Estimating alpha, beta, and gamma diversity through deep learning

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta, and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.

Species monitoring through space and time — combining microfluidic SNP genotyping and environmental niche modelling on herbarium specimens of the rare Northern dragonhead, Dracocephalum ruyschiana (Lamiaceae)

Aim: We have studied population genetic change through time in the Northern dragonhead, Dracocephalum ruyschiana (Lamiaceae); a plant species that has experienced a drastic population decline and habitat loss in Europe. We aimed at adding a historic level to the monitoring of dragonhead by testing a microfluidic SNP array approach on herbarium specimens up to 200 years old and comparing the genomic results with that of modern populations in Norway. We also aimed to gain a more holistic species knowledge to guide monitoring efforts by combining herbarium genomics with ecological niche modelling (ENM). Location: Europe (mainly Norway) Methods: We have applied a microfluidic array consisting of 96 SNP markers on 130 herbarium specimens collected from 1820 to 2008. Obtained genotype data were compared with SNP data from modern samples using various population genetic analyses. We used sample metadata and observational records to model the species’ environmental niche. Results: The SNP array successfully genotyped all included herbarium specimens but was less capable of capturing diversity outside of Norway, which was genetically highly divergent from the Norwegian dragonheads. The historic-modern comparison revealed similar genetic structure in space and limited change through time in Norway. The ENM suggests that dragonhead has not fully achieved its potential distribution in Norway, which is anchored in warmer and drier regions, including areas where it does not occur today. Main conclusions: With the appropriate design procedures, the SNP array technology is promising for genotyping old herbarium specimens; an invaluable source of information from the past. We found no signs of the severe reduction in population size in our temporal genomic data of Norwegian dragonhead. Regardless, the regional populations in Norway are genetically divergent, both from each other and more so from populations outside of Norway, rendering continued protection of all existing populations of the species relevant.

Hidden diversity within a polytypic species: The enigmatic Sceloporus torquatus Wiegmann, 1828 (Reptilia, Squamata, Phrynosomatidae)

The spiny lizard genus Sceloporus was described by Wiegmann in 1828, with S. torquatus posteriorly designated as the type species. The taxonomic history of S. torquatus is complicated, as it has been confused with other taxa by numerous authors. Many modern systematics works have been published on Sceloporus, but none have included all five recognized S. torquatus subspecies: S. t. torquatus, S. t. melanogaster, S. t. binocularis, S. t. mikeprestoni, and S. t. madrensis. Additionally, there is previous evidence for at least one unnamed taxon. The present study is the first taxonomic revision of the enigmatic S. torquatus based on molecular phylogenies using combined molecular data from 12S, ND4 and RAG1 genes, and Maximum Likelihood and Bayesian inference phylogenetic methods. This work includes the most extensive sampling across the entire distribution, as well as divergence time estimates and environmental niche modelling, which combined offer a spatio-temporal framework for understanding the evolution of the species. Additionally, a series of morphological characters are analyzed to identify significant differences between lineages consistently recovered in the molecular phylogenies. Using this integrative approach, evidence is presented for eight lineages within the S. torquatus complex, five of which correspond to previously recognized subspecies and three represent unnamed taxa masked by morphological conservatism. Finally, to maintain taxonomic stability a lectotype and paralectoype are designated for S. torquatus, and certain taxonomic changes are suggested in order to reflect the phylogenetic relationships within the S. torquatus complex.

Switching costs in stochastic environments drive the emergence of matching behaviour in animal decision-making through the promotion of reward learning strategies

A principle of choice in animal decision-making named probability matching (PM) has long been detected in animals, and can arise from different decision-making strategies. Little is known about how environmental stochasticity may influence the switching time of these different decision-making strategies. Here we address this problem using a combination of behavioral and theoretical approaches, and show, that although a simple Win-Stay-Loss-Shift (WSLS) strategy can generate PM in binary-choice tasks theoretically, budgerigars (Melopsittacus undulates) actually apply a range of sub-tactics more often when they are expected to make more accurate decisions. Surprisingly, budgerigars did not get more rewards than would be predicted when adopting a WSLS strategy, and their decisions also exhibited PM. Instead, budgerigars followed a learning strategy based on reward history, which potentially benefits individuals indirectly from paying lower switching costs. Furthermore, our data suggest that more stochastic environments may promote reward learning through significantly less switching. We suggest that switching costs driven by the stochasticity of an environmental niche can potentially represent an important selection pressure associated with decision-making that may play a key role in driving the evolution of complex cognition in animals.

The effect of landscape structure on the evolution of two alternative dispersal strategies

Background Dispersal is an important event for most organisms at least once in their life cycle. The evolution of dispersal can be influenced by local adaptation, landscape structure, and perceived temporal and spatial variation. The interaction between local adaptation, landscape heterogeneity, temporal variability and rules of dispersal may be more complex than previously assumed. Therefore, we sought to understand the influence of emigration rules and landscape structure on emerging dispersal rates and traits. Here, we implemented an individual-based model (IBM) of trait evolution in scenarios characterized by different landscape structures and different degrees of spatial heterogeneity and global temporal variation. Individuals could evolve two traits coding for their environmental niche (position of niche optimum and niche width), and two traits determining nearest-neighbor dispersal: an individual emigrates with a probability defined by the first trait (random emigration), but emigrates with certainty if the fertility expected in the patch of residence falls below a threshold specified by the second trait (habitat-dependent emigration). Results We note an interaction effect between dispersal strategy and spatial variance—lower emigration under habitat-dependent than under random emigration if spatial heterogeneity is low, but eventually a reversal of this ranking if heterogeneity becomes large. Landscapes with sharp transition of habitat attributes result in a high degree of spatial sorting, while fractal landscapes do not. Emigration rates are overall lowest, when spatial variation is highest. Conclusions We conclude that emergent emigration rates are influenced more by landscape structure and spatio-temporal heterogeneity than by the emigration strategy. With the ongoing land use change more research into this topic could help highlight the difficulties species might face under the change from landscapes characterized by gradual transition zones to landscapes dominated by abrupt ecotones, the latter typical for agricultural and urban settings.

Ecological assessment and environmental niche modelling of Himalayan rhubarb (Rheum webbianum Royle) in northwest Himalaya

In an era of anthropocene, threatened and endemic species with small population sizes and habitat specialists experience a greater global conservation concern in view of being at higher risk of extinction. Predicting and plotting appropriate potential habitats for such species is a rational method for monitoring and restoring their dwindling populations in expected territories. Ecological niche modelling (ENM) coalesces species existence sites with environmental raster layers to construct models that describe possible distributions of plant species. The present study is aimed to study the potential distribution and cultivation hotspots for reintroducing the high value, vulnerable medicinal herb (Rheum webbianum) in the Union territories of Jammu and Kashmir and Ladakh using population attributes and ecological niche modelling approach. Sixty-three populations inventoried from twenty-eight areas display a significant change in the phytosociological attributes on account of various anthropogenic threats. The current potential habitats coincide with actual distribution records and the mean value of Area Under Curve (AUC) was 0.98 and the line of predicted omission was almost adjacent to omission in training samples, thus validating a robustness of the model. The potential habitat suitability map based on the current climatic conditions predicted a total of 103760 km2 as suitable area for the growth of Rheum webbianum. Under the future climatic conditions, there is a significant reduction in the habitat suitability ranging from -78531.34 Km2 (RCP 4.5 for 2050) to -77325.81 (RCP 8.5 for 2070). Furthermore, there is a slight increase in the suitable habitats under future climatic conditions, ranging from +21.99 Km2 under RCP 8.5 (2050) to +3.14 Km2 under RCP 4.5 (2070). The Jackknife tests indicated Precipitation of Driest Month (BIO14) as the most contributing climatic variable in governing the distribution of R. webbianum. Therefore, scientifically sound management strategies are urgently needed to save whatever populations are left in-situ to protect this species from getting extinct. Present results can be used by conservationists for mitigating the biodiversity decline and exploring undocumented populations of R. webbianum on one hand and by policymakers in implementing the policy of conservation of species with specific habitat requirements by launching species recovery programmes in future on the other.

Climatic curves as predictors in MaxEnt niche modeling

Environmental niche modelling (ENM) uses different types of variables to predict species occurrence. In widespread use are variables derived from climatic curves, i.e., average annual changes in some climatic parameter. This study shows how to use the climatic curves themselves as ENM predictors. The key step is projection of the curves' constituent variables on a suitable spline basis, which preserves time-ordering of the variables and supports smoothness of predictions. Complexity of the model is controlled by sensible choice of the spline basis, followed by lasso regularization in model fitting.

Sickle Cell Anemia and Babesia Infection

Babesia is an intraerythrocytic, obligate Apicomplexan parasite that has, in the last century, been implicated in human infections via zoonosis and is now widespread, especially in parts of the USA and Europe. It is naturally transmitted by the bite of a tick, but transfused blood from infected donors has also proven to be a major source of transmission. When infected, most humans are clinically asymptomatic, but the parasite can prove to be lethal when it infects immunocompromised individuals. Hemolysis and anemia are two common symptoms that accompany many infectious diseases, and this is particularly true of parasitic diseases that target red cells. Clinically, this becomes an acute problem for subjects who are prone to hemolysis and depend on frequent transfusions, like patients with sickle cell anemia or thalassemia. Little is known about Babesia’s pathogenesis in these hemoglobinopathies, and most parallels are drawn from its evolutionarily related Plasmodium parasite which shares the same environmental niche, the RBCs, in the human host. In vitro as well as in vivo Babesia-infected mouse sickle cell disease (SCD) models support the inhibition of intra-erythrocytic parasite proliferation, but mechanisms driving the protection of such hemoglobinopathies against infection are not fully studied. This review provides an overview of our current knowledge of Babesia infection and hemoglobinopathies, focusing on possible mechanisms behind this parasite resistance and the clinical repercussions faced by Babesia-infected human hosts harboring mutations in their globin gene.