Evolving thermal thresholds explain the distribution of temperature sex reversal in an Australian dragon lizard

Aim: Species with temperature-dependent sex determination (TSD) are particularly vulnerable to climate change because a resultant skew in population sex ratio can have severe demographic consequences and increase vulnerability to local extinction. The Australian central bearded dragon (Pogona vitticeps) has a thermosensitive ZZ male/ZW female system of genetic sex determination (GSD). High incubation temperatures cause reversal of the ZZ genotype to a viable female phenotype. Nest temperatures in the wild are predicted to vary on a scale likely to produce heterogeneity in the occurrence of sex reversal, and so we predict that sex reversal will correlate positively with inferred incubation conditions. Location: Mainland Australia. Methods: Wild-caught specimens of P. vitticeps vouchered in museum collections and collected during targeted field trips were genotypically and phenotypically sexed to determine the distribution of sex reversal across the species range. To determine whether environmental conditions or genetic structure can explain this distribution, we infer the incubation conditions experienced by each individual and apply a multi-model inference approach to determine which conditions associate with sex reversal. Further, we conduct reduced representation sequencing on a subset of specimens to characterize the population structure of this broadly distributed species. Results: Here we show that sex reversal in this widespread Australian dragon lizard is spatially restricted to the eastern part of the species range. Neither climatic variables during the inferred incubation period nor geographic population genetic structure explain this disjunct distribution of sex reversal. The main source of genetic variation arose from isolation by distance across the species range. Main conclusions: We propose that local genetic adaptation in the temperature threshold for sex reversal can counteract the sex-reversing influence of high incubation temperatures in P. vitticeps. Our study demonstrates that complex evolutionary processes need to be incorporated into modelling biological responses to future climate scenarios

High-resolution distribution modeling of a threatened short-range endemic plant informed by edaphic factors

Short-range endemic plants often have edaphic specializations that, with their restricted distributions, expose them to increased risk of anthropogenic extinction. Here, we present a modeling approach to understand habitat suitability for Ricinocarpos brevis R.J.F.Hend. & Mollemans (Euphorbiaceae), a threatened shrub confined to three isolated populations in the semi-arid south-west of Western Australia. The model is a maximum entropy species distribution projection constructed on the basis of physical soil characteristics and geomorphology data at approximately 25 m2 (1 arc-second) resolution. The model predicts the species to occur on shallow, low bulk density soils that are located high in the landscape. The model shows high affinity (72.1% average likelihood of occurrence) for the known populations of R. brevis, as well as identifying likely locations that are not currently known to support the species. There was a strong relationship between the likelihood of R. brevis occurrence and soil moisture content that the model estimated at a depth of 20 cm. We advocate that our approach should be standardized using publicly available data to generate testable hypotheses for the distribution and conservation management of short-range endemic plant species for all of continental Australia

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

Enhancing digital soil mapping in southeastern Brazil: incorporating stream density and soil reflectance from multiple depths.

This study proposes a novel and simple method to incorporate laboratory soil spectral data in the production of digital soil maps

Accounting for heterogeneity in θ-σ relationship:application to wheat phenotyping using ΕMI

Geophysical methods, such as electromagnetic induction (EMI), can be effective for monitoring changes in soil moisture at the field scale, particularly in agricultural applications. The electrical conductivity (σ) inferred from EMI needs to be converted to soil moisture content (θ) using an appropriate relationship. Typically, a single global relationship is applied to an entire agricultural field, however, soil heterogeneity at the field scale may limit the effectiveness of such an approach. One application area that may suffer from such an effect is crop phenotyping. Selecting crop varieties based on their root traits is important for crop breeding and maximizing yield. Hence, high throughput tools for phenotyping the root system architecture and activity at the field-scale are needed. Water uptake is a major root activity and, under appropriate conditions, can be approximated by measuring changes in soil moisture from time-lapse geophysical surveys. We examine here the effect of heterogeneity in the θ-σ relationship using a crop phenotyping study for illustration. In this study, the θ-σ relationship was found to vary substantially across a field site. To account for this, we propose a range of local (plot specific) θ-σ models. We show that the large number of parameters required for these models can be estimated from baseline σ and θ measurements. Finally, we compare the use of global (field scale) and local (plot scale) models with respect to ranking varieties based on the estimated soil moisture content change

Combining visible near-infrared spectroscopy and water vapor sorption for soil specific surface area estimation

Abstract The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near‐infrared spectroscopy (vis‐NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests