Drivers of habitat availability for terrestrial mammals: Unravelling the role of livestock, land conversion and intrinsic traits in the past 50 years.

The global decline of terrestrial species is largely due to the degradation, loss and fragmentation of their habitats. The conversion of natural ecosystems for cropland, rangeland, forest products and human infrastructure are the primary causes of habitat deterioration. Due to the paucity of data on the past distribution of species and the scarcity of fine-scale habitat conversion maps, however, accurate assessment of the recent effects of habitat degradation, loss and fragmentation on the range of mammals has been near impossible. We aim to assess the proportions of available habitat within the lost and retained parts of mammals' distribution ranges, and to identify the drivers of habitat availability. We produced distribution maps for 475 terrestrial mammals for the range they occupied 50 years ago and compared them to current range maps. We then calculated the differences in the percentage of 'area of habitat' (habitat available to a species within its range) between the lost and retained range areas. Finally, we ran generalized linear mixed models to identify which variables were more influential in determining habitat availability in the lost and retained parts of the distribution ranges. We found that 59% of species had a lower proportion of available habitat in the lost range compared to the retained range, thus hypothesizing that habitat loss could have contributed to range declines. The most important factors negatively affecting habitat availability were the conversion of land to rangeland and high density of livestock. Significant intrinsic traits were those related to reproductive timing and output, habitat breadth and medium body size. Our findings emphasize the importance of implementing conservation strategies to mitigate the impacts caused by human activities on the habitats of mammals, and offer evidence indicating which species have the potential to reoccupy portions of their former range if other threats cease to occur.

Translating habitat class to land cover to map area of habitat of terrestrial vertebrates.

Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.

Conversión de la Categoría de Hábitat a Cobertura de Terreno para Mapear el Área de Hábitat de los Vertebrados Terrestres Resumen El área del hábitat (AOH) está definida como "el hábitat disponible para una especie, es decir, el hábitat dentro del área de distribución de la especie" y se calcula mediante la sustracción de las áreas de terreno inadecuado y la elevación del área de distribución. El Esquema de Clasificación de Hábitats de la Unión Internacional para la Conservación de la Naturaleza proporciona información sobre las asociaciones entre los hábitats de las especies y con frecuencia se utilizan las opiniones no validadas de expertos para cotejar el hábitat con los tipos de cobertura de terreno, lo que genera una fuente de incertidumbre en los mapas de AOH. Desarrollamos un método orientado por datos para convertir las categorías de hábitat que maneja la UICN en cobertura de terreno basado en los datos de localidad puntual de 6,986 especies de mamíferos terrestres, aves, anfibios y reptiles. Extrajimos la categoría de cobertura de terreno en cada localidad puntual y la cotejamos con la categoría o categorías de hábitat de UICN asignada a cada especie incidente en la localidad. Después modelamos cada categoría de cobertura de terreno como función del hábitat según la UICN usando modelos de regresión logística. Las proporciones de probabilidad resultantes fueron usadas para evaluar la solidez de la asociación entre cada categoría de hábitat y de cobertura de terreno. Después comparamos el desempeño de nuestro modelo orientado por datos con el desempeño de una tabla de conversión publicada basada en el conocimiento de expertos. Calculamos la asociación entre las categorías de hábitat y las de cobertura de terreno como una variable continua, pero para mapear el AOH como una presencia o ausencia binaria, fue necesario aplicar un umbral de asociación. Este umbral puede ser elegido por el usuario de acuerdo con el balance requerido entre los errores de omisión y comisión. Algunos hábitats (p. ej.: bosques y desiertos) fueron asignados a las categorías de cobertura de terreno con más confianza que otros (p. ej.: humedales y artificiales). El modelo de conversión orientado por los datos y el conocimiento de los expertos tuvieron un desempeño igual de eficiente, pero el modelo proporcionó una mayor estandarización, objetividad y repetitividad. Además, nuestra estrategia permitió una mayor flexibilidad en el uso de los resultados y de la incertidumbre para ser cuantificados. Nuestro modelo puede modificarse para análisis regionales y para diferentes grupos taxonómicos.

