes and estimated their niche differences using n-dimensional hypervolumes. A higher photosynthetic rate and antioxidative abilities were detected in the southern group of S. thunbergii than in the northern group. In addition, significant niche differentiation was detected between the two groups, suggesting the possibility for local adaptation. Given these results, we inferred that the southern group (rear-edge populations) may be more resilient to climate change. To examine climate-driven range shifts of S. thunbergii, we constructed species- and lineage-level species distribution models (SDMs). Predictions of both levels showed considerable distribution contracts along the Chinese coasts in the future. For the southern group, the lineage-level model predicted less habitat loss than the species-level model. Our results highlight the importance of considering intraspecific variation in climate change vulnerability assessments for coastal species.

Evolution of harmful algal blooms in the East China Sea under eutrophication and warming scenarios

Harmful algal blooms (HABs) worldwide are experiencing obvious changes under the combined impacts of global warming, eutrophication, and other driving forces. In the East China Sea (ECS), large-scale blooms caused by dinoflagellates occurred since 2000 and there has been an apparent shift of bloom-causative microalgae from diatoms to dinoflagellates. To predict the future evolution of HABs in this region, a model was developed based on the competition between diatoms and dinoflagellates, which would serve to reproduce the seasonal succession of microalgal blooms driven by multiple environmental factors. The evolution features of HABs were then projected under different scenarios of eutrophication and global warming. Under the 'business as usual' scenario, dinoflagellate blooms are expected to become more frequent with higher peak biomass concentrations over the next 30 years. Changes in nutrient composition of the Changjiang riverine discharge may largely give rise to this phenomenon, and accelerated warming associated with climate change may result in earlier occurrence of dinoflagellate blooms. To prevent further intensification of dinoflagellate blooms, efforts could be made to reduce nitrogen inputs and maintain or even increase silicate inputs from the Changjiang river.

Antimicrobial resistance in commensal Staphylococcus aureus from wild ungulates is driven by agricultural land cover and livestock farming

Staphylococcus aureus is a human pathobiont (i.e., a commensal microorganism that is potentially pathogenic under certain conditions), a nosocomial pathogen and a leading cause of morbidity and mortality in humans. S. aureus is also a commensal and pathogen of companion animals and livestock. The dissemination of antimicrobial resistant (AMR) S. aureus, particularly methicillin-resistant (MRSA), has been associated to its ability for establishing new reservoirs, but limited attention has been devoted to the role of the environment. To fill this gap, we aimed to characterize animal carrier status, AMR phenotypes, predominant clonal lineages and their relationship with clinical and food-chain settings, as well as to find predictors of AMR occurrence. Nasal swabs (n = 254) from wild boar (n = 177), red deer (n = 54) and fallow deer (n = 23) hunted in Portugal, during the season 2019/2020, yielded an overall carrier proportion of 35.8%, ranging from 53.7% for red deer and 32.2% for wild boar to 21.7% for fallow deer. MRSA from wild boar and phenotypically linezolid-resistant S. aureus from wild boar and red deer were isolated, indicating that resistance to antimicrobials restricted to clinical practice also occurs in wildlife. The most prevalent genotypes were t11502/ST2678 (29.6%) and t12939/ST2678 (9.4%), previously reported in wild boar from Spain. Clonal lineages reported in humans and livestock, like CC1, CC5 or CC8 (19.1%) and ST425, CC133 or CC398 (23.5%), respectively, were also found. The sequence type ST544, previously restricted to humans, is described in wildlife for the first time. We also identified that land use (agricultural land cover), human driven disturbance (swine abundance) and host-related factors (sex) determine resistance occurrence. These findings suggest that antibiotics used in clinical settings, agriculture and livestock farming, spill over to wildlife, leading to AMR emergence, with potential biological, ecological, and human health effects. This work is one of the most comprehensive surveys in Europe of S. aureus occurrence and determinants among widely distributed wild ungulates.

