Prediction of the distribution of Arctic‐nesting pink‐footed geese under a warmer climate scenario

Global climate change is expected to shift species ranges polewards, with a risk of range contractions and population declines of especially high-Arctic species. We built species distribution models for Svalbard-nesting pink-footed geese to relate their occurrence to environmental and climatic variables, and used the models to predict their distribution under a warmer climate scenario. The most parsimonious model included mean May temperature, the number of frost-free months and the proportion of moist and wet moss dominated vegetation in the area. The two climate variables are indicators for whether geese can physiologically fulfil the breeding cycle or not and the moss vegetation is an indicator of suitable feeding conditions. Projections of the distribution to warmer climate scenarios propose a large north- and eastward expansion of the potential breeding range on Svalbard even at modest temperature increases (1 and 21C increase in summer temperature, respectively). Contrary to recent suggestions regarding future distributions of Arctic wildlife, we predict that warming may lead to a further growth in population size of, at least some, Arctic breeding geese

Predicting Distribution of Aedes Aegypti and Culex Pipiens Complex, Potential Vectors of Rift Valley Fever Virus in Relation to Disease Epidemics in East Africa.

The East African region has experienced several Rift Valley fever (RVF) outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. DIVERSE ECOLOGICAL NICHE MODELLING TECHNIQUES HAVE BEEN DEVELOPED FOR THIS PURPOSE: we present a maximum entropy (Maxent) approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex) responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods

Spatial heterogeneity of habitat suitability for Rift Valley fever occurrence in Tanzania: an ecological niche modelling approach

Despite the long history of Rift Valley fever (RVF) in Tanzania, extent of its suitable habitat in the country remains unclear. In this study we investigated potential effects of temperature, precipitation, elevation, soil type, livestock density, rainfall pattern, proximity to wild animals, protected areas and forest on the habitat suitability for RVF occurrence in Tanzania. Presence-only records of 193 RVF outbreak locations from 1930 to 2007 together with potential predictor variables were used to model and map the suitable habitats for RVF occurrence using ecological niche modelling. Ground-truthing of the model outputs was conducted by comparing the levels of RVF virus specific antibodies in cattle, sheep and goats sampled from locations in Tanzania that presented different predicted habitat suitability values. Habitat suitability values for RVF occurrence were higher in the northern and central-eastern regions of Tanzania than the rest of the regions in the country. Soil type and precipitation of the wettest quarter contributed equally to habitat suitability (32.4% each), followed by livestock density (25.9%) and rainfall pattern (9.3%). Ground-truthing of model outputs revealed that the odds of an animal being seropositive for RVFV when sampled from areas predicted to be most suitable for RVF occurrence were twice the odds of an animal sampled from areas least suitable for RVF occurrence (95% CI: 1.43, 2.76, p < 0.001). The regions in the northern and central-eastern Tanzania were more suitable for RVF occurrence than the rest of the regions in the country. The modelled suitable habitat is characterised by impermeable soils, moderate precipitation in the wettest quarter, high livestock density and a bimodal rainfall pattern. The findings of this study should provide guidance for the design of appropriate RVF surveillance, prevention and control strategies which target areas with these characteristics

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling.

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere

Current and potential geographical distribution of Platymeris biguttatus (Linnaeus, 1767) with description of nymphs

Background: The description of Platymeris biguttatus (Linnaeus 1767) nymphal instars as well as the prediction of the potentially suitable ecological niche was the main goal of this study. Our research was based on 258 specimens of P. biguttatus species of museum collections. A set of 23 environmental predictor variables covering Africa was used at ecological niche modeling - a method performed using the Maxent software to prepare potential distribution maps for this species. Results: The results suggested the most suitable areas seen as potentially suitable ecological niche for P. biguttatus in Africa. A jackknife test showed that temperature seasonality and percentage of tree cover were among the most important environmental variables affecting the distribution of the species. The analysis of climate preferences shows that most of the potentially suitable niches for this species were located in the area of tropical savanna climate, with a small participation of tree vegetation. Conclusions: P. biguttatus was only known to be widely distributed in the tropical part of continental Africa. Thanks to the ecological niche modeling methods and the museum data on the occurrence of the species, we introduced new information about potentially suitable ecological niches and the possible range of distribution

Building leaders for the UN Ocean Science Decade : a guide to supporting early career women researchers within academic marine research institutions

