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The coastal areas of Vanuatu are under a multitude of threats stemming from commercialization, human development, and climate change. Atyphella Olliff is a genus of firefly that includes species endemic to these coastal areas and will need protection. The research that has already been conducted was affected by accessibility due to the remote nature of the islands which left numerous knowledge gaps caused by a lack of distributional data (e.g., Wallacean shortfall). Species distribution models (SDM) are a powerful tool that allow for the modeling of the broader distribution of a taxon, even with limited distributional data available. SDMs assist in filling the knowledge gap by predicting potential areas that could contain the species of interest, making targeted collecting and conservation efforts more feasible when time, resources, and accessibility are major limiting factors. Here a MaxEnt prediction was used to direct field collecting and we now provide an updated predictive distribution for this endemic firefly genus. The original model was validated with additional fieldwork, ultimately expanding the known range with additional locations first identified using MaxEnt. A bias analysis was also conducted, providing insight into the effect that developments such as roads and settlements have on collecting and therefore the SDM, ultimately allowing for a more critical assessment of the overall model. After demonstrating the accuracy of the original model, this new updated SDM can be used to identify specific areas that will need to be the target of future conservation efforts by local government officials.

Animal movement has been identified as a key feature in understanding animal behavior, distribution and habitat use and foraging strategies among others. Large datasets of invididual locations often remain unused or used only in part due to the lack of practical models that can directly infer the desired features from raw GPS locations and the complexity of existing approaches. Some of them being disputed for their lack of biological justifications in their design. We propose a simple model of individual movement with explicit parameters, based on a two-dimensional biased and correlated random walk with three forces related to advection (correlation), attraction (bias) and immobility of the animal. These forces can be directly estimated using individual data. We demonstrate the approach by using GPS data of 5 red deer with a high frequency sampling. The results show that a simple random walk template can account for the spatial complexity of wild animals. The practical design of the model is also verified for detecting spatial feature abnormalities and for providing estimates of density and abundance of wild animals. Integrating even more additional features of animal movement, such as individuals’ interactions or environmental repellents, could help to better understand the spatial behavior of wild animals.

Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier’s beaked whales, sperm whales, and Risso’s dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.

Persistence of wild Pacific oyster, Magallana gigas, also known as Crassostrea gigas, has been increasingly reported across Northern European waters in recent years. While reproduction is inhibited by cold waters, recent warm summer temperature has increased the frequency of spawning events. Although correlation between the increasing abundance of Pacific oyster reefs in Northern European waters and climate change is documented, persistence of wild populations may also be influenced by external recruitment from farmed populations and other wild oyster populations, as well as on competition for resources with aquaculture sites. Our understanding of the combined impact of the spawning frequency, external recruitment, and competition on wild population persistence is limited. This study applied an age-structured model, based on ordinary differential equations, to describe an oyster population under discrete temperature-related dynamics. The impact of more frequent spawning events, external recruitment, and changes in carrying capacity on Pacific oyster density were simulated and compared under theoretical scenarios and two case studies in Southern England. Results indicate that long term persistence of wild oyster populations towards carrying capacity requires a high frequency of spawning events but that in the absence of spawning, external recruitment from farmed populations and other wild oyster populations may act to prevent extinction and increase population density. However, external recruitment sources may be in competition with the wild population so that external recruitment is associated with a reduction in wild population density. The implications of model results are discussed in the context of wild oyster population management.

Ecological data are collected over vast geographic areas using digital sensors such as camera traps and bioacoustic recorders. Camera traps have become the standard method for surveying many terrestrial mammals and birds, but camera trap arrays often generate millions of images that are time-consuming to label. This causes significant latency between data collection and subsequent inference, which impedes conservation at a time of ecological crisis. Machine learning algorithms have been developed to improve the speed of labelling camera trap data, but it is uncertain how the outputs of these models can be used in ecological analyses without secondary validation by a human. Here, we present our approach to developing, testing and applying a machine learning model to camera trap data for the purpose of achieving fully automated ecological analyses. As a case-study, we built a model to classify 26 Central African forest mammal and bird species (or groups). The model generalizes to new spatially and temporally independent data (n = 227 camera stations, n = 23,868 images), and outperforms humans in several respects (e.g. detecting ‘invisible’ animals). We demonstrate how ecologists can evaluate a machine learning model's precision and accuracy in an ecological context by comparing species richness, activity patterns (n = 4 species tested) and occupancy (n = 4 species tested) derived from machine learning labels with the same estimates derived from expert labels. Results show that fully automated species labels can be equivalent to expert labels when calculating species richness, activity patterns (n = 4 species tested) and estimating occupancy (n = 3 of 4 species tested) in a large, completely out-of-sample test dataset. Simple thresholding using the Softmax values (i.e. excluding ‘uncertain’ labels) improved the model's performance when calculating activity patterns and estimating occupancy but did not improve estimates of species richness. We conclude that, with adequate testing and evaluation in an ecological context, a machine learning model can generate labels for direct use in ecological analyses without the need for manual validation. We provide the user-community with a multi-platform, multi-language graphical user interface that can be used to run our model offline.

Eucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for.

Understanding the complex factors and mechanisms driving the functioning of coastal ecosystems is vital towards assessing how organisms, ecosystems, and ultimately human populations will cope with the ecological consequences of natural and anthropogenic impacts. Towards this goal, coastal monitoring programs and studies must deliver information on a range of variables and factors, from taxonomic/functional diversity and spatial distribution of habitats, to anthropogenic stress indicators such as land use, fisheries use, and pollution. Effective monitoring programs must therefore integrate observations from different sources and spatial scales to provide a comprehensive view to managers. Here we explore integrating aerial surveys from a low-cost Remotely Piloted Aircraft System (RPAS) with concurrent underwater surveys to deliver a novel approach to coastal monitoring. We: (i) map depth and substrate of shallow rocky habitats, and; (ii) classify the major biotopes associated with these environmental axes; and (iii) combine data from i and ii to assess the likely distribution of common sessile organismal assemblages over the survey area. Finally, we propose a general workflow that can be adapted to different needs and aerial platforms, which can be used as blueprints for further integration of remote-sensing with in situ surveys to produce spatially-explicit biotope maps.