Integrating Bayesian Belief Networks in a toolbox for decision support on plastic clean-up technologies in rivers and estuaries

Current mitigation strategies to offset marine plastic pollution, a global concern, typically rely on preventing floating debris from reaching coastal ecosystems. Specifically, clean-up technologies are designed to collect plastics by removing debris from the aquatic environment such as rivers and estuaries. However, to date, there is little published data on their potential impact on riverine and estuarine organisms and ecosystems. Multiple parameters might play a role in the chances of biota and organic debris being unintentionally caught within a mechanical clean-up system, but their exact contribution to a potential impact is unknown. Here, we identified four clusters of parameters that can potentially determine the bycatch: (i) the environmental conditions in which the clean-up system is deployed, (ii) the traits of the biota the system interacts with, (iii) the traits of plastic items present in the system, and, (iv) the design and operation of the clean-up mechanism itself. To efficiently quantify and assess the influence of each of the clusters on bycatch, we suggest the use of transparent and objective tools. In particular, we discuss the use of Bayesian Belief Networks (BBNs) as a promising probabilistic modelling method for an evidence-based trade-off between removal efficiency and bycatch. We argue that BBN probabilistic models are a valuable tool to assist stakeholders, prior to the deployment of any clean-up technology, in selecting the best-suited mechanism to collect floating plastic debris while managing potential adverse effects on the ecosystem.

Comprehensive modelling and cost-benefit optimization for joint regulation of algae in urban water system

Algal blooms in urban water system is an international concern, which especially in China, have become a major obstacle to the urban water environment improvement since the preliminary achievements were made in the treatment of black and odorous water bodies. The complex blooming mechanisms require a joint regulation plan. This study established a framework that consisted of three steps, i.e., simulation, optimization, and verification, to build an optimal joint regulation plan. By taking the urban river network in Suzhou Pingjiang Xincheng as a case study, the cost-benefits of six alternative regulation measures were assessed using an algal bloom mechanism model and the discounted cash flow model based on 70 regulation scenarios. The joint regulation plan was optimized using the marginal-cost-based greedy strategy on the basis of the cost-benefits of different measures. The optimized joint plans, which were verified to be global optima, were more cost-effective than the designed regulation scenarios, and reduced the average chlorophyll-a concentrations by 55.3%-60.1% compared with the status quo. Applying the optimized cost allocation ratios of each measure to adjust the existing regulation scheme of another similar case verified that the optimization results had great generalizability.

A habitat suitability model for aquaculture site selection: Ria de Aveiro and Rias Baixas

Aquaculture is one of the fastest-growing activities worldwide. This strong and rapid development of the sector tends to be reflected in significant environmental impacts and new challenges in the management of the coastal areas. In this context, this work intends to contribute to the sustainability of the sector, by proposing an innovative method to identify preferred locations to ensure sustainable expansion of fish and mussels aquaculture, under optimal hydrodynamic and water quality conditions in Ria de Aveiro (Portugal) and Rias Baixas (Spain). A habitat model was developed, integrating hydrodynamic and water quality modelling results into a suitability index based on the definition of variable suitability functions. The results show that 22% of Ria de Aveiro is very good for fish production. In contrast, the production of pelagic fish in Rias Baixas is not recommended due to vertical gradients of water temperature and seasonal events of hypoxia. Concerning to mussels, the habitat model classifies 31% of Ria de Aveiro area as very good for production, while most of the Rias Baixas area presents this highest classification, confirming the high exploitation of the region. The definition of appropriate areas for aquaculture exploitation is highly related with the different geomorphological, hydrological and biogeochemical processes of Ria de Aveiro and Rias Baixas. Results for Ria de Aveiro indicate that the upstream areas are the most vulnerable from the water quality point of view, highlighting the importance of the advective processes in the lagoon's water quality, in opposition to Rias Baixas dynamics, where stratification is more relevant. In Rias Baixas, the strong vertical gradient of water temperature and dissolved oxygen prevents fish from having sustainable growth rates. Therefore, this work demonstrated the potential of the proposed method based on hydrodynamic and biogeochemical modelling to support the decision-making process in planning aquaculture expansion.

