Effects of food availability and its seasonal changes on the dietary habits of wild silvery lutungs (Trachypithecus cristatus) in a coastal forest in west Sumatra, Indonesia
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Abstract The dietary composition of primates is often affected by the availability of food, as well as seasonal variations within their habitat. This study presents quantitative data on the feeding behavior of wild silvery lutungs ( Trachypithecus cristatus ) inhabiting a coastal forest in West Sumatra, Indonesia. This research emphasizes the relationship between food availability and the way this changes by season. Feeding strategies of these primates was also examined. Feeding behavior of a group of wild silvery lutungs was observed for 12 months from August 2018 to July 2019, with a total observation time of 482 hours. We recorded 74 items from 37 different plant species that were included in the lutungs’ diet. Nineteen out of 37 plant species comprised > 1% of the lutung’s annual diet. They can therefore be considered the main plant food sources for the lutungs. The lutungs mainly fed on young leaves (68.3%). However, during certain months, both ripe and unripe fruit formed a major part of their diet. Plant part-based analyses showed a negative correlation between the availability of young leaves and ripe fruit feeding. Plant species-based analyses revealed few significant positive relationships between the feeding percentages of the main dietary items (mainly young leaves) and their availability. This implies that young leaves and ripe fruit were both the preferred and the fallback foods. This also indicates that lutung activity, ranging patterns, and activity at the study site are affected mainly by food distribution and availability. Further research is needed to increase the understanding of Asian colobine feeding strategies, feeding patterns, and food resource characteristics.Keywords:
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Abstract This study documents the existence of January season‐ality in preferred stocks. January seasonality is found in all grades of preferred stock while stronger summer returns are also observed in low‐grade preferred, which resembles findings reported in common equity. Although January seasonality is also found in high‐grade preferred stocks, a size effect‐which is used to explain the January seasonality in low‐grade bonds‐may not be able to account for the same seasonality observed in high‐grade preferred.
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Several methods have been used to assess the seasonality of health outcomes in epidemiological studies. However, little information is available on the methods to study the changes in seasonality before and after adjusting for environmental or other known seasonally varying factors. Such investigations will help us understand the role of these factors in seasonal variation in health outcomes and further identify currently unknown or unmeasured risk factors. This tutorial illustrates a statistical procedure for examining the seasonality of health outcomes and their changes, after adjusting for potential environmental drivers by assessing and comparing shape, timings and size. We recommend a three-step procedure, each carried out and compared before and after adjustment: (i) inspecting the fitted seasonal curve to determine the broad shape of seasonality; (ii) identifying the peak and trough of seasonality to determine the timings of seasonality; and (iii) estimating the peak-to-trough ratio and attributable fraction to measure the size of seasonality. Reporting changes in these features on adjusting for potential drivers allows readers to understand their role in seasonality and the nature of any residual seasonal pattern. Furthermore, the proposed approach can be extended to other health outcomes and environmental drivers.
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Seasonality in tourism has traditionally been regarded as a major problem which needs to be overcome, but relatively little research has been conducted on the patterns or causes of this phenomenon. The paper begins with a discussion of the characteristics of seasonality and develops a methodology to study the phenomenon in more detail. Seasonality in parts of Scotland are analysed in order to test the approach suggested and to determine if the measures proposed are practical. Two basic questions are examined, whether seasonality in tourism in the study area has changed significantly over a thirty year period, and whether there are spatial dimensions to seasonality that can be detected in the study area. The paper concludes that the variation in seasonality over the time period studied is more complex than anticipated and results are not consistent throughout the area and the measures used. Evidence of spatial variation in seasonality was confirmed. The study concludes that while the measures and approach used would appear to have merit and applicability, further research is needed to produce definitive answers and to fully understand the process of seasonality in tourism.
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The purpose of this paper is to quantify seasonal variations in tourism and to benefit from an understanding of seasonality. The investigation was based on data regarding the number of tourist arrivals in Montenegro and the approach is based on five measures seasonal range, coefficient of seasonal variation, seasonality ratio, seasonality indicator and the Gini coefficient. The results of quantifying show a pronounced seasonality that is constant with only negligible variations over time. There are no vital discrepancies among the results obtained using different measures. These findings suggest the following: to understand the nature of seasonality it is enough to use only one measure and there is no need for a holistic way of measuring.
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Correlation in space between seasonality of malaria and seasonality of rainfall was studied in Sri Lanka. A simple seasonality index was developed by making use of the bimodal seasonality of both malaria and rainfall. The malaria seasonality index was regressed against the rainfall seasonality index taking spatial autocorrelation into account. Despite the presence of spatial autocorrelation, the coefficient for the rainfall seasonality index in explaining the malaria seasonality index was found to be significant. The results suggest that rainfall is an important driver of malaria seasonality.
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Petra is a mature tourism destination in the south of Jordan with a degree of seasonality over the last 10 years. Despite the recognized importance of Petra for the tourism industry in Jordan, there has been a lack of studies that discuss seasonal demand variations and its impacts on other related industries in the region. Yet, this study aims at analyzing patterns and effects of seasonality of tourism demand in Petra for the period 2006-2017. The required data was obtained from Ministry of Tourism and Antiquities (MoTA). Four methods were used to measure tourism seasonality in Petra. These are: Seasonality indicator; Seasonality ratio; Gini coefficient; and Seasonality index. The results of the study showed a modest level of tourism seasonality in the study area. Among methods, Seasonality index appeared to be the appropriate and simple way to calculate seasonality patterns at tourism destinations. The results showed that there are two peaks of seasonality in tourism demand of Petra. The first and the highest one was in April and the second took place in the months of October and November. In addition, seven months represented the low season of tourism demand in Petra. These are December, January and February as well as June, July, August and September. The tourism seasonality in Petra based on that is mainly due to the weather in these months which represent the coldest and warmest months in the year respectively.
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