Area of Habitat maps for the world's terrestrial birds and mammals.

Area of Habitat (AOH) is "the habitat available to a species, that is, habitat within its range". It complements a geographic range map for a species by showing potential occupancy and reducing commission errors. AOH maps are produced by subtracting areas considered unsuitable for the species from their range map, using information on each species' associations with habitat and elevation. We present AOH maps for 5,481 terrestrial mammal and 10,651 terrestrial bird species (including 1,816 migratory bird species for which we present separate maps for the resident, breeding and non-breeding areas). Our maps have a resolution of 100 m. On average, AOH covered 66 ± 28% of the range maps for mammals and 64 ± 27% for birds. The AOH maps were validated independently, following a novel two-step methodology: a modelling approach to identify outliers and a species-level approach based on point localities. We used AOH maps to produce global maps of the species richness of mammals, birds, globally threatened mammals and globally threatened birds.

Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data.

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN's computational burden and GSA's demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400-2500 nm spectral range at 15 cm-1 (between 0.3-9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.

Solid-phase synthesis of lipidated peptides.

A new flexible and efficient methodology for the solid-phase synthesis of lipidated peptides has been developed. The approach is based on the use of previously synthesized building blocks and overcomes the limitations of previously reported methods, since long doubly lipidated peptides can be synthesized by using this route. Furthermore, it was thus possible to prepare a large number of N- and H-Ras peptides bearing a wide range of reporter and/or linking groups--efficient tools for the investigation of biological processes. In terms of efficiency and flexibility this solid-phase method is superior to the solution-phase synthesis. It gives pure peptides in multimilligram amounts within a much shorter time and with superior overall yield.

An acylation cycle regulates localization and activity of palmitoylated Ras isoforms.

We show that the specific subcellular distribution of H- and Nras guanosine triphosphate-binding proteins is generated by a constitutive de/reacylation cycle that operates on palmitoylated proteins, driving their rapid exchange between the plasma membrane (PM) and the Golgi apparatus. Depalmitoylation redistributes farnesylated Ras in all membranes, followed by repalmitoylation and trapping of Ras at the Golgi, from where it is redirected to the PM via the secretory pathway. This continuous cycle prevents Ras from nonspecific residence on endomembranes, thereby maintaining the specific intracellular compartmentalization. The de/reacylation cycle also initiates Ras activation at the Golgi by transport of PM-localized Ras guanosine triphosphate. Different de/repalmitoylation kinetics account for isoform-specific activation responses to growth factors.

sigma-Borane and dihydroborate complexes of ruthenium.

Room temperature reaction of the bis(dihydrogen) complex RuH(2)(H(2))(2)(PCy(3))(2) (1) with excess pinacol borane (HBpin) generates the novel complex RuH[(mu-H)(2)Bpin](sigma-HBpin)(PCy(3))(2) (2) by loss of dihydrogen. Complex 2 was characterized spectroscopically and by X-ray crystallography. It contains two pinacolborane moieties coordinated in a different fashion, one as a dihydroborate (B-H distances : 1.58(3) and 1.47(3) A) and the other as a sigma-borane (B-H distance: 1.35(3) A). In addition, reaction of 1 with one equiv of HBpin yields total conversion to a new complex tentatively formulated as RuH[(mu-H)(2)Bpin](H(2))(PCy(3))(2) (3) on the basis of NMR data. In the presence of excess HBpin, 3 is converted to 2. Furthermore, under an atmosphere of dihydrogen, a C(7)D(8) solution of 2 rapidly converts to 3 and finally regenerates 1 over a much longer period. Thus, complex 3 is an intermediate in the formation of 2 from 1. In these processes the borane is eliminated as HBpin later hydrolyzed to BpinOBpin. Selective hydroboration of ethylene (3 bar) into C(2)H(5)Bpin is achieved using 1 or 2 as catalyst precursors in toluene, whereas in THF, competitive formation of the vinylborane C(2)H(3)Bpin (56% under 20 bar of C(2)H(4)) can be favored.