AlleleShift: an R package to predict and visualize population-level changes in allele frequencies in response to climate change

Background

At any particular location, frequencies of alleles that are associated with adaptive traits are expected to change in future climates through local adaption and migration, including assisted migration (human-implemented when climate change is more rapid than natural migration rates). Making the assumption that the baseline frequencies of alleles across environmental gradients can act as a predictor of patterns in changed climates (typically future but possibly paleo-climates), a methodology is provided by AlleleShift of predicting changes in allele frequencies at the population level.

Methods

The prediction procedure involves a first calibration and prediction step through redundancy analysis (RDA), and a second calibration and prediction step through a generalized additive model (GAM) with a binomial family. As such, the procedure is fundamentally different to an alternative approach recently proposed to predict changes in allele frequencies from canonical correspondence analysis (CCA). The RDA step is based on the Euclidean distance that is also the typical distance used in Analysis of Molecular Variance (AMOVA). Because the RDA step or CCA approach sometimes predict negative allele frequencies, the GAM step ensures that allele frequencies are in the range of 0 to 1.

Results

AlleleShift provides data sets with predicted frequencies and several visualization methods to depict the predicted shifts in allele frequencies from baseline to changed climates. These visualizations include 'dot plot' graphics (function shift.dot.ggplot), pie diagrams (shift.pie.ggplot), moon diagrams (shift.moon.ggplot), 'waffle' diagrams (shift.waffle.ggplot) and smoothed surface diagrams of allele frequencies of baseline or future patterns in geographical space (shift.surf.ggplot). As these visualizations were generated through the ggplot2 package, methods of generating animations for a climate change time series are straightforward, as shown in the documentation of AlleleShift and in the supplemental videos.

Availability

AlleleShift is available as an open-source R package from https://cran.r-project.org/package=AlleleShift and https://github.com/RoelandKindt/AlleleShift. Genetic input data is expected to be in the adegenet::genpop format, which can be generated from the adegenet::genind format. Climate data is available from various resources such as WorldClim and Envirem.

Ecological Modelling of Insect Movement in Cropping Systems

The spatio-temporal dynamics of insect pests in agricultural landscapes involves the potential of species to move, invade, colonise, and establish in different areas. This study revised the dispersal of the important crop pests Diabrotica speciosa Germar and Spodoptera frugiperda (J.E. Smith) by using computational modelling to represent the movement of these polyphagous pests in agricultural mosaics. The findings raise significant questions regarding the dispersal of pests through crops and refuge areas, indicating that understanding pest movement is essential for developing strategies to predict critical infestation levels to assist in pest-management decisions. In addition, our modelling approach can be adapted for other insect species and other cropping systems despite discussing two specific species in the current manuscript. We present an overview of studies, combining experimentation and ecological modelling, discussing the methods used and the importance of studying insect movement as well as the implications for agricultural landscapes in Brazil.

Next-generation ensemble projections reveal higher climate risks for marine ecosystems

Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning.

Towards Building a Sustainable Future: Positioning Ecological Modelling for Impact in Ecosystems Management

As many ecosystems worldwide are in peril, efforts to manage them sustainably require scientific advice. While numerous researchers around the world use a great variety of models to understand ecological dynamics and their responses to disturbances, only a small fraction of these models are ever used to inform ecosystem management. There seems to be a perception that ecological models are not useful for management, even though mathematical models are indispensable in many other fields. We were curious about this mismatch, its roots, and potential ways to overcome it. We searched the literature on recommendations and best practices for how to make ecological models useful to the management of ecosystems and we searched for 'success stories' from the past. We selected and examined several cases where models were instrumental in ecosystem management. We documented their success and asked whether and to what extent they followed recommended best practices. We found that there is not a unique way to conduct a research project that is useful in management decisions. While research is more likely to have impact when conducted with many stakeholders involved and specific to a situation for which data are available, there are great examples of small groups or individuals conducting highly influential research even in the absence of detailed data. We put the question of modelling for ecosystem management into a socio-economic and national context and give our perspectives on how the discipline could move forward.

