Extrinsic and intrinsic factors affecting the activity budget of alpine marmots ( Marmota marmota )

From Springer Nature via Jisc Publications RouterHistory: received 2021-05-24, accepted 2022-02-07, registration 2022-02-08, pub-electronic 2022-03-15, online 2022-03-15, pub-print 2022-07Publication status: PublishedAbstract: Extrinsic and intrinsic factors may influence the activity budget of wild animals, resulting in a variation in the time spent in different activities among populations or individuals of the same species. In this study, we examined how extrinsic and intrinsic factors affect the behaviour of the alpine marmot (Marmota marmota), a hibernating social rodent inhabiting high-elevation prairies in the European Alps. We collected behavioural observations during scan sampling sessions on marked individuals at two study sites with different environmental characteristics. We used Bayesian hierarchical multinomial regression models to analyse the influence of both intrinsic (sex and age-dominance status) and extrinsic (environmental and climatic variables) factors on the above-ground activity budget. Marmots spent most of their time above ground foraging, and were more likely to forage when it was cloudy. Extrinsic factors such as the site, period of the season (June, July–August, and August–September), and time of the day were all related to the probability of engaging in vigilance behaviour, which reaches its peak in early morning and late afternoon and during July, the second period included in the study. Social behaviours, such as affiliative and agonistic behaviours, were associated mostly with sex and age-dominance status, and yearlings were the more affiliative individuals compared to other status. Overall, our results suggest that in alpine marmots, intrinsic factors mostly regulate agonistic and affiliative behaviours, while extrinsic factors, with the unexpected exception of temperature, affect the probabilities of engaging in all types of behavioural categories

A high-resolution, integrated system for rice yield forecasting at district level

To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the ‘Tropical Japonica’ cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems

Estimation of nutritional properties of alpine grassland from MODIS NDVI data: R code

<p>Code used to preprocess data and make statistical analyses used in the following paper: <br>L. Ranghetti, B. Bassano, G. Bogliani, A. Palmonari, A. Formigoni, L. Stendardi and A. von Hardenberg (2016). "MODIS time series contribution for the estimation of nutritional properties of alpine grassland". <i>European Journal of Remote Sensing</i>, 49: 691-718, doi: http://dx.doi.org/10.5721/EuJRS20164936</p> <p>In particular, the code does the following operations:<br>1) download of the necessary MODIS tiles, generation of GeoTIFFs clipped on the local study area, calculation of NDVI images;<br>2) estimation of daily NDVI values for the position of field plots from MODIS preprocessed data;<br>3) determination of phenological variables (beginning of growing season, maximum yearly NDVI and relative day of occurrence) for each plot;<br>4) integration of field data (provided as external csv files) with computed measures of NDVI and phenological metrics;<br>5) calibration of the predictive models about biomass and nutritional variables (2012 dataset) and validation (2012 CV and 2013 datasets).</p> <p>Version 1.0 has been used to perform analysis in the paper. Please visit https://github.com/ggranga/LR_EstGrass for future development.</p

Nutritional content of alpine grassland in Gran Paradiso National Park, 2013 dataset

<p><strong>Abstract</strong></p> <p>This dataset has been collected to validate the predictability of forage quality of alpine grasslands from remotely sensed data. We investigated this relationship in the Gran Paradiso National Park (Western Italian Alps). In 2013 we collected a total of 112 grass samples within random plots, from June to July. From these samples we estimated biomass, relative and available crude protein, fiber (neutral detergent, acid detergent and lignin) and fiber digestibility (at 24 and 240 hours).</p><p>Data were used in the following publication: L. Ranghetti, B. Bassano, G. Bogliani, A. Palmonari, A. Formigoni, L. Stendardi and A. von Hardenberg (2016). «MODIS time series contribution for the estimation of nutritional properties of alpine grassland». <i>European Journal of Remote Sensing</i>, 49: 691-718, doi: http://dx.doi.org/10.5721/EuJRS20164936.</p> <p> </p> <p><strong>Data structure</strong></p> <p>field2013.csv</p> <p>int_id : int record ID (serial)<br>text_id: Factor w/ 142 levels record ID (string)<br>group : Factor w/ 18 levels group of the harvest (number of repetition)<br>doy : int day of the year of the harvest<br>plot : Factor w/ 19 levels plot name (matching with plot.2012$plot)<br>AB : num Aboveground Biomass (g)<br>H2O : num Water content (ratio between AB and fresh weight)<br>CP : num Crude Protein (% of total weight)<br>NDF : num Neutral Detergent fiber (% of total weight)<br>ADF : num Acid Detergent fiber (% of total weight)<br>ADL : num Lignin (% of total weight)<br>dNDF24 : num NDF digestibility after 24 hours (% of NDF)<br>dNDF240: num NDF digestibility after 240 hours (% of NDF)</p> <p> </p> <p>plots2013.dbf</p> <p>plot : Factor w/ 19 levels string code which identify single plots<br>elevation: int elevation of the plot, estracted from the Tinitaly 10m DEM (see text)<br>aspect : int aspect of the plot, derived from the elevation map</p> <p> </p

