1. Introduction
The Crested Ibis (Nipponia nippon) is one of the rarest bird species in the world. It was formerly widespread in North-east Asia until the late nineteenth century, north from Russia Far East to Taiwan Island and east from Japan to Gansu Province in China [1] The population of Crested Ibis rapidly declined during the first half of the twentieth century due to habitat degradation and over-hunting, and the species was even evaluated as extinct in the wild when the last five individuals in Japan were captured into captivity in 1981. After a few months in the same year, a remnant population of two breeding pairs and three nestlings was re-discovered in Yangxian, Shaanxi Province in Central China and renewed hope for this species [2].
Intensive conservation efforts have been made to restore the population since the rediscovery. The Shaanxi Crested Ibis Nature Reserve was established, and intensive breeding monitoring was conducted to prevent predation of eggs and chicks and of nest disturbance by human activities. To mitigate the conflicts between animal conservation and local community development and ensure continuous population development, a series of community-based conservation measures have been undertaken [3]. As a result, the wild population increased to over 6000 individuals in 2022, with another 1500 individuals in captivity.
To reduce the risk of extinction inherent to a species confined to limited area vulnerable to catastrophes such as epidemic diseases, climate disasters and feeding ground pollution, an experimental reintroduction was conducted at a basin in Qinling Mountain, 30 km from the main distribution area of wild population during 2004–2005, where 23 captive-bred Crested Ibises were released after acclimation and successfully bred since 2006 [4]. Since 2007, reintroduction of Crested Ibis has been carried out at Shaanxi, Henan, Zhejiang, and Hunan Province [4,5,6,7]. In addition, studying the impact of climate change on birds is an important research field in avian ecology. When climate changes, the physiological and biochemical responses of birds also change [8]. According to a report by the World Wildlife Fund [9], the significant decrease in rainfall in the coastal areas of southern California, USA, in 2002 led to a 97% decrease in the breeding numbers of the Brown-capped Rosy-Finch and Wrentit, almost facing extinction. When global temperatures rise by 3 °C, the habitat of the Red-tailed Black-Cockatoo in southeastern Australia will be reduced to 2% of its current level. Hence, climate change’s impact on the survival of birds is becoming increasingly evident, and global warming will lead to the extinction of a large number of bird species. In light of this, this paper speculated that climate change may also affect the survival of the Crested Ibis, and explore the correlation between the activity trajectory of Crested Ibis and climate changes by statistical methods.
2. Materials and Methods
2.1. Overview of the Study Area
The study area is located in the Crested Ibis National Nature Reserve (107°21′–107°44′ E, 33°44′–33°35′ N) in Yangxian County, Hanzhong City of Shaanxi Province. The experimental area is situated in a transitional zone from mountains to low mountains and hills, with a total area of 37,549 hectares. The change in altitude presents an obvious vertical gradient. The lowest altitude is 500 m and the highest altitude reaches 2900 m. The area belongs to the transitional climatic zone from the warm and humid to the northern subtropical zone. The average temperature is 12–14 °C, and the annual precipitation is 900–1000 mm. There are various types of land cover in the study area, including rice paddies, reservoirs, rivers, and farmlands in valleys.
2.2. Collection and Preprocessing of Trajectory Data
The crested ibis’s trajectory was tracked using the HQBG3621L backpack-style tracker (provided by Hunan Global Letter Technology Co., Ltd., Changsha, China), which is about 79 mm long, 23 mm wide, 30 mm high, and weighs about 26–32 g. The backpack-style tracker was put on five young crested ibises during the 2015 breeding season. These young crested ibises came from different nests in the Crested Ibis National Nature Reserve in Yangxian County, Shaanxi Province. The tracker collects the Crested Ibis’s trajectory data in real time through the combination of Beidou and GPS. The tracker has a battery life of up to five years when the average sunshine duration is greater than 1 h. The sampling frequency of the tracker is 1 h, and the collected data are transmitted to the server through GPRS and stored in the local storage card at the same time to ensure safe storage of trajectory data. As shown in Table 1, the trajectory data mainly include equipment terminal number, time, longitude, latitude, speed, altitude, accuracy, and heading direction (true north is 0).
During long-term operation, due to abnormal conditions such as insufficient power supply of the tracker, extreme weather, and occlusion by obstacles, the collected data may have problems such as data loss and low accuracy. For data with a single loss rate of less than 10%, the data are filled by cubic spline interpolation; for data with a single loss rate greater than 10%, or data with an accuracy level of C or D, it should be directly eliminated.
In addition, if the speed is not “0”, it indicates that the Crested Ibis is flying. Such data should not be considered a stopping point and should be “invalid data”. For “invalid data”, it is automatically eliminated by setting a threshold (speed > 5 km/h). Through the above data filling and eliminating, a high-quality data basis can be provided for subsequent identification of habitats.
As shown in Table 2, the dataset used in this study includes 8760 data pieces for Crested Ibis with ID 22 (CAFL003), 1245 data pieces for Crested Ibis with ID 21 (CAFL004), 312 data pieces for Crested Ibis with ID 20 (CAFL013), 525 data pieces for Crested Ibis with ID 18 (CAFL013), and 871 data pieces for Crested Ibis with ID 10 (4B04A0). Each data entry consists of 8 bytes. The dataset also includes data from weather stations with ID 57126 in Yang County, Xi’an, and ID 53947 in Tongchuan, providing daily average temperature, relative humidity, and rainfall data. The data from the Tongchuan weather station cover the period from 18 March 2014, to 7 July 2020, while the data from the Yang County weather station cover the period from 24 July 2015, to 7 September 2020.
2.3. Handling Missing Values
Due to issues such as sensor device failure and unstable wireless links, there are significant data missing problems in the Crested Ibis monitoring dataset. Figure 1 shows the extent of data missing in the Crested Ibis activity data, revealing that the missing data mainly manifests as continuous gaps of different durations. If the missing data are not addressed, it results in a reduced amount of information obtained and potentially lead to inaccurate calculation results. However, the monitoring period for individual Crested Ibis activity is long, and it is not possible to repeat observations that were unsatisfactory [10,11]. Therefore, this study compares six time series missing data imputation methods, including simple imputation, clustering imputation, weighted k-nearest neighbor imputation, random forest imputation, and multiple imputation, to handle the missing data in the Crested Ibis activity dataset. The method with the best imputation performance is selected to impute the missing values in the Crested Ibis activity data.
Based on the six imputation methods mentioned above, in this study, imputation of missing values in the Crested Ibis activity data was conducted using Rstudio 4.2.3 software. Figure 2 presents the visualized results of the missing value replacement for the Crested Ibis activity data (CAFL003) using these six imputation methods. The green dots represent actual values, while the red dots represent imputed values. Observing Figure 2b, it can be noted that the imputed values obtained from the clustering imputation method and random forest imputation method deviate significantly from the true values, indicating poor imputation performance. On the other hand, the imputed values obtained from linear interpolation and multiple imputation methods mostly fluctuate slightly around their true values, suggesting that these two imputation methods have better performance in imputing the Crested Ibis activity data. Table 3 provides the RMSE values and corresponding p-values of the t-test for these imputation methods. From the table, it can be seen that linear interpolation has the