Quantifying local ecological knowledge to model historical abundance of long-lived, heavily-exploited fauna

Deriving robust historical population trends for long-lived species subject to human exploitation is challenging in scenarios where long-term scientific data are scarce or unavailable, as often occurs for species affected by small-scale fisheries and subsistence hunting. The importance of Local Ecological Knowledge (LEK) in data-poor scenarios is increasingly recognized in conservation, both in terms of uncovering historical trends and for engaging community stewardship of historic information. Building on previous work in marine historical ecology and local ecological knowledge, we propose a mixed socio-ecological framework to reliably document and quantify LEK to reconstruct historical population trends. Our method can be adapted by interdisciplinary teams to study various long-lived taxa with a history of human use. We demonstrate the validity of our approach by reconstructing long-term abundance data for the heavily-exploited East Pacific green turtle (Chelonia mydas) in Baja California, Mexico, which was driven to near extinction by a largely unregulated fishery from the early 1950s to the 1980s. No scientific baseline abundance data were available for this time-frame because recent biological surveys started in 1995 after all green turtle fisheries in the area were closed. To fill this data gap, we documented LEK among local fishers using ethnographic methods and obtained verified, qualitative data to understand the socio-environmental complexity of the green turtle fishery. We then established an iterative framework to synthesize and quantify LEK using generalized linear models (GLMs) and nonlinear regression (NLR) to generate a standardized, LEK-derived catch-per-unit-effort (CPUE) time-series. CPUE is an index of abundance that is compatible with contemporary scientific survey data. We confirmed the accuracy of LEK-derived CPUE estimates via comparisons with fisheries statistics available for 1962–1982. We then modeled LEK-derived abundance trends prior to 1995 using NLR. Our model established baseline abundance and described historical declines, revealing that the most critical (exponential) decline occurred between 1960 and 1980. This robust integration of LEK data with ecological science is of critical value for conservation and management, as it contributes to a holistic view of a species’ historic and contemporary conservation status.

Challenges and emerging systems biology approaches to discover how the human gut microbiome impact host physiology

Research in the human gut microbiome has bloomed with advances in next generation sequencing (NGS) and other high-throughput molecular profiling technologies. This has enabled the generation of multi-omics datasets which holds promises for big data-enabled knowledge acquisition in the form of understanding the normal physiological and pathological involvement of gut microbiomes. Ample evidence suggests that distinct microbial compositions in the human gut are associated with different diseases. However, the biological mechanisms underlying these associations are often unclear. There is a need to move beyond statistical associations to discover how changes in the gut microbiota mechanistically affect host physiology and disease development. This review summarises state-of-the-art big data and systems biology approaches for mechanism discovery.

Applying a combined geospatial and farm scale model to identify suitable locations for mussel farming

Mussel farming has increasingly come into focus as a potential mitigation measure for fish farms and eutrophication, in addition to being a food source. This study presents a GIS-based suitability analysis combined with a farm scale model to identify appropriate mussel farming sites. The sites are investigated in terms of potential mussel harvest, nutrient removal, and effects on water transparency. The model is applied to the south-western Baltic Sea. The identified suitable area is about 5-8% of the case study extent. The model shows that elevated chlorophyll levels stimulate mussel growth and that upon mussel harvest, nutrients can be removed. A single mussel farm cannot compensate for all nutrients emitted by a fish farm, but it can increase water transparency up to at least 200 m from the farm. Potential nutrient removal and water transparency increases are essential criteria for site selection in eutrophic seas, such as the Baltic Sea.

Calculation procedure for RITY-A phenology model of Ips typographus

The RITY-2 phenology model was developed for the spatiotemporal simulation of the seasonal development of European spruce bark beetle, Ips typographus. RITY-2 is based on the PHENIPS model and was developed through improving PHENIPS with innovative approaches and calibrating and validating it for Slovenia. RITY-2 predictions are based on air temperatures from Integrated Nowcasting through a Comprehensive Analysis (INCA) system, which is used to calculate the effective bark temperature for beetle development. In this paper we describe the calculation procedure for RITY-2.•

INCA enables high resolution spatial and temporal simulations and predictions.

•

An innovative procedure was introduced that finds the most appropriate spring date threshold from which the calculation of the phenological model is initiated.

•

Simplified and customized linear models for calculation of the air temperature in the forest and bark temperatures were developed.

Urban effluents affect the early development stages of Brazilian fish species with implications for their population dynamics

The pollution from urban effluents discharged into natural waters is a major cause of aquatic biodiversity loss. Ecotoxicological testing contributes significantly to understand the risk of exposure to the biota and to establish conservation policies. The objective of the current study was to assess the toxicity of a river highly influenced by urban effluents (Atuba River, Curitiba city, Southern Brazil) to the early stages of development in four South American native fish species, investigating the consequences at the population level through mathematical modelling. The species chosen were Salminus brasiliensis, Prochilodus lineatus, Rhamdia quelen, and Pseudoplatystoma corruscans, ecologically important species encompassing different conservation statuses and vulnerability. The embryos were exposed from 8 to 96 h post fertilization to the Atuba River water, collected downstream of the largest wastewater treatment plant in the Metropolitan Region of Curitiba, and their survival rates and deformities were registered. The species S. brasiliensis and P. lineatus presented the highest mortality rates, showing high sensitivity to the pollutants present in the water. According to the individual-based mathematical model, these species showed high vulnerability and risk of extinction under the tested experimental conditions, even when different sensitivity scenarios of juveniles and adults were considered. The other two species, R. quelen and P. corruscans, showed a more resistant condition to mortality, but also presented high frequency and severity of deformities. These results emphasize the importance of testing the sensitivity of different Brazilian native species for the conservation of biodiversity and the application of models to predict the effects of pollutants at the population level.

Ecological niche modelling to estimate the distribution of Culicoides, potential vectors of bluetongue virus in Senegal

BACKGROUND

Vector-borne diseases are among the leading causes of morbidity and mortality in humans and animals. In the Afrotropical region, some are transmitted by Culicoides, such as Akabane, bluetongue, epizootic haemorrhagic fever and African horse sickness viruses. Bluetongue virus infection has an enormous impact on ruminant production, due to its high morbidity and mortality rates.

METHODS

A nationwide Culicoides trapping campaign was organized at the end of the 2012 rainy season in Senegal. A Maximum Entropy approach (MaxEnt), Boosted Regression Tree (BRT) method and Ecological Niche Factor Analysis (ENFA) were used to develop a predictive spatial model for the distribution of Culicoides, using bio-climatic variables, livestock densities and altitude.

RESULTS

The altitude, maximum temperature of the warmest month, precipitation of the warmest quarter, mean temperature of the wettest quarter, temperature seasonality, precipitation of the wettest quarter and livestock density were among the most important factors to predict suitable habitats of Culicoides. Culicoides occurrences were, in most of the cases, positively correlated to precipitation variables and livestock densities; and negatively correlated to the altitude and temperature indices. The Niayes area and the Groundnut basin were the most suitable habitats predicted.

CONCLUSION

We present ecological niche models for different Culicoides species, namely C. imicola, C. oxystoma, C. enderleini and C. miombo, potential vectors of bluetongue virus, on a nationwide scale in Senegal. Through our modelling approach, we were able to determine the effect of bioclimatic variables on Culicoides habitats and were able to generate maps for the occurrence of Culicoides species. This information will be helpful in developing risk maps for disease outbreaks.

Let's Train More Theoretical Ecologists - Here Is Why

A tangled web of vicious circles, driven by cultural issues, has prevented ecology from growing strong theoretical roots. Now this hinders development of effective conservation policies. To overcome these barriers in view of urgent societal needs, we propose a global network of postgraduate theoretical training programs.