Bluetongue virus (BTV) serotype 8 has been circulating in Europe since a major outbreak occurred in 2006, causing economic losses to livestock farms. The unpredictability of the biting activity of midges that transmit BTV implies difficulty in computing accurate transmission models. This study uniquely integrates field collections of midges at a range of European latitudes (in Sweden, The Netherlands, and Italy), with a multi-scale modelling approach. We inferred the environmental factors that influence the dynamics of midge catching, and then directly linked predicted midge catches to BTV transmission dynamics. Catch predictions were linked to the observed prevalence amongst sentinel cattle during the 2007 BTV outbreak in The Netherlands using a dynamic transmission model. We were able to directly infer a scaling parameter between daily midge catch predictions and the true biting rate per cow per day. Compared to biting rate per cow per day the scaling parameter was around 50% of 24 h midge catches with traps. Extending the estimated biting rate across Europe, for different seasons and years, indicated that whilst intensity of transmission is expected to vary widely from herd to herd, around 95% of naive herds in western Europe have been at risk of sustained transmission over the last 15 years. Funding Agencies|Ministry of Economic Affairs, The NetherlandsMinistry of Economic Affairs, Netherlands [1300018161]; DEFRADepartment for Environment, Food & Rural Affairs (DEFRA); EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/S022244/1]; BBSRC, United KingdomUK Research & Innovation (UKRI)Biotechnology and Biological Sciences Research Council (BBSRC) [BB/S01750X/1]; Swedish Research Council (FORMAS), Sweden; Italian Ministry of Education, University and Research (MIUR) (Prin 2012)Ministry of Education, Universities and Research (MIUR) [2012T85B3R]

Wild bee decline has been reported worldwide. Some bumblebee species (Bombus spp.) have declined in Europe and North America, and their ranges have shrunk due to climate and land cover changes. In countries with limited historical and current occurrence data, it is often difficult to investigate bumblebee range shifts. Here we estimated the past/present distributions of six major bumblebee species in Japan with species distribution modeling using current occurrence data and past/present climate and land cover data. The differences identified between estimated past and present distributions indicate possible range shifts. The estimated ranges of B. diversus, B. hypocrita, B. ignitus, B. honshuensis, and B. beaticola shrank over the past 26 years, but that of B. ardens expanded. The lower altitudinal limits of the estimated ranges became higher as temperature increased. When focusing on the effects of land cover change, the estimated range of B. diversus slightly shrank due to an increase in forest area. Such increase in forest area may result from the abandonment of agricultural lands and the extension of the rotation time of planted coniferous forests and secondary forests. Managing old planted coniferous forests and secondary forests will be key to bumblebee conservation for adaptation to climate change.

Tigers and leopards have experienced considerable declines in their population due to habitat loss and fragmentation across their historical ranges. Multi-scale habitat suitability models (HSM) can inform forest managers to aim their conservation efforts at increasing the suitable habitat for tigers by providing information regarding the scale-dependent habitat-species relationships. However the current gap of knowledge about ecological relationships driving species distribution reduces the applicability of traditional and classical statistical approaches such as generalized linear models (GLMs), or occupancy surveys to produce accurate predictive maps. This study investigates the multi-scale habitat relationships of tigers and leopards and the impacts of future climate change on their distribution using a machine-learning algorithm random forest (RF). The recent advancements in the machine-learning algorithms provide a powerful tool for building accurate predictive models of species distribution and their habitat relationships even when little ecological knowledge is available about the species. We collected species occurrence data using camera traps and indirect evidence of animal presences (scats) in the field over 2 years of rigorous sampling and used a machine-learning algorithm random forest (RF) to predict the habitat suitability maps of tiger and leopard under current and future climatic scenarios. We developed niche overlap models based on the recently developed statistical approaches to assess the patterns of niche similarity between tigers and leopards. Tiger and leopard utilized habitat resources at the broadest spatial scales (28,000 m). Our model predicted a 23% loss in the suitable habitat of tigers under the RCP 8.5 Scenario (2050). Our study of multi-scale habitat suitability modeling provides valuable information on the species habitat relationships in disturbed and human-dominated landscapes concerning two large felid species of conservation importance. These areas may act as refugee habitats for large carnivores in the future and thus should be the focus of conservation importance. This study may also provide a methodological framework for similar multi-scale and multi-species monitoring programs using robust and more accurate machine learning algorithms such as random forest.

Tracking concentrations of regional airborne pollen is valuable for a variety of fields including plant and animal ecology as well as human health. However, current methods for directly measuring regional pollen concentrations are labor-intensive, requiring special equipment and manual counting by professionals leading to sparse data availability in select locations. Here, we use publicly available Google Trends data to evaluate whether searches for the term “pollen” can be used to approximate local observed early-season pollen concentrations as reported by the National Allergy Bureau across 25 U.S. regions from 2012–2017, in the context of site-specific characteristics. Our findings reveal that two major factors impact the ability of internet search data to approximate observed pollen: (1) volume/availability of internet search data, which is tied to local population size and media use; and (2) signal intensity of the seasonal peak in searches. Notably, in regions and years where internet search data was abundant, we found strong correlations between local search patterns and observed pollen, thus revealing a potential source of daily pollen data across the U.S. where observational pollen data are not reliably available.

This study aims to identify environmentally suitable areas for 15 of the most harmful invasive alien terrestrial vertebrates (IATV) in Europe in a transparent and replicable way. We used species distribution models and publicly-available data from GBIF to predict environmental suitability and to identify hotspots of IATV accounting for knowledge gaps in their distributions. To deal with the ecological particularities of invasive species, we followed a hierarchical approach to estimate the global climatic suitability for each species and incorporated this information into refined environmental suitability models within Europe. Combined predictions on environmental suitability identified potential areas of IATV concentrations or hotspots. Uncertainty of predictions identified regions requiring further survey efforts for species detection. Around 14% of Europe comprised potential hotspots of IATV richness, mainly located in northern France, UK, Belgium and the Netherlands. IATV coldspots covered ~ 9% of Europe, including southern Sweden and Finland, and northern Germany. Most of Europe (~ 77% area) comprised uncertain suitability predictions, likely caused by a lack of data. Priorities on prevention and control should focus on potential hotspots where harmful impacts might concentrate. Promoting the collection of presence data within data-deficient areas is encouraged as a core strategy against IATVs.

Biotic interactions play an important role in species distribution models, whose ignorance may cause an overestimation of species' potential distributions. Species of the family Orchidaceae are almost totally dependent on mycorrhizal symbionts and pollinators, with sexually deceptive orchids being often highly specialized, and thus the interactions with their pollinators are expected to strongly affect distribution predictions. We used Maxent algorithm to explore the extent of current and future habitat suitability for two Greek endemic sexually deceptive orchids (Ophrys argolica and Ophrys delphinensis) in relation to the potential distribution of their unique pollinator (Anthophora plagiata). Twelve climate change scenarios were used to predict future distributions. Results indicated that the most important factors determining potential distribution were precipitation seasonality for O. argolica and geological substrate for O. delphinensis. The current potential distribution of the two orchids was almost of the same extent but spatially different, without accounting for their interaction with A. plagiata. When the interaction was included in the models, their potentially suitable area decreased for both species. Under future climatic conditions, the effects of the orchid-pollinator interaction were more intense. Specifically, O. argolica was restricted in specific areas of southern Greece, whereas O. delphinensis was expected to become extinct. Our findings highlighted the significant role of plant–pollinator interactions in species distribution models. Failing to study such interactions might expose plant species to serious conservation issues.

Global changes pose both risks and opportunities to agriculture and forestry, and biological forecasts can inform future management strategies. Here, we investigate potential land-use opportunities arising from climate change for these sectors in Europe, and risks associated with the introduction and establishment of novel insect pests. Adopting a metaweb approach including all interaction links between 126 crops and forest tree species and 89 black-listed insect pest species, we show that the metawebs shift toward increased numbers of links and overlap of suitable area under climate change. Decomposing the metaweb across regions shows large saturation in southern Europe, while many novel interactions are expected for northern Europe. In light of the rising consumer awareness about human health and environmental impacts of food and wood production, the challenge will be to effectively exploit new opportunities to create diverse local agriculture and forestry while controlling pest species and reducing risks from pesticide use., Communications Biology, 3 (1), ISSN:2399-3642

The interaction of pathogens between wild and farmed aquatic animal populations is a concern that remains unclear and controversial. Ichthyophthirius multifiliis, a ciliated protozoan parasite, is a pathogen of freshwater finfish species with geographic and host range that causes significant economic losses in aquaculture. Flow-through farming systems may facilitate the transfer of such a parasite with free-living stages between farmed and wild stocks. Here, experimental and field study infection data are used to describe the infection dynamics of Ichthyophthirius multifiliis in rainbow trout using a simple macroparasite model by including host resistance. The study considered flow-through farming systems with a single or two age-class compartments and simulated the transfer of the parasite between farmed and wild fish populations. Results suggest that aquaculture can promote the prevalence of the resistance in wild stocks by increasing the parasite population in the wild environment. At the same time, acquired resistance in the farmed fish population may protect the wild fish population from lethal effects of the parasite by reducing the total parasite population. This study offers a promising mathematical basis for understanding the effects of freshwater aquaculture in disease spread in wildlife, developing risk assessment modeling, and exploring new ways of aquaculture management.

Several methodologies related to the biomechanical risk assessment and the uprooting and breaking potential of palms are reviewed and evaluated in this study. Also a simple mathematical model was designed, to simulate the results of critical wind speed predictions for a tall coconut palm by using classic beam theory and Brazier buckling. First, the review presents arguments that assess the applicability of some influential claims and tree and palm risk assessment methods that have been amply marketed in the last 20 years. Then, the analysis goes beyond the classical procedures and theories that have influenced the arboricultural industry and related press so far. And afterwards, rationale behind several postulated ideas are presented, that are hoped to be fruitful in the path towards a new biomechanical theory for the biomechanical risk assessment of palms. The postulated model envisages the palm stem as a viscoelastic and hollow cylinder that is not only prone to buckling, ovalization and kinking, but also fatigue, shear, splitting and crack propagation. This envisaging was also the main reason why simple Brazier buckling formulation was experimentally applied to simulate the breaking risk of a cocostem. This study also enables a better understanding of the wide range of factors that may influence the mechanical behaviour of trees and palms under (wind) loading.

Modular food web theory shows how weak energetic fluxes resulting from consumptive interactions plays a major role in stabilizing food webs in space and time. Despite the reliance on energetic fluxes, food web theory surprisingly remains poorly understood within an ecosystem context that naturally focuses on material fluxes. At the same time, while ecosystem theory has employed modular nutrient-limited ecosystem models to understand how limiting nutrients alter the structure and dynamics of food webs, ecosystem theory has overlooked the role of key ecosystem interactions and their strengths (e.g., plant-nutrient; R-N) in mediating the stability of nutrient-limited ecosystems. Here, towards integrating food web theory and ecosystem theory, we first briefly review consumer-resource interactions (C-R) highlighting the relationship between the structure of C-R interactions and the stability of food web modules. We then translate this framework to nutrient-based systems, showing that the nutrient-plant interaction behaves as a coherent extension of current modular food web theory; however, in contrast to the rule that weak C-R interactions tend to be stabilizing we show that strong nutrient-plant interactions are potent stabilizers in nutrient-limited ecosystem models., Schonberger et al. combine nutrient-plant interactions and consumer-resource interactions into simulation models, and explore how the strength of these interactions affects ecosystem stability across several types of trophic modules. Their study concludes that strong nutrient-plant interactions increase ecosystem stability, contrasting the notion that weak consumer-resource interactions tend to be stabilizing.

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.

Land use regression (LUR) is a widely used method to develop prediction models in environmental sciences. However, the process of creating and applying LUR models is repetitive and time-consuming. The XLUR tool was developed to automate this process, while at the same time providing a detailed log of the model building process for reproducibility, and providing evaluation metrics to assess model quality. The aim of this research is to provide a technical demonstration of the use of XLUR in two scenarios. We demonstrate the use of the XLUR tool to build models for predicting PM10 concentrations in Greater Manchester and intestinal enterococci along the Northwest coast of England. The examples show how the tool facilitates (a) model building using standard published protocols and (b) assessment of prediction quality. As is common with LUR approaches, prediction quality is reliant on data and the characteristics of the phenomena being modelled.

Human-driven threats are changing biodiversity, impacting ecosystem services. The loss of one species can trigger secondary extinctions of additional species, because species interact-yet the consequences of these secondary extinctions for services remain underexplored. Herein, we compare robustness of food webs and the ecosystem services (hereafter 'services') they provide; and investigate factors determining service responses to secondary extinctions. Simulating twelve extinction scenarios for estuarine food webs with seven services, we find that food web and service robustness are highly correlated, but that robustness varies across services depending on their trophic level and redundancy. Further, we find that species providing services do not play a critical role in stabilizing food webs - whereas species playing supporting roles in services through interactions are critical to the robustness of both food webs and services. Together, our results reveal indirect risks to services through secondary species losses and predictable differences in vulnerability across services.

Climate change impacts on the structure and function of ecosystems will worsen public health issues like allergic diseases. Birch trees (Betula spp.) are important sources of aeroallergens in Central and Northern Europe. Birches are vulnerable to climate change as these trees are sensitive to increased temperatures and summer droughts. This study aims to examine the effect of climate change on airborne birch pollen concentrations in Central Europe using Bavaria in Southern Germany as a case study. Pollen data from 28 monitoring stations in Bavaria were used in this study, with time series of up 30 years long. An integrative approach was used to model airborne birch pollen concentrations taking into account drivers influencing birch tree abundance and birch pollen production and projections made according to different climate change and socio-economic scenarios. Birch tree abundance is projected to decrease in parts of Bavaria at different rates, depending on the climate scenario, particularly in current centres of the species distribution. Climate change is expected to result in initial increases in pollen load but, due to the reduction in birch trees, the amount of airborne birch pollen will decrease at lower altitudes. Conversely, higher altitude areas will experience expansions in birch tree distribution and subsequent increases in airborne birch pollen in the future. Even considering restrictions for migration rates, increases in pollen load are likely in Southwestern areas, where positive trends have already been detected during the last three decades. Integrating models for the distribution and abundance of pollen sources and the drivers that control birch pollen production allowed us to model airborne birch pollen concentrations in the future. The magnitude of changes depends on location and climate change scenario.\ud \ud Supporting Information

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

Spike detection for raw high-frequency eddy covariance time series is a challenging task because of the confounding effect caused by complex dynamics and the high level of noise affecting such data. To cope with these features, a new despiking procedure rooted on robust functionals is proposed. By processing simulated data, it is demonstrated that the proposed procedure performs better than the existing algorithms and can be therefore considered as a candidate for the implementation in data center environmental monitoring systems, where the availability of automatic procedures ensuring a high quality standard of released products constitutes an essential prerequisite.

Southern Iran is a conservation priority area for the endangered Persian onager (Equus hemionus onager), which is threatened by habitat fragmentation and conflict with local communities. To better understand factors that influence onager conservation, we administered a questionnaire in local communities to survey their ecological knowledge, personal experience related to onager, and attitudes toward traditional solutions for reducing crop damage by onager. In addition, we used resistant kernel and factorial least-cost path analyses to identify core areas and corridors for onager movement, and spatial randomization of vehicle collisions and crossing locations to test the predictive ability of resistant kernel and factorial least-cost path predictions of movement. We found that local communities that were knowledgeable about onagers experienced less crop damage from onager compared with those who used traditional methods. Habitat connectivity models revealed that core areas of movement are highly concentrated at the center of protected areas. Some sections of core areas have been cut off by roads where most vehicle collisions with onagers occurred. We propose that effective onager conservation will require integrated landscape-level management to reduce mortality risk, protection of core areas and corridors, development of mitigation strategies to reduce vehicle collisions, and conflict mediation between local communities and onagers.

Estimating the potential distribution of invasive species has been primarily achieved by employing species distribution models (SDM). Recently introduced joint species distribution models (JSDM) that include species interactions are expected to improve model output. Here we compare the predictive ability of SDM and JSDM by modelling the distribution of one of the most prolific avian invaders in the world, the common myna (Acridotheres tristis), in a recent introduction in Israel. Our results indicate that including information on the local species composition did not improve model accuracy, possibly because of the unique characteristics of this species that include broad environmental tolerance and behavior flexibility. However, the JSDM provided insights into co-occurrence patterns of common mynas and their local heterospecifics, suggesting that at this time point, there is no evidence of species exclusion by common mynas. Our findings suggest that the invasion potential of common mynas depends greatly on urbanization and less so on the local species composition and reflect the major role of anthropogenic impact in increasing the distribution of avian invaders.

Individual-based models, ‘IBMs’, describe naturally the dynamics of interacting organisms or social or financial agents. They are considered too complex for mathematical analysis, but computer simulations of them cannot give the general insights required. Here, we resolve this problem with a general mathematical framework for IBMs containing interactions of an unlimited level of complexity, and derive equations that reliably approximate the effects of space and stochasticity. We provide software, specified in an accessible and intuitive graphical way, so any researcher can obtain analytical and simulation results for any particular IBM without algebraic manipulation. We illustrate the framework with examples from movement ecology, conservation biology, and evolutionary ecology. This framework will provide unprecedented insights into a hitherto intractable panoply of complex models across many scientific fields., Individual-based models are widely used to simulate complex systems of interacting agents. Here the authors provide a mathematical framework that automates the analysis of any model in a wide class, facilitating a deeper understanding of the scientific questions these models are used to address.

Greywater recycling and rainwater harvesting have the potential to increase the resilience of water management and reduce the need for investment in conventional water supply schemes. However, their water-savings would partly depend on the location and built-form of urban development and hence its household sizes and rainwater per dwelling. We have therefore tested how spatial planning options would affect the future viability of alternative water supply in the Greater South East of England. Our integrated modelling framework, for the first time, forecasts the future densities and variability of built-form to provide inputs to the modelling of alternative water supply. We show that using projections of the existing housing stock would have been unsound, and that using standard dwelling types and household sizes would have substantially overestimated the water-savings, by not fully representing how the variability in dwelling dimensions and household-sizes would affect the cost effectiveness of these systems. We compare the spatial planning trend over a 30 year period with either compaction at higher densities within existing urban boundaries, or market-led more dispersed development. We show how the viability of alternative water supply would differ between these three spatial planning options. The water-savings of rainwater harvesting would vary greatly at a regional scale depending on residential densities and rainfall. Greywater recycling would be less affected by spatial planning but would have a finer balance between system costs and water-savings and its feasibility would vary locally depending on household sizes and water efficiency. The sensitivity of the water savings to differences in rainfall and water prices would vary with residential density. The findings suggest that forecasts of residential densities, rainfall and the water price could be used in conjunction with more detailed local studies to indicate how spatial planning would affect the future water saving potential of alternative water supply.

With inadequate resources to manage the threats facing biodiversity worldwide, achieving projected management outcomes is critical for efficient resource allocation and species recovery. Despite this, conservation plans to mitigate threats rarely articulate the likelihood of management success. Here we develop a general value of information approach to quantify the impact of uncertainty on 20 threatening processes affecting 976 listed species and communities. To our knowledge, this is the most comprehensive quantification of the impacts of uncertainty on threat management. We discover that, on average, removing uncertainty about management effectiveness could triple the gain in persistence achieved by managing under current uncertainty. Management of fire, invasive animals and a plant pathogen are most impeded by uncertainty; management of invasive plants is least impacted. Our results emphasise the tremendous importance of reducing uncertainty about species responses to management, and show that failure to consider management effectiveness wastes resources and impedes species recovery., Conservation planning rarely considers the uncertainty in management outcomes. Here, the authors develop a value of information approach to quantify uncertainty of threat management success and show that it can improve efficiency of interventions across a large sample of threatened species.

We present a teaching methodology to answer the question ‘How can we link teaching with research in our engineering courses?’ Due to the combination of hybrid Problem Based Learning with Research Tutored/Oriented Teaching, the methodology enables the students to acquire the skills of ‘problem solving’, ‘working in teams’, ‘analytic’, ‘written communication’, ‘planning my own work’, ‘organization’, ‘learning more deeply’, ‘tackling with unfamiliar problems’ and ‘innovation’; the last two are directly related to the Scientific Research Process and are considered important for modern engineers. We apply the methodology in the course ‘Ecological Modelling of Surface Waters’ that we teach in TUM and NTUA following the teaching sequence ‘Scope of modelling – Background knowledge – Simple model – Tutorial – Real case’. Due to its simplicity, the proposed methodology can be easily applied by University researchers, who are usually not familiar with the theoretical aspects of learning.

Biodiversity informatics has advanced rapidly with the maturation of major biodiversity data infrastructures (BDDIs), such as the Global Biodiversity Information Facility sharing unprecedented data volumes. Nevertheless, taxonomic, temporal and spatial data coverage remains unsatisfactory. With an increasing data need, the global BDDIs require continuous inflow from local data mobilisation, and national BDDIs are being developed around the world. The global BDDIs are specialised in certain data types or data life cycle stages which, despite possible merits, renders the BDDI landscape fragmented and complex. That this often is repeated at the national level creates counterproductive redundancy, complicates user services, and frustrates funders. Here, we present the Finnish Biodiversity Information Facility (FinBIF) as a model of an all-inclusive BDDI. It integrates relevant data types and phases of the data life cycle, manages them under one IT architecture, and distributes the data through one service portal under one brand. FinBIF has experienced diverse funder engagement and rapid user uptake. Therefore, we suggest the integrated and inclusive approach be adopted in national BDDI development.

Environmental and ecosystem models can help to guide management of changing natural systems by projecting alternative future states under a common set of scenarios. Combining contrasting models into multi-model ensembles (MMEs) can improve the skill and reliability of projections, but associated uncertainty complicates communication of outputs, affecting both the effectiveness of management decisions and, sometimes, public trust in scientific evidence itself. Effective data visualisation can play a key role in accurately communicating such complex outcomes, but we lack an evidence base to enable us to design them to be visually appealing whilst also effectively communicating accurate information. To address this, we conducted a survey to identify the most effective methods for visually communicating the outputs of an ensemble of global climate models. We measured the accuracy, confidence, and ease with which the survey participants were able to interpret 10 visualisations depicting the same set of model outputs in different ways, as well as their preferences. Dot and box plots outperformed all other visualisations, heat maps and radar plots were comparatively ineffective, while our infographic scored highly for visual appeal but lacked information necessary for accurate interpretation. We provide a set of guidelines for visually communicating the outputs of MMEs across a wide range of research areas, aimed at maximising the impact of the visualisations, whilst minimizing the potential for misinterpretations, increasing the societal impact of the models and ensuring they are well-placed to support management in the future.

Water users can reduce their impact on scarce freshwater resources by using more abundant regional brackish or saline groundwater resources. Decentralized water supply networks (WSN) can connect these regional groundwater resources with water users. Here, we present WaterROUTE (Water Route Optimization Utility Tool & Evaluation), a model which optimizes water supply network configurations based on infrastructure investment costs while considering the water quality (salinity) requirements of the user. We present an example simulation in which we determine the optimal WSN for different values of the maximum allowed salinity at the demand location while supplying 2.5 million m3 year-1 with regional groundwater. The example simulation is based on data from Zeeuws-Vlaanderen, the Netherlands. The optimal WSN configurations for the years 2030, 2045 and 2110 are generated based on the simulated salinity of the regional groundwater resources. The simulation results show that small changes in the maximum salinity at the demand location have significant effects on the WSN configuration and therefore on regional planning. For the example simulation, the WSN costs can differ by up to 68% based on the required salinity at the demand site. WaterROUTE can be used to design water supply networks which incorporate alternative water supply sources such as local brackish groundwater (this study), effluent, or rainwater.

Human-driven threats are changing biodiversity, impacting ecosystem services. The loss of one species can trigger secondary extinctions of additional species, because species interact–yet the consequences of these secondary extinctions for services remain underexplored. Herein, we compare robustness of food webs and the ecosystem services (hereafter ‘services’) they provide; and investigate factors determining service responses to secondary extinctions. Simulating twelve extinction scenarios for estuarine food webs with seven services, we find that food web and service robustness are highly correlated, but that robustness varies across services depending on their trophic level and redundancy. Further, we find that species providing services do not play a critical role in stabilizing food webs – whereas species playing supporting roles in services through interactions are critical to the robustness of both food webs and services. Together, our results reveal indirect risks to services through secondary species losses and predictable differences in vulnerability across services., Food web responses to species losses have the potential to cascade to ecosystem services. Here the authors apply ecological network robustness modelling to ecosystem services in salt marsh ecosystems, finding that species with supporting roles are critical to robustness of both food webs and ecosystem services.

Animal movement and resource use are tightly linked. Investigating these links to understand how animals use space and select habitats is especially relevant in areas affected by habitat fragmentation and agricultural conversion. We set out to explore the space use and habitat selection of Burmese pythons (Python bivittatus) in a heterogenous, agricultural landscape within the Sakaerat Biosphere Reserve, northeast Thailand. We used VHF telemetry to record the daily locations of seven Burmese pythons and created dynamic Brownian Bridge Movement Models to produce occurrence distributions and model movement extent and temporal patterns. To explore relationships between movement and habitat selection we used integrated step selection functions at both the individual and population level. Burmese pythons had a mean 99% occurrence distribution contour of 98.97 ha (range 9.05–285.56 ha). Furthermore, our results indicated that Burmese pythons had low mean individual motion variance, indicating infrequent moves and long periods at a single location. In general, Burmese pythons restricted movement and selected aquatic habitats but did not avoid potentially dangerous land use types like human settlements. Although our sample is small, we suggest that Burmese pythons are capitalizing on human disturbed landscapes.

Highlights • Analysis of sewer pipes promoted faster public health actions to control epidemic. • An environmental health indicator was made available to the population. • Environmental data was used in decision making to control the epidemic • SARS-CoV-2 lineages were demonstrated through whole-genome sequencing. • WBE is an excellent surveillance tool to assess the course of the COVID-19 epidemic., Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) wastewater-based epidemiology (WBE) has been applied as a complementary approach for spatial tracking of coronavirus disease 2019 cases (COVID-19) as well as early warning of the occurrence of infected populations. The present study presents the result of the monitoring of sanitary sewerage in the municipality of Niterói, a metropolitan region of Rio de Janeiro (Brazil) and its use as a complementary indicator in the surveillance of COVID-19 cases, thus assisting actions of public health from local authorities. Twelve composite raw sewage samples were weekly collected from two wastewater treatment plants (WWTPs) and alternately from 17 sewer pipes (SP) from surrounding neighbourhoods and slums throughout 20 weeks (April 15th to August 25th, 2020). Two hundred twenty-three samples were concentrated using the ultracentrifugation-based method and SARS-CoV-2 RNA detected and quantified by RT-qPCR using primers and probe targeting the N2 genome. SARS-CoV-2 RNA was detected in 84.3% (188/223) of samples with a positive rate ranging from 42% (5/12) in the first week of monitoring to 100% during the peak of epidemic with viral concentration ranging from 3.1 to 7.1 log10 genome copies /100 mL throughout the studied period. Positive rates were higher in WWTPs when compared to SP, being useful tool for monitoring trends in the evolution of the COVID-19 curve, while SP data were more effective when health public interventions were needed. Whole-genome sequencing using Illumina MiSeq System confirmed the lineage of three genomes as B.1.1.33 (clade G) containing the nucleotide substitutions observed in strains that circulate in the Rio de Janeiro during the period of this study. In addition, geoprocessing tool was used to build heat maps based on SARS-CoV-2 data from sewage samples, which were weekly updated and available online to the general population as an indicator of the ongoing epidemic situation in Niterói city, raising public awareness., Graphical abstract Image, graphical abstract

Highlights • Effective WASH is necessary in the control of SARS-CoV-2 infection. • WASH situation in low income countries is still inadequate for effective control of SARS-CoV-2 infection. • Proper awareness on effective WASH is required under the current SARS-CoV-2 outbreak. • Detection of viral particles in faces, urine and wastewater could serve as early warning on SARS-CoV-2 infection trend., Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the deadly respiratory disease called coronavirus disease of 2019 (COVID-19), an ongoing global public health emergency that has been declared a pandemic by the World Health Organization. We review literature on the transmission and control of SARS-CoV-2 and discuss the challenges of focusing on water, sanitation and hygiene (WASH) as critical control measures in low-income countries. A significantly higher prevalence of SARS-CoV-2 infection and COVID-19 related deaths has been reported for the United States of America and other high-income countries in Europe and Asia, regardless of advanced medical facilities in those countries. In contrast, much lower COVID-19 related morbidity and mortality rates have been documented in many low-income countries, despite having comparatively higher socioeconomic burdens and suboptimal medical facilities. By September 29, 2020 over one million deaths have been reported. On the same day, the cumulative total of COVID-19 related morbidity for Africa was 35,954 with 3.5% of the global COVID-19 related deaths. We present arguments for the relatively low COVID-19 morbidity and mortality rates in many low-income countries and discuss the critical importance of WASH for preventing the spread of infectious diseases like COVID-19. We observe that the key recommendations put forward by the World Health Organization to effectively control the pandemic have been difficult to implement in low-income countries. We conclude that the pandemic reinforces previous pronouncements that adequate and effective WASH measures are crucial for public health and recommend closer coordination between public health and WASH sectors., Graphical abstract Image, graphical abstract

To determine the risk state distribution, risk level, and risk evolution situation of agricultural non-point source pollution (AGNPS), we built an ‘Input-Translate-Output’ three-dimensional evaluation (ITO3dE) model that involved 12 factors under the support of GIS and analyzed the spatiotemporal evolution characteristics of AGNPS risks from 2005 to 2015 in Chongqing by using GIS space matrix, kernel density analysis, and Getis-Ord Gi* analysis. Land use changes during the 10 years had a certain influence on the AGNPS risk. The risk values in 2005, 2010, and 2015 were in the ranges of 0.40–2.28, 0.41–2.57, and 0.41–2.28, respectively, with the main distribution regions being the western regions of Chongqing (Dazu, Jiangjin, etc.) and other counties such as Dianjiang, Liangping, Kaizhou, Wanzhou, and Zhongxian. The spatiotemporal transition matrix could well exhibit the risk transition situation, and the risks generally showed no changes over time. The proportions of ‘no-risk no-change’, ‘low-risk no-change’, and ‘medium-risk no-change’ were 10.86%, 33.42%, and 17.25%, respectively, accounting for 61.53% of the coverage area of Chongqing. The proportions of risk increase, risk decline, and risk fluctuation were 13.45%, 17.66%, and 7.36%, respectively. Kernel density analysis was suitable to explore high-risk gathering areas. The peak values of kernel density in the three periods were around 1110, suggesting that the maximum gathering degree of medium-risk pattern spots basically showed no changes, but the spatial positions of high-risk gathering areas somehow changed. Getis-Ord Gi* analysis was suitable to explore the relationships between hot and cold spots. Counties with high pollution risks were Yongchuan, Shapingba, Dianjiang, Liangping, northwestern Fengdu, and Zhongxian, while counties with low risks were Chengkou, Wuxi, Wushan, Pengshui, and Rongchang. High-value hot spot zones gradually dominated in the northeast of Chongqing, while low-value cold spot zones gradually dominated in the Midwest. Our results provide a scientific base for the development of strategies to prevent and control AGNPS in Chongqing.

Dengue is one of the most important vector-borne infection in Sri Lanka currently leading to vast economic and social burden. Neither a vaccine nor drug is still not being practiced, vector controlling is the best approach to control disease transmission in the country. Therefore, early warning systems are imminent requirement. The aim of the study was to develop Geographic Information System (GIS)-based multivariate analysis model to detect risk hotspots of dengue in the Gampaha District, Sri Lanka to control diseases transmission. A risk model and spatial Poisson point process model were developed using separate layers for patient incidence locations, positive breeding containers, roads, total buildings, public places, land use maps and elevation in four high risk areas in the district. Spatial correlations of each study layer with patient incidences was identified using Kernel density and Euclidean distance functions with minimum allowed distance parameter. Output files of risk model indicate that high risk localities are in close proximity to roads and coincide with vegetation coverage while the Poisson model highlighted the proximity of high intensity localities to public places and possibility of artificial reservoirs of dengue. The latter model further indicate that clustering of dengue cases in a radius of approximately 150 m in high risk areas indicating areas need intensive attention in future vector surveillances.

Background Data on the national-level impact of pneumococcal conjugate vaccine (PCV) introduction on mortality are lacking from Africa. PCV was introduced in South Africa in 2009. We estimated the impact of PCV introduction on all-cause pneumonia mortality in South Africa, while controlling for changes in mortality due to other interventions. Methods and findings We used national death registration data in South Africa from 1999 to 2016 to assess the impact of PCV introduction on all-cause pneumonia mortality in all ages, with the exclusion of infants aged, Jackie Kleynhans and colleagues investigate whether introduction of the pneumococcal conjugate vaccine may have reduced all-cause pneumonia mortality in South Africa., Author summary Why was this study done? The bacterium, Streptococcus pneumoniae, causes a third of childhood pneumonia deaths in sub-Saharan Africa. The pneumococcal conjugate vaccine (PCV) has been used in South Africa since 2009 and has been found to be effective in reducing vaccine-serotype invasive pneumococcal disease. The effect of PCV on pneumonia mortality has not been assessed in South Africa, or on national scale in Africa. We wanted to estimate the impact of this vaccine on all-cause pneumonia mortality in South Africa. What did the researches do and find? We performed an ecological modelling study using national death registration data in South Africa from 1999 to 2016 to assess the impact of PCV introduction on all-cause pneumonia mortality. Due to multiple other changes in South Africa at the time, pneumonia mortality was already decreasing before PCV was introduced. Therefore, we needed to control for these changes while measuring the impact of PCV on pneumonia mortality. We did this by creating a synthetic control from multiple other causes of deaths and compared the number of pneumonia deaths after PCV introduction to this synthetic control. We estimated that pneumonia mortality reduced between 23% and 33% in children younger than 19 years, with an estimated 18,000 deaths prevented between 2009 and 2016. What do these findings mean? PCV has contributed in reducing childhood pneumonia mortality in South Africa. Vaccination of children with PCV should continue globally and should be expanded to all African countries.

Climate change has been widely accepted as one of the major threats for global biodiversity and understanding its potential effects on species distribution is crucial to optimise conservation planning in future scenarios under global change. Integrating detailed climatic data across spatial and temporal scales into species distribution modelling can help to predict potential changes in biodiversity. Consequently, this type of data can be useful for developing efficient biodiversity management and conservation planning. The provision of such data becomes even more important in highly biodiverse regions, currently suffering from climatic and landscape changes. The Transboundary Biosphere Reserve of Meseta Ibérica (BRMI; Portugal-Spain) is one of the most relevant reserves for wildlife in Europe. This highly diverse region is of great ecological and socio-economical interest, suffering from synergistic processes of rural land abandonment and climatic instabilities that currently threaten local biodiversity. Aiming to optimise conservation planning in the Reserve, we provide a complete dataset of historical and future climate models (1 x 1 km) for the BRMI, used to build a series of distribution models for 207 vertebrate species. These models are projected for 2050 under two climate change scenarios. The climatic suitability of 52% and 57% of the species are predicted to decrease under the intermediate and extreme climatic scenarios, respectively. These models constitute framework data for improving local conservation planning in the Reserve, which should be further supported by implementing climate and land-use change factors to increase the accuracy of future predictions of species distributions in the study area. Herein, we provide a complete dataset of state-of-the-art historical and future climate model simulations, generated by global-regional climate model chains, with climatic variables resolved at a high spatial resolution (1 × 1 km) over the Transboundary Biosphere Reserve of Meseta Ibérica. Additionally, a complete series of distribution models for 207 species (168 birds, 24 reptiles and 15 amphibians) under future (2050) climate change scenarios is delivered, which constitute framework data for improving local conservation planning in the reserve.

In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.

In 2019, it was estimated that more than 50 million captive Atlantic salmon in Norway died in the final stage of their production in marine cages. This mortality represents a significant economic loss for producers and a need to improve welfare for farmed salmon. Single adverse events, such as algal blooms or infectious disease outbreaks, can explain mass mortality in salmon cages. However, little is known about the production, health, or environmental factors that contribute to their baseline mortality during the sea phase. Here we conducted a retrospective study including 1627 Atlantic salmon cohorts put to sea in 2014–2019. We found that sea lice treatments were associated with Atlantic salmon mortality. In particular, the trend towards non-medicinal sea lice treatments, including thermal delousing, increases Atlantic salmon mortality in the same month the treatment is applied. There were differences in mortality among production zones. Stocking month and weight were other important factors, with the lowest mortality in smaller salmon stocked in August–October. Sea surface temperature and salinity also influenced Atlantic salmon mortality. Knowledge of what affects baseline mortality in Norwegian aquaculture can be used as part of syndromic surveillance and to inform salmon producers on farming practices that can reduce mortality.

Many global environmental agendas, including halting biodiversity loss, reversing land degradation, and limiting climate change, depend upon retaining forests with high ecological integrity, yet the scale and degree of forest modification remain poorly quantified and mapped. By integrating data on observed and inferred human pressures and an index of lost connectivity, we generate a globally consistent, continuous index of forest condition as determined by the degree of anthropogenic modification. Globally, only 17.4 million km

The rise of small businesses, self-employment, and homeworking are transforming traditional industrial ways of working Our research fills a noticeable gap in the literature by using portable devices (i e , smartphones) to capture individual mobility data on an understudied population group – small business owners (owner managers and self-employed with up to 49 employees) and whether they work from home in comparison with employees who work at their employer's premises or partly or mainly from home We recorded week-long individual GPS data on 702 participants and derived a set of measures of daily mobility (number of trips, trip duration, trip distance, and maximum distance from home) Each measure is modelled against a range of individual and neighbourhood-level covariates Our findings contrast with existing studies that suggest homeworking or self-employment may be associated with lower levels of daily mobility or with compensatory effects between work and non-work travel Overall, our study points to higher levels of daily mobility of owners of small businesses and the self-employed in cities as they travel longer distances Further, some homeworkers have on aggregate longer daily trip distances than ‘traditional’ premise-based employees Most striking, female home-based business owners fall into this group If homeworking is here to stay after the COVID-19 pandemic, we may see both increases and/or decreases of daily mobility depending on worker types and gender © 2020 Elsevier Ltd

Iran lies at the southernmost range limit of brown bears globally. Therefore, understanding the habitat associations and patterns of population connectivity for brown bears in Iran is relevant for the species’ conservation. We applied species distribution modeling to predict habitat suitability and connectivity modeling to identify population core areas and corridors. Our results showed that forest density, topographical roughness, NDVI and human footprint were the most influential variables in predicting brown bear distribution. The most crucial core areas and corridor networks for brown bear are concentrated in the Alborz and Zagros Mountains. These two core areas were predicted to be fragmented into a total of fifteen isolated patches if dispersal of brown bear across the landscape is limited to 50,000 cost units, and aggregates into two isolated habitat patches if the species is capable of dispersing 400,000 cost units. We found low overlap between corridors, and core habitats with protected areas, suggesting that the existing protected area network may not be adequate for the conservation of brown bear in Iran. Our results suggest that effective conservation of brown bears in Iran requires protection of both core habitats and the corridors between them, especially outside Iran’s network of protected areas., We would like to thank provincial offices of Iranian Department of Environment for supporting the study. J.V.L.B. was supported by a Ramon & Cajal research contract (RYC-2015-18932) from the Spanish Ministry of Economy, Industry and Competitiveness. V.P. was supported by (1) the Excellence Project CGL2017-82782-P financed by the Spanish Ministry of Science, Innovation and Universities, the Agencia Estatal de Investigación (AEI) and the Fondo Europeo de Desarrollo Regional (FEDER, EU), and by (2) a GRUPIN research grant from the Regional Government of Asturias (Ref.: IDI/2018/000151).

22 pages, 5 figures, 1 table, supplementary information https://doi.org/10.1038/s41558-021-01173-9.-- Data availabilityAll standardized forcing variables from the ESMs are available at https://doi.org/10.48364/ISIMIP.575744.1; all outputs from the MEMs are available via ISIMIP (https://www.isimip.org/gettingstarted/data-access/).-- Code availabilityAll code used to analyse simulations is available at https://github.com/Fish-MIP/CMIP5vsCMIP6, 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, This work was supported by the Jarislowsky Foundation (D.P.T.), the Natural Sciences and Engineering Research Council of Canada Discovery Grant programme (D.P.T., H.K.L., T.D.E., W.W.L.C., J.P.-A. and V.C.); Australian Research Council (ARC) Discovery Projects DP170104240 (J.L.B. and C.N.), DP190102293 (J.L.B., C.N., A.J.R., J.D.E. and D.P.T.) and DP150102656 (J.D.E.); the European Union’s Horizon 2020 research and innovation programme under grant agreements 817578 (TRIATLAS) (M.C., J.S., L.S., O.M., L.B., Y.-J.S., N.B. and J.R.), 869300 (FutureMARES) (J.A.F.-S.,Y.-J.S. and M.C.) and 862428 (MISSION ATLANTIC (J.A.F.-S, Y.-J.S. and M.C.); the Spanish National Project ProOceans (PID2020-118097RB-I00) (M.C. and J.S.); the Open Philanthropy Project (C.S.H.); the United Kingdom Research and Innovation (UKRI) Global Challenges Research Fund (GCRF) One Ocean Hub (NE/S008950/1) (K.O.-C. and L.S.); the Simons Foundation (nos. 54993, 645921) (G.L.B.); the Belmont Forum and BiodivERsA under the BiodivScen ERA-Net COFUND programme (SOMBEE project, ANR-18-EBI4-0003-01) (Y.-J.S. and N.B.); the MEOPAR Postdoctoral Fellowship Award 2020–2021 and the Ocean Frontier Institute (Module G) (A.B.-B.); the French ANR project CIGOEF (grant ANR-17-CE32-0008-01) (O.M., L.B. and J.R.); the California Ocean Protection Council Grant C0100400, the Alfred P. Sloan Foundation and the Extreme Science and Engineering Discovery Environment (XSEDE) allocation TG-OCE170017 (D.B. and J.G.); the National Oceanographic and Atmospheric Association (NA20OAR4310441, NA20OAR4310442) (C.M.P.). M.C. acknowledges the Severo Ochoa Centre of Excellence accreditation (CEX2019-000928-S) to the Institute of Marine Science (ICM-CSIC)

Most existing protected area networks are biased to protect charismatic species or landscapes. We hypothesized that conservation networks designed to include unseen biodiversity—species rich groups that consist of inconspicuous taxa, or groups affected by knowledge gaps—are more efficient than networks that ignore these groups. To test this hypothesis, we generated species distribution models for 3006 arthropod species to determine which were represented in three networks of different sizes and biogeographic origin. We assessed the efficiency of each network using spatial prioritization to measure its completeness, the increment needed to achieve conservation targets, and its specificity, the extent to which proposed priority areas to maximize unseen biodiversity overlap with existing networks. We found that the representativeness of unseen biodiversity in the studied protected areas, or extrinsic representativeness, is low, with ~ 40% of the analyzed unseen biodiversity species being unprotected. We also found that existing networks should be expanded ~ 26% to 46% of their current area to complete targets, and that existing networks do not efficiently conserve the unseen biodiversity given their low specificity (as low as 8.8%) unseen biodiversity. We conclude that information on unseen biodiversity must be included in systematic conservation planning approaches to design more efficient and ecologically representative protected areas.

Individual-based models are doubly complex: as well as representing complex ecological systems, the software that implements them is complex in itself. Both forms of complexity must be managed to create reliable models. However, the ecological modelling literature to date has focussed almost exclusively on the biological complexity. Here, we discuss methods for containing software complexity. Strategies for containing complexity include avoiding, subdividing, documenting and reviewing it. Computer science has long-established techniques for all of these strategies. We present some of these techniques and set them in the context of IBM development, giving examples from published models. Techniques for avoiding software complexity are following best practices for coding style, choosing suitable programming languages and file formats and setting up an automated workflow. Complex software systems can be made more tractable by encapsulating individual subsystems. Good documentation needs to take into account the perspectives of scientists, users and developers. Code reviews are an effective way to check for errors, and can be used together with manual or automated unit and integration tests. Ecological modellers can learn from computer scientists how to deal with complex software systems. Many techniques are readily available, but must be disseminated among modellers. There is a need for further work to adapt software development techniques to the requirements of academic research groups and individual-based modelling.