scholarly journals Environmental factors influencing guanaco distribution and abundance in central Patagonia, Argentina

2019 ◽  
Vol 46 (1) ◽  
pp. 1 ◽  
Author(s):  
Julieta Pedrana ◽  
Alejandro Travaini ◽  
Juan Ignacio Zanón ◽  
Sonia Cristina Zapata ◽  
Alejandro Rodríguez ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Vegetation Index ◽  
Oil Field ◽  
Population Trends ◽  
Anthropogenic Factors ◽  
Pest Species ◽  
Habitat Suitability Model ◽  
Argentine Patagonia ◽  
Normalised Difference Vegetation Index ◽  
Survey Effort

Context The guanaco is the largest wild herbivore inhabiting the Patagonian steppes. Since the end of the 19th Century, it has suffered a progressive decline in numbers owing to poaching and unregulated hunting because of on an assumed competition with sheep. Unfortunately, there has never been a management program for guanaco populations in Argentine Patagonia. Consequently, the guanaco is still considered a pest species by ranchers and has never been considered profitable in the range management model implemented in Patagonia. Aims The present article updates the distribution limits of guanaco and estimate its abundance across Chubut, a large province of Patagonia, Argentina. The relative effects of several environmental and anthropogenic factors on guanaco distribution are also assessed. Methods Road surveys (7010km) and species distribution modelling were used to build a habitat suitability model and a distribution map. A distance sampling method was used to estimate guanaco population densities and size. The survey effort required to monitor population trends in this region was also calculated. Key results According to the best habitat suitability model, guanaco distribution decreased with altitude and primary productivity, as measured by Normalised Difference Vegetation Index (NDVI), and increased with the distance to the nearest urban centre and oil field. Guanaco distribution showed a clear geographical pattern in Chubut, with low to medium occurrence probability towards the west and higher values towards the east. Guanaco population size was estimated as 657304 individuals (95% CI 457437 to 944059), with a mean density of 2.97 guanacos km–2. Finally, through simulations of guanaco monitoring, it was estimated that an annual survey effort of 10 to thirty 30-km road transects is needed to detect with confidence a significant population decrease or increase over the next 6 or 10 years. Conclusions The habitat suitability map presented herein highlights areas with high guanaco densities in Chubut, where it would be possible to identify ranches suitable for performing profitable herding and shearing experiences. Implications The maps of guanaco distribution and density, as well as the survey effort required to monitor population trends, may be used to inform decisions concerning the sustainable use of this species.

scholarly journals DELINEATING ECO-SENSITIVE ZONES USING GEOSPATIAL METHODS – A TEST CASE OF JHILMIL JHEEL CONSERVATION RESERVE

2018 ◽  
Vol IV-5 ◽  
pp. 459-466
Author(s):  
V. Prakash ◽  
S. Saran ◽  
G. Talukdar
Keyword(s):  
Habitat Suitability ◽  
Vegetation Index ◽  
Rapid Development ◽  
Test Case ◽  
Anthropogenic Pressures ◽  
Focal Species ◽  
Maxent Model ◽  
Normalised Difference Vegetation Index ◽  
Long Time ◽  
Swamp Deer

<p><strong>Abstract.</strong> Most of the protected areas (PAs) in India have a hard boundary; very rarely having a transition zone to minimise the negative human wildlife interface. With increasing anthropogenic pressures, areas surrounding PAs are becoming integral for conservation. Government of India introduced a concept of Eco-sensitive Zones (ESZ) around PAs to minimise anthropogenic pressures and regulate rapid development in these areas. However, delineation of ESZs is a complex process and may take a long time. In this paper, a novel geospatial approach has been presented to delineate ESZ using a species centric approach. A case study using Swamp deer (<i>Rucervus duvaucelli duvaucelli</i>) as focal species was explored for its potential to delineate ESZ around protected area Jhilmil Jheel Conservation Reserve (JJCR) located in Uttarakhand India. Maximum entropy or Maxent model was used to identify habitat suitability. Normalised difference vegetation index (NDVI), altitude, land cover and distance to roads were used as co-variates. Seasonal variations for habitat suitability were also considered. In this study habitat suitability map of swamp deer was further rationalised based on habitat fragmentation and management limitations and proposed as ESZ of JJCR. This approach for delineation of ESZ can be very useful for PAs in India which have focal species and are yet to declare their ESZ.</p>