Deriving predicted no-effect concentrations (PNECs) for emerging contaminants in the river Po, Italy, using three approaches: Assessment factor, species sensitivity distribution and AQUATOX ecosystem modelling

Over the past decades, per- and polyfluoroalkyl substances (PFASs) found in environmental matrices worldwide have raised concerns due to their toxicity, ubiquity and persistence. A widespread pollution of groundwater and surface waters caused by PFASs in Northern Italy has been recently discovered, becoming a major environmental issue, also because the exact risk for humans and nature posed by this contamination is unclear. Here, the Po River in Northern Italy was selected as a study area to assess the ecological risk posed by perfluoroalkyl acids (PFAAs), a class of PFASs, considering the noticeable concentration of various PFAAs detected in the Po waters over the past years. Moreover, the Po has a large environmental and socio-economic importance: it is the largest Italian river and drains a densely inhabited, intensely cultivated and heavily industrialized watershed. Predicted no-effect concentrations (PNECs) were derived using two regulated methodologies, assessment factors (AFs) and species sensitivity distribution (SSD), which rely on published ecotoxicological laboratory tests. Results were compared to those of a novel methodology using the mechanistic ecosystem model AQUATOX to compute PNECs in an ecologically-sound manner, i.e. considering physical, chemical, biological and ecological processes in the river. The model was used to quantify how the biomasses of the modelled taxa in the river food web deviated from natural conditions due to varying inputs of the chemicals. PNEC for each chemical was defined as the lowest chemical concentration causing a non-negligible yearly biomass loss for a simulated taxon with respect to a control simulation. The investigated PFAAs were Perfluorooctanoic acid (PFOA) and Perfluorooctanesulfonic acid (PFOS) as long-chained compounds, and Perfluorobutanoic acid (PFBA) and Perfluorobutanesulfonic acid (PFBS) as short-chained homologues. Two emerging contaminants, Linear Alkylbenzene Sulfonate (LAS) and triclosan, were also studied to assess the performance of the three methodologies for chemicals whose ecotoxicology and environmental fate are well-studied. The most precautionary approach was the use of AFs generally followed by SSD and then AQUATOX, except for PFOS, for which AQUATOX yielded a much lower PNEC compared to the other approaches since, unlike the other two methodologies, it explicitly simulates sublethal toxicity and indirect ecological effects. Our findings highlight that neglecting the role of ecological processes when extrapolating from laboratory tests to ecosystems can result in under-protective threshold concentrations for chemicals. Ecosystem models can complement existing laboratory-based methodologies, and the use of multiple methods for deriving PNECs can help to clarify uncertainty in ecological risk estimates.

A modelling framework to predict bat activity patterns on wind farms: An outline of possible applications on mountain ridges of North Portugal

Worldwide ecological impact assessments of wind farms have gathered relevant information on bat activity patterns. Since conventional bat study methods require intensive field work, the prediction of bat activity might prove useful by anticipating activity patterns and estimating attractiveness concomitant with the wind farm location. A novel framework was developed, based on the stochastic dynamic methodology (StDM) principles, to predict bat activity on mountain ridges with wind farms. We illustrate the framework application using regional data from North Portugal by merging information from several environmental monitoring programmes associated with diverse wind energy facilities that enable integrating the multifactorial influences of meteorological conditions, land cover and geographical variables on bat activity patterns. Output from this innovative methodology can anticipate episodes of exceptional bat activity, which, if correlated with collision probability, can be used to guide wind farm management strategy such as halting wind turbines during hazardous periods. If properly calibrated with regional gradients of environmental variables from mountain ridges with windfarms, the proposed methodology can be used as a complementary tool in environmental impact assessments and ecological monitoring, using predicted bat activity to assist decision making concerning the future location of wind farms and the implementation of effective mitigation measures.

