Biology subjects

Robin, V. V.

Publications and source records attributed to Robin, V. V..

4 recordsLinked to original sources

bioRxiv2021

Demographic changes of a tropical understory bird in naturally patchy montane habitats in southern India

The occurrence, density and survival of a species often depend on various aspects of the habitat that it occupies including patch size and disturbance. The demography of most threatened tropical species largely remain unstudied but could provide valuable information about their biology and insights for their conservation. Our study examined the effect of patch size and disturbance on different demographic parameters of an understory, threatened, endemic bird, the white-bellied shortwing in the tropical biodiversity hotspot of the Western Ghats in India. We sampled eight plots on a sky-island using mist-nets for four years in a Robust design mark-recapture framework. Based on model selection using AIC values, the model with survival as a function of disturbance fits the data better than models with abundance or with these parameters modeled as functions of forest patch size. Shortwing density and sex ratio were not different across forest patch sizes or differing disturbance regimes. However, the survival rate of the species significantly decreased with increasing disturbance but was not related to forest patch size. Our study is possibly the first to provide critical baseline information on the demography of a tropical understory species from this region.

ecology↗

bioRxiv2021

Spectrogram cross-correlation can be used to measure the complexity of bird vocalizations

O_LIBirdsong is an important signal in mate attraction and territorial defense. Quantifying the complexity of these songs can shed light on individual fitness, sexual selection, and behavior. Several techniques have been used to quantify song complexity and be broadly categorized into diversity indices, measures of stationary probabilities, and measures of sequential variations. However, these methods are unable to account for important acoustic features like the frequency bandwidth and the variety in the shape of syllables which are an integral part of these vocal signals. This study proposes a new complexity measure that considers intra-song note variability and calculates a weighted index for birdsongs using spectral cross-correlation. C_LIO_LIWe compared the previously described methods to understand the advantages and limitations based on the factors that would be affecting the complexity of songs. We developed a new method- Note Variability Index (NVI), which incorporates the spectral features of notes while quantifying complexity. This measure alleviates the need for manual annotations of notes that can be error-prone. We used Spectrogram Cross-Correlation (SPCC) to compare notes within a song and used the output values to quantify song complexity. C_LIO_LITo check for the efficacy of the new method, we generated synthetic songs to caricature extremes in song complexity and compared selected conventional complexity measures along with the NVI. We provide case-specific limitations of these methods. Additionally, to examine the efficacy of this new method in real-world scenarios, we used natural birdsongs from multiple species across the globe with varying song structures to compare conventional methods with NVI. C_LIO_LITo our knowledge, NVI is the only song complexity method that captures the variation of spectral features of notes in songs where the conventional methods fail to distinguish between similar song structures with different note types. As NVI does not need a manual classification of notes, it can be easily implemented for any type of birdsong with existing sound analysis softwares; it is very quick, avoids the possible biases in note classification, and can possibly be automated for large datasets in the future. C_LI

ecology↗

bioRxiv2021

Testing the efficacy of hyperspectral (AVIRIS-NG), multispectral (Sentinel-2) and radar (Sentinel-1) remote sensing images to detect native and invasive non-native trees

Invasive alien species (IAS) threaten tropical grasslands and native biodiversity and impact ecosystem service delivery, ecosystem function, and associated human livelihoods. Tropical grasslands have been dramatically and disproportionately lost to invasion by trees. The invasion continues to move rapidly into the remaining fragmented grasslands impacting various native grassland-dependent species and water streamflow in tropical montane habitats. The Shola Sky Islands of the Western Ghats host a mosaic of native grasslands and forests; of which the grasslands have been lost to exotic tree invasion (Acacias, Eucalyptus and Pines) since the 1950s. The invasion intensities, however, differ between these species wherein Acacia mearnsii and Pinus patula are highly invasive in contrast to Eucalyptus globulus. These disparities necessitate distinguishing these species for effective grassland restoration. Further, these invasive alien trees are highly intermixed with native species, thus requiring high discrimination abilities to native species apart from the non-native species. Here we assess the accuracy of various satellite and airborne remote sensing sensors and machine learning classification algorithms to identify the spatial extent of native habitats and invasive trees. Specifically, we test Sentinel-1 SAR and Sentinel-2 multispectral data and assess high spatial and spectral resolution AVIRIS-NG imagery identifying invasive species across this landscape. Sensor combinations thus include hyperspectral, multispectral and radar data and present tradeoffs in associated costs and ease of procurement. Classification methods tested include Support Vector Machine (SVM), Classification and Regression Trees (CART) and Random Forest (RF) algorithms implemented on the Google Earth Engine platform. Results indicate that AVIRIS-NG data in combination with SVM recover the highest classification skill (Overall -98%, Kappa-0.98); while CART and RF yielded < 90% accuracy. Fused Sentinel-1 and Sentinel-2 produce 91% accuracy, while Sentinel-2 alone yielded 91% accuracy with RF and SVM classification; but only with higher coverage of ground control points. AVIRIS-NG imagery was able to accurately (97%) demarcate the Acacia invasion front while Sentinel-1 and Sentinel-2 data failed. Our results suggest that Sentinel-2 images could be useful for detecting the native and non-native forests with more ground truth points, but hyperspectral data (AVIRIS-NG) permits distinguishing, native and non-native tree species and recent invasions with high precision using limited ground truth points. We suspect that large areas will have to be mapped and assessed in the coming years by conservation managers, NGOs to plan restoration, or to assess the success of restoration activities, and several data procurement and analysis steps may have to be simplified.

ecology↗

bioRxiv2019

Local extinction of a montane, endemic grassland bird driven by landscape change across its global distribution

ContextTropical montane habitats support high biodiversity, and are hotspots of endemism, with grasslands being integral components of many such landscapes. The montane grasslands of the Western Ghats have seen extensive land-use change over anthropogenic timescales. The factors influencing the ability of grassland-dependent species to persist in habitats experiencing loss and fragmentation, particularly in montane grasslands, are poorly known.\n\nObjectivesWe studied the relationship between the Nilgiri pipit Anthus nilghiriensis, a threatened endemic bird that typifies these montane grasslands, and its habitat, across most of its global distribution. We examined what habitat features make remnants viable habitat, which is necessary for effective management.\n\nMethodsWe conducted 663 surveys in 170 sites and used both single-season occupancy modelling and N-mixture modelling to account for processes influencing detection, presence, and abundance.\n\nResultsElevation had a positive influence on species presence, patch size had a moderate positive influence and patch isolation a moderate negative influence. Species abundance was positively influenced by elevation and characteristics related to habitat structure, and negatively influenced by the presence of invasive woody vegetation.\n\nConclusionsThe strong effect of elevation on the highly range-restricted Nilgiri pipit makes it vulnerable to, and an indicator of, climate change. This highly range-restricted species is locally extinct at several locations and persists at low densities in remnants of recent fragmentation, suggesting an extinction debt. Our findings indicate a need to control and reverse the spread of exotic woody invasives to preserve the grasslands themselves and the specialist species dependent upon them.

ecology↗