Analysis of the spatial variation in the abundance of lesser rheas using density surface models

2018 ◽  
Vol 45 (1) ◽  
pp. 47 ◽  
Author(s):  
Milagros Antún ◽  
Ricardo Baldi ◽  
Lucas M. Bandieri ◽  
Romina L. D' Agostino
Keyword(s):  
Spatial Variation ◽  
Vegetation Index ◽  
Austral Summer ◽  
High Resolution Data ◽  
Argentine Patagonia ◽  
Lesser Rhea ◽  
Normalised Difference Vegetation Index ◽  
Surface Models ◽  
Density Surface ◽  
Sampling Procedures

Context The study of the spatial variation in abundance of wild populations and the identification of factors explaining the observed patterns are key both to understand aspects of basic ecology and the effects of human activities. This is usually difficult to evaluate for low-density and widely distributed species, such as the lesser rhea (Rhea pennata pennata), an endemic bird from South America. Recent advances in spatial modelling such as the density surface models (DSM) combine distance-sampling procedures with modelling techniques to produce maps of spatial variation in abundance, and its relationship with predictive variables. Aims We aimed to analyse the spatial distribution and abundance of lesser rhea, and the variables that affect its abundance in Península Valdés (PV) Argentine Patagonia. Methods We conducted 338.4 km of ground surveys of lesser rheas in PV during the end of the Austral summer of 2015. Spatial models were constructed using DSM. Ecological and human-related variables were included in the models to account for variation in the abundance of animals at 4-km2 spatial resolution. Key results We estimated an overall density of 0.44 birds km–2 (CV = 32%) for the prediction area of 3320 km2. High values of normalised difference vegetation index, a correlate of plant productivity, were associated with increased numbers of lesser rheas. The location of ranch buildings, indicators of human presence, had a strong negative effect on lesser rheas, although their abundance increased at high sheep stocking rates. Conclusions As reported by previous studies in different sites, the abundance of lesser rheas in our study area was low. The use of DSM allowed a detailed examination of the spatial variation, as well as the variables involved and the uncertainty of the prediction. Implications The use of DSM techniques can be a useful tool for conservation planning and monitoring. Spatial, high-resolution data combined with knowledge on the factors affecting the number of animals are crucial to target specific conservation actions and monitor their results, and should allow government agencies to make better decisions concerning conservation-oriented management.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

Habitat factors related to wild rabbit population trends after the initial impact of rabbit haemorrhagic disease

2006 ◽  
Vol 33 (6) ◽  
pp. 467 ◽  
Author(s):  
Carlos Calvete ◽  
Enrique Pelayo ◽  
Javier Sampietro
Keyword(s):  
Habitat Quality ◽  
Population Trends ◽  
Wild Rabbit ◽  
Pest Species ◽  
Predator Species ◽  
Rabbit Population ◽  
Habitat Factors ◽  
Rabbit Haemorrhagic Disease ◽  
Haemorrhagic Disease

The European wild rabbit (Oryctolagus cuniculus) is an introduced pest species in Australia and New Zealand. Rabbits have a devastating negative impact on agricultural production and biodiversity in these countries, and Rabbit Haemorrhagic Disease (RHD) is currently included in control strategies for rabbit populations. On the other hand, the European wild rabbit is a key native prey species in the Iberian Peninsula. Since the arrival of RHD, however, rabbit populations have undergone dramatic decreases and several predator species at risk of extinction are currently dependent on the rabbit population density. Therefore, from the point of view of biodiversity conservation, evaluating habitat correlates and trends of rabbit populations after the first RHD epizootic is of great interest to improve the long-term control or promotion of wild rabbit populations. We estimated the relationship between habitat factors and long-term population trends as well as the relationships between habitat factors and rabbit abundance 2 and 14 years after the arrival of RHD in several Iberian rabbit populations. We observed that only 26% of surveyed populations seemed to experience an increase in rabbit abundance over the last 12 years and that this increase was higher in the low-rabbit-abundance areas of l992, leading to high rabbit abundance in 2004. Our results suggested that short- and long-term impacts of RHD were related to habitat quality. The initial impact of RHD was higher in more suitable habitats, but increasing long-term population trends were positively related to good habitat quality.

Assessing the impact of seasonal precipitation and temperature on vegetation in a grass-dominated rangeland

2014 ◽  
Vol 36 (2) ◽  
pp. 185 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber
Keyword(s):  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Precipitation And Temperature

Changes in vegetation are affected by many climatic factors and have been successfully monitored through satellite remote sensing over the past 20 years. In this study, the Normalised Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, was selected as an indicator of change in vegetation. Monthly MODIS composite NDVI at a 1-km resolution was acquired throughout the 2004–09 growing seasons (i.e. April–September). Data describing daily precipitation and temperature, primary factors affecting vegetation growth in the semiarid rangelands of Idaho, were derived from the Surface Observation Gridding System and local weather station datasets. Inter-annual and seasonal fluctuations of precipitation and temperature were analysed and temporal relationships between monthly NDVI, precipitation and temperature were examined. Results indicated NDVI values observed in June and July were strongly correlated with accumulated precipitation (R2 >0.75), while NDVI values observed early in the growing season (May) as well as late in the growing season (August and September) were only moderately related with accumulated precipitation (R2 ≥0.45). The role of ambient temperature was also apparent, especially early in the growing season. Specifically, early growing-season temperatures appeared to significantly affect plant phenology and, consequently, correlations between NDVI and accumulated precipitation. It is concluded that precipitation during the growing season is a better predictor of NDVI than temperature but is interrelated with influences of temperature in parts of the growing season.

Temporal and spatial heterogeneity of drought impact on vegetation growth on the Inner Mongolian Plateau

2018 ◽  
Vol 40 (2) ◽  
pp. 113 ◽  
Author(s):  
Miao Bailing ◽  
Li Zhiyong ◽  
Liang Cunzhu ◽  
Wang Lixin ◽  
Jia Chengzhen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Severity Index ◽  
Drought Severity ◽  
Summer Drought ◽  
Desert Steppe ◽  
Vegetation Types ◽  
Mongolian Plateau ◽  
Typical Steppe ◽  
Recent Climate ◽  
Normalised Difference Vegetation Index

Drought frequency and intensity have increased in recent decades, with consequences for the structure and function of ecosystems of the Inner Mongolian Plateau. In this study, the Palmer drought severity index (PDSI) was chosen to assess the extent and severity of drought between 1982 and 2011. The normalised difference vegetation index (NDVI) was used to analyse the responses of five different vegetation types (forest, meadow steppe, typical steppe, desert steppe and desert) to drought. Our results show that during the last 30 years, the frequency and intensity of droughts have increased significantly, especially in summer and autumn. The greatest decline in NDVI in response to drought was observed in typical steppe and desert steppe vegetation types. Compared with other seasons, maximum decline in NDVI was observed in summer. In addition, we found that NDVI in the five vegetation types showed a lag time of 1–2 months from drought in the spring and summer. Ancillary soil moisture conditions influenced the drought response, with desert steppe showing a stronger lag effect to spring and summer drought than the other vegetation types. Our results show that drought explains a high proportion of changes in NDVI, and suggest that recent climate change has been an important factor affecting vegetation productivity in the area.

scholarly journals Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

2015 ◽  
Vol 10 (4) ◽  
pp. 215 ◽  
Author(s):  
Roberta Rossi ◽  
Alessio Pollice ◽  
Gianfranco Bitella ◽  
Rocco Bochicchio ◽  
Amedeo D'Antonio ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Medicago Sativa ◽  
Vegetation Index ◽  
Nonlinear Models ◽  
Ground Cover ◽  
Plant Productivity ◽  
Additive Models ◽  
Deep Soil ◽  
Soil Features ◽  
Normalised Difference Vegetation Index

Alfalfa is a highly productive and fertility-building forage crop; its performance, can be highly variable as influenced by within-field soil spatial variability. Characterising the relations between soil and forage- variation is important for optimal management. The aim of this work was to model the relationship between soil electrical resistivity (ER) and plant productivity in an alfalfa (<em>Medicago sativa</em> L.) field in Southern Italy. ER mapping was accomplished by a multi-depth automatic resistivity profiler. Plant productivity was assessed through normalised difference vegetation index (NDVI) at 2 dates. A non-linear relationship between NDVI and deep soil ER was modelled within the framework of generalised additive models. The best model explained 70% of the total variability. Soil profiles at six locations selected along a gradient of ER showed differences related to texture (ranging from clay to sandy-clay loam), gravel content (0 to 55%) and to the presence of a petrocalcic horizon. Our results prove that multi-depth ER can be used to localise permanent soil features that drive plant productivity.

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