Improving dynamic phytoplankton reserve-utilization models with an indirect proxy for internal nitrogen

Ecologists have often used indirect proxies to represent variables that are difficult or impossible to measure directly. In phytoplankton, the internal concentration of the most limiting nutrient in a cell determines its growth rate. However, directly measuring the concentration of nutrients within cells is inaccurate, expensive, destructive, and time-consuming, substantially impairing our ability to model growth rates in nutrient-limited phytoplankton populations. The red chlorophyll autofluorescence (hereafter "red fluorescence") signal emitted by a cell is highly correlated with nitrogen quota in nitrogen-limited phytoplankton species. The aim of this study was to evaluate the reliability of including flow cytometric red fluorescence as a proxy for internal nitrogen status to model phytoplankton growth rates. To this end, we used the classic Quota model and designed three approaches to calibrate its model parameters to data: where empirical observations on cell internal nitrogen quota were used to fit the model ("Nitrogen-Quota approach"), where quota dynamics were inferred only from changes in medium nutrient depletion and population density ("Virtual-Quota approach"), or where red fluorescence emission of a cell was used as an indirect proxy for its internal nitrogen quota ("Fluorescence-Quota approach"). Two separate analyses were carried out. In the first analysis, stochastic model simulations were parameterized from published empirical relationships and used to generate dynamics of phytoplankton communities reared under nitrogen-limited conditions. Quota models were fitted to the dynamics of each simulated species with the three different approaches and the performance of each model was compared. In the second analysis, we fit Quota models to laboratory time-series and we calculate the ability of each calibration approach to describe the observed trajectories of internal nitrogen quota in the culture. Results from both analyses concluded that the Fluorescence-Quota approach including per-cell red fluorescence as a proxy of internal nitrogen substantially improved the ability of Quota models to describe phytoplankton dynamics, while still accounting for the biologically important process of cell nitrogen storage. More broadly, many population models in ecology implicitly recognize the importance of accounting for storage mechanisms to describe the dynamics of individual organisms. Hence, the approach documented here with phytoplankton dynamics may also be useful for evaluating the potential of indirect proxies in other ecological systems.

Predicting stochastic community dynamics in grasslands under the assumption of competitive symmetry

Community dynamics is influenced by multiple ecological processes such as environmental spatiotemporal variation, competition between individuals and demographic stochasticity. Quantifying the respective influence of these various processes and making predictions on community dynamics require the use of a dynamical framework encompassing these various components. We here demonstrate how to adapt the framework of stochastic community dynamics to the peculiarities of herbaceous communities, by using a short temporal resolution adapted to the time scale of competition between herbaceous plants, and by taking into account the seasonal drops in plant aerial biomass following winter, harvesting or consumption by herbivores. We develop a hybrid inference method for this novel modelling framework that both uses numerical simulations and likelihood computations. Applying this methodology to empirical data from the Jena biodiversity experiment, we find that environmental stochasticity has a larger effect on community dynamics than demographic stochasticity, and that both effects are generally smaller than observation errors at the plot scale. We further evidence that plant intrinsic growth rates and carrying capacities are moderately predictable from plant vegetative height, specific leaf area and leaf dry matter content. We do not find any trade-off between demographical components, since species with larger intrinsic growth rates tend to also have lower demographic and environmental variances. Finally, we find that our model is able to make relatively good predictions of multi-specific community dynamics based on the assumption of competitive symmetry.

Simulation of population response to ionizing radiation in an ecosystem with a limiting resource--Model and analytical solutions

A dynamic mathematical model is formulated, predicting the development of radiation effects in a generic animal population, inhabiting an elemental ecosystem 'population-limiting resource'. Differential equations of the model describe the dynamic responses to radiation damage of the following population characteristics: gross biomass; intrinsic fractions of healthy and reversibly damaged tissues in biomass; intrinsic concentrations of the self-repairing pool and the growth factor; and amount of the limiting resource available in the environment. Analytical formulae are found for the steady states of model variables as non-linear functions of the dose rate of chronic radiation exposure. Analytical solutions make it possible to predict the expected severity of radiation effects in a model ecosystem, including such endpoints as morbidity, mortality, life shortening, biosynthesis, and population biomass. Model parameters are selected from species data on lifespan, physiological growth and mortality rates, and individual radiosensitivity. Thresholds for population extinction can be analytically calculated for different animal species, examples are provided for generic mice and wolf populations. The ecosystem model demonstrates a compensatory effect of the environment on the development of radiation effects in wildlife. The model can be employed to construct a preliminary scale 'radiation exposure-population effects' for different animal species; species can be identified, which are vulnerable at a population level to chronic radiation exposure.