Nutritional content of alpine grassland in Gran Paradiso National Park, 2012 dataset

<p><strong>Abstract</strong></p> <p>This dataset has been collected to check the predictability of forage quality of alpine grasslands from remotely sensed data. We investigated this relationship in the Gran Paradiso National Park (Western Italian Alps). In 2012 we collected a total of 142 grass samples within 19 experimental plots every two weeks during the whole growing season. From these samples we estimated biomass, relative and available crude protein, fiber (neutral detergent, acid detergent and lignin) and fiber digestibility (at 24 and 240 hours).</p><p>Data were used in the following publication: L. Ranghetti, B. Bassano, G. Bogliani, A. Palmonari, A. Formigoni, L. Stendardi and A. von Hardenberg (2016). «MODIS time series contribution for the estimation of nutritional properties of alpine grassland». European Journal of Remote Sensing, 49: 691-718, doi: http://dx.doi.org/10.5721/EuJRS20164936.</p> <p> </p> <p><strong>Data structure</strong></p> <p>field2012.csv</p> <p>int_id : int record ID (serial)<br>text_id: Factor w/ 142 levels record ID (string)<br>group : Factor w/ 18 levels group of the harvest (number of repetition)<br>doy : int day of the year of the harvest<br>plot : Factor w/ 19 levels plot name (matching with plot.2012$plot)<br>AB : num Aboveground Biomass (g)<br>H2O : num Water content (ratio between AB and fresh weight)<br>CP : num Crude Protein (<p> </p> <p>field2012_heights.csv</p> <p>int_id : int record ID (serial)<br>text_id: Factor w/ 142 levels record ID (string)<br>group : Factor w/ 18 levels group of the harvest (number of repetition)<br>doy : int day of the year of the harvest<br>plot : Factor w/ 19 levels plot name (matching with plot.2012$plot)<br>h_tot : num Mean height of total biomass (cm)<br>h_green: num Mean height of green biomass (cm)</p> <p> </p> <p>plots2012.dbf</p> <p>plot : Factor w/ 19 levels string code which identify single plots<br>elevation: int elevation of the plot, estracted from the Tinitaly 10m DEM (see text)<br>aspect : int aspect of the plot, derived from the elevation map<br>Localita : Factor w/ 19 levels descriptive location of plots (in Italian)</p> <p> </p> <p> </p> <p> </p

Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies

The intensive rice cultivation area in northwestern Italy hosts the largest surface of rice paddies in Europe, and it is valued as a substantial habitat for aquatic biodiversity, with the paddies acting as a surrogate for the lost natural wetlands. The extent of submerged paddies strictly depends on crop management practices: in this framework, the recent diffusion of rice seeding in dry conditions has led to a reduction of flooded surfaces during spring and could have contributed to the observed decline of the populations of some waterbird species that exploit rice fields as foraging habitat. In order to test the existence and magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period, MODIS remotely-sensed data were used to estimate the extent of the average flooded surface and the proportion of flooded rice fields in the years 2000–2016 during the nesting period of waterbirds. A general reduction of flooded rice fields during the rice-sowing season was observed, averaging − 0.86 ± 0.20 % per year (p-value &lt; 0.01). Overall, the loss in submerged surface area during the sowing season reached 44 % of the original extent in 2016, with a peak of 78 % in the sub-districts to the east of the Ticino River. Results highlight the usefulness of remote sensing data and techniques to map and monitor water dynamics within rice cropping systems. These techniques could be of key importance to analyze the effects at the regional scale of the recent increase of dry-seeded rice cultivations on watershed recharge and water runoff and to interpret the decline of breeding waterbirds via a loss of foraging habitat

ropensci/MODIStsp: Version 2.0.5

MODIStsp 2.0.5 Main changes Edit documentation related to the change of maintainer (see https://docs.ropensci.org/MODIStsp/articles/lorenzo). Add the argument parallel to function MODIStsp() and MODSIStsp_process() to allow running the processing in single core modality. Minor changes Fix Travis tests Bug fix (#222) Previous versions Versions 2.0.0 to 2.0.4 were not referenced on GitHub; here below the news of those versions (the source code can be found on CRAN). MODIStsp 2.0.3 - Main changes This submission should fix errors on Debian CRAN builds, due to improper trigger of an internal function leading to writing in the user's lib folder. Fixes a bug leading to crash when using scale_val = TRUE and change_no_data = FALSE Fixes a bug leading to the GUI crashing rather than giving info messages in case not all input parameters are specified Implements redirection to MODIS products web pages when pressing the corresponding button Modifies slightly the Shiny GUI MODIStsp 2.0.0 - Main changes Replace the old gWidgets-based GUI with a new one based on Shiny; Enhances support for CLI usage. Now all parameters can be passed to the MODIStsp function. If also a opts_file is passed, values specified explicitly in the call override those in the options file; Fixes problems in retrieval of corners for MODIS products in 4008 projection (fixes #204); Fixes problems/improves support for datasets with multiple NoData values. Now, all NoData values are kept to original values if NoData change is set to FALSE. Also, Scale/Offset are no longer wrongly applied also to NoData values when scaleval = TRUE