Characterizing the spatial distribution of giant pandas (Ailuropoda melanoleuca) in fragmented forest landscapes

Aim To examine the effects of forest fragmentation on the distribution of the entire wild giant panda (Ailuropoda melanoleuca) population, and to propose a modelling approach for monitoring the spatial distribution and habitat of pandas at the landscape scale using Moderate Resolution Imaging Spectro‐radiometer (MODIS) enhanced vegetation index (EVI) time‐series data. Location Five mountain ranges in south‐western China (Qinling, Minshan, Qionglai, Xiangling and Liangshan). Methods Giant panda pseudo‐absence data were generated from data on panda occurrences obtained from the third national giant panda survey. To quantify the fragmentation of forests, 26 fragmentation metrics were derived from 16‐day composite MODIS 250‐m EVI multi‐temporal data and eight of these metrics were selected following factor analysis. The differences between panda presence and panda absence were examined by applying significance testing. A forward stepwise logistic regression was then applied to explore the relationship between panda distribution and forest fragmentation. Results Forest patch size, edge density and patch aggregation were found to have significant roles in determining the distribution of pandas. Patches of dense forest occupied by giant pandas were significantly larger, closer together and more contiguous than patches where giant pandas were not recorded. Forest fragmentation is least in the Qinling Mountains, while the Xiangling and Liangshan regions have most fragmentation. Using the selected landscape metrics, the logistic regression model predicted the distribution of giant pandas with an overall accuracy of 72.5% (κ = 0.45). However, when a knowledge‐based control for elevation and slope was applied to the regression, the overall accuracy of the model improved to 77.6% (κ = 0.55). Main conclusions Giant pandas appear sensitive to patch size and isolation effects associated with fragmentation of dense forest, implying that the design of effective conservation areas for wild giant pandas must include large and dense forest patches that are adjacent to other similar patches. The approach developed here is applicable for analysing the spatial distribution of the giant panda from multi‐temporal MODIS 250‐m EVI data and landscape metrics at the landscape scale.     

NEDD1: function in microtubule nucleation, spindle assembly and beyond

Nedd1 was originally identified as a developmentally regulated gene in the mouse central nervous system. NEDD1 has homologues across a range of species, being particularly conserved in a region of WD40 repeats contained in the amino-terminal half of the protein. Human NEDD1 was recently found to localise to the centrosome and mitotic spindle. It binds to the components of the gamma-tubulin ring complex and target this complex to the centrosome and spindle. Depletion of NEDD1 causes loss of the gamma-tubulin ring complex from the centrosome and results in the failure of microtubule nucleation and spindle assembly. In addition, phosphorylation of NEDD1 during mitosis is critical for targeting gamma-tubulin to the spindle, but not the centrosome. There is still much unknown about the function of this protein and how it may be involved in development and disease. This short review summarises some of the recent work on NEDD1 and discusses how this interesting protein may have additional yet unexplored functions.     

Finessing atlas data for species distribution models

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli’s eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data. Location Malaga province, Andalusia, southern Spain. Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 × 1 km) than the original atlas data (10 × 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli’s eagle at a local scale from a national‐level occurrence data set; however, the usefulness of this approach may be limited to well‐known species.     

Mechanisms underlying insect freeze tolerance

Freeze tolerance – the ability to survive internal ice formation – has evolved repeatedly in insects, facilitating survival in environments with low temperatures and/or high risk of freezing. Surviving internal ice formation poses several challenges because freezing can cause cellular dehydration and mechanical damage, and restricts the opportunity to metabolise and respond to environmental challenges. While freeze‐tolerant insects accumulate many potentially protective molecules, there is no apparent ‘magic bullet’ – a molecule or class of molecules that appears to be necessary or sufficient to support this cold‐tolerance strategy. In addition, the mechanisms underlying freeze tolerance have been minimally explored. Herein, we frame freeze tolerance as the ability to survive a process: freeze‐tolerant insects must withstand the challenges associated with cooling (low temperatures), freezing (internal ice formation), and thawing. To do so, we hypothesise that freeze‐tolerant insects control the quality and quantity of ice, prevent or repair damage to cells and macromolecules, manage biochemical processes while frozen/thawing, and restore physiological processes post‐thaw. Many of the molecules that can facilitate freeze tolerance are also accumulated by other cold‐ and desiccation‐tolerant insects. We suggest that, when freezing offered a physiological advantage, freeze tolerance evolved in insects that were already adapted to low temperatures or desiccation, or in insects that could withstand small amounts of internal ice formation. Although freeze tolerance is a complex cold‐tolerance strategy that has evolved multiple times, we suggest that a process‐focused approach (in combination with appropriate techniques and model organisms) will facilitate hypothesis‐driven research to understand better how insects survive internal ice formation.     

A Direct Interaction with NEDD1 Regulates γ-Tubulin Recruitment to the Centrosome

The centrosome is the primary microtubule organizing centre of the cell. γ-tubulin is a core component of the centrosome and is required for microtubule nucleation and centrosome function. The recruitment of γ-tubulin to centrosomes is mediated by its interaction with NEDD1, a WD40-repeat containing protein. Here we demonstrate that NEDD1 is likely to be oligomeric in vivo and binds directly to γ-tubulin through a small region of just 62 residues at the carboxyl-terminus of the protein. This carboxyl-terminal domain that binds γ-tubulin has a helical structure and is a stable tetramer in solution. Mutation of residues in NEDD1 that disrupt binding to γ-tubulin result in a mis-localization of γ-tubulin away from the centrosome. Hence, this study defines the binding site on NEDD1 that is required for its interaction with γ-tubulin, and shows that this interaction is required for the correct localization of γ-tubulin.