Impacts of management and climate change on nitrate leaching in a forested karst area

Forest management and climate change, directly or indirectly, affect drinking water resources, both in terms of quality and quantity. In this study in the Northern Limestone Alps in Austria we have chosen model calculations (LandscapeDNDC) in order to resolve the complex long-term interactions of management and climate change and their effect on nitrogen dynamics, and the consequences for nitrate leaching from forest soils into the karst groundwater. Our study highlights the dominant role of forest management in controlling nitrate leaching. Both clear-cut and shelterwood-cut disrupt the nitrogen cycle to an extent that causes peak concentrations and high fluxes into the seepage water. While this effect is well known, our modelling approach has revealed additional positive as well as negative impacts of the expected climatic changes on nitrate leaching. First, we show that peak nitrate concentrations during post-cutting periods were elevated under all climate scenarios. The maximal effects of climatic changes on nitrate concentration peaks were 20-24 mg L(-1) in 2090 with shelterwood or clear-cut management. Second, climate change significantly decreased the cumulative nitrate losses over full forest rotation periods (by 10-20%). The stronger the expected temperature increase and precipitation decrease (in summer), the lesser were the observed nitrate losses. However, mean annual seepage water nitrate concentrations and cumulative nitrate leaching were higher under continuous forest cover management than with shelterwood-cut and clear-cut systems. Watershed management can thus be adapted to climate change by either reducing peak concentrations or long-term loads of nitrate in the karst groundwater.

A comparison of observation-level random effect and Beta-Binomial models for modelling overdispersion in Binomial data in ecology & evolution

Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

Definition of radon prone areas in Friuli Venezia Giulia region, Italy, using geostatistical tools

Studying the geographical distribution of indoor radon concentration, using geostatistical interpolation methods, has become common for predicting and estimating the risk to the population. Here we analyse the case of Friuli Venezia Giulia (FVG), the north easternmost region of Italy. Mean value and standard deviation are, respectively, 153 Bq/m(3) and 183 Bq/m(3). The geometric mean value is 100 Bq/m(3). Spatial datasets of indoor radon concentrations are usually affected by clustering and apparent non-stationarity issues, which can eventually yield arguable results. The clustering of the present dataset seems to be non preferential. Therefore the areal estimations are not expected to be affected. Conversely, nothing can be said on the non stationarity issues and its effects. After discussing the correlation of geology with indoor radon concentration It appears they are created by the same geologic features influencing the mean and median values, and can't be eliminated via a map-based approach. To tackle these problems, in this work we deal with multiple definitions of RPA, but only in quaternary areas of FVG, using extensive simulation techniques.

Towards a predictive model to assess the natural position of the Posidonia oceanica seagrass meadows upper limit

The upper portion of the meadows of the protected Mediterranean seagrass Posidonia oceanica occurs in the region of the seafloor mostly affected by surf-related effects. Evaluation of its status is part of monitoring programs, but proper conclusions are difficult to draw due to the lack of definite reference conditions. Comparing the position of the meadow upper limit with the beach morphodynamics (i.e. the distinctive type of beach produced by topography and wave climate) provided evidence that the natural landwards extension of meadows can be predicted. An innovative model was therefore developed in order to locate the region of the seafloor where the meadow upper limit should lie in natural conditions (i.e. those governed only by hydrodynamics, in absence of significant anthropogenic impact). This predictive model was validated in additional sites, which showed perfect agreement between predictions and observations. This makes the model a valuable tool for coastal management.