Diverse and inclusive marine research is paramount to addressing ocean sustainability challenges in the 21st century, as envisioned by the UN Decade of Ocean Science for Sustainable Development. Despite increasing efforts to diversify ocean science, women continue to face barriers at various stages of their career, which inhibits their progression to leadership within academic institutions. In this perspective, we draw on the collective experiences of thirty-four global women leaders, bolstered by a narrative review, to identify practical strategies and actions that will help empower early career women researchers to become the leaders of tomorrow. We propose five strategies: (i) create a more inclusive culture, (ii) ensure early and equitable career development opportunities for women ECRs, (iii) ensure equitable access to funding for women ECRs, (iv) offer mentoring opportunities and, (v) create flexible, family-friendly environments. Transformational, meaningful, and lasting change will only be achieved through commitment and collaborative action across various scales and by multiple stakeholders.Peer reviewe

The importance of Portuguese Continental Shelf Waters to Balearic Shearwaters revealed by aerial census

The Balearic shearwater Puffinus mauretanicus is one of the most threatened seabirds in the world. To evaluate the abundance and distribution of Balearic Shearwaters in Portuguese Continental Shelf Waters, during the post-breeding period when migrating birds are outside the Mediterranean Sea, we conducted 5 aerial surveys between 2010 and 2014 (21 survey days covering 62,716 km2). Following a line transect method, observers recorded a total of 181 Balearic Shearwaters sightings. Using Distance sampling software, we estimated an overall species abundance (2010–2014) of 10,182, ranging between 2338 in 2010 and 23,221 individuals in 2012. During the 2012 post-breeding period, the Portuguese Continental Shelf Waters were used by up to 96.8% of the latest migratory population assessment. Considering Balearic Shearwater estimates per sampling block, there was a preference for the North and Center sectors of the Portuguese coast (respectively, 7058 and 1366 individuals) where several SPAs were already designated. We computed the annual and overall habitat predictive models for Balearic Shearwaters using a maximum entropy algorithm on MaxEnt software. In all models, the Balearic shearwater distribution was best predicted by mean chlorophyll concentration. Balearic Shearwaters are mostly present in shallow shelf and coastal waters particularly in the widest portions of the continental shelf. These areas are strongly influenced by upwelling, which concurs with the chlorophyll concentration being the most important predicting variable. Portuguese Continental Shelf Waters are one of the most important post-breeding grounds to the Balearic ShearwaterPortuguese Wildlife Society and projects SafeSea EEA-Grants, FAME (Proj. 2009-1/089) and European Commission’s Life Programme (MarPro NAT/PT/00038). This study was also partly supported by the Portuguese Foundation for Science and Technology (FCT) with Grants SFRH/ BD/30240/2006 to M. Ferreira and SFRH/BD/32841/2006 to P. C. Rodrigues. C. Eira is supported by FCT through CESAM UID/AMB/50017/2013 co-funded by FCT/MEC and FEDER, within PT2020 and Compete 2020 and S. Monteiro is financed by a Grant (BPD/0043/AMB/50017) from UID/AMB/50017/2013. This work was also partially supported by the strategic programme UID/BIA/04050/2013 (POCI-01-0145-FEDER-007569) funded by FCT and by ERDF (COMPETE2020). The authors thank observers and airplane pilots who contributed to this workinfo:eu-repo/semantics/publishedVersio

Are we failing to protect threatened mangroves in the Sundarbans world heritage ecosystem?

The Sundarbans, the largest mangrove ecosystem in the world, is under threat from historical and future human exploitation and sea level rise. Limited scientific knowledge on the spatial ecology of the mangroves in this world heritage ecosystem has been a major impediment to conservation efforts. Here, for the first time, we report on habitat suitability analyses and spatial density maps for the four most prominent mangrove species - Heritiera fomes, Excoecaria agallocha, Ceriops decandra and Xylocarpus mekongensis. Globally endangered H. fomes abundances declined as salinity increased. Responses to nutrients, elevation, and stem density varied between species. H. fomes and X. mekongensis preferred upstream habitats. E. agallocha and C. decandra preferred down-stream and mid-stream habitats. Historical harvesting had negative influences on H. fomes, C. decandra and X. mekongensis abundances. The established protected area network does not support the most suitable habitats of these threatened species. We therefore recommend a reconfiguration of the network to include these suitable habitats and ensure their immediate protection. These novel habitat insights and spatial predictions can form the basis for future forest studies and spatial conservation planning, and have implications for more effective conservation of the Sundarbans mangroves and the many other species that rely on them

Are We Predicting the Actual or Apparent Distribution of Temperate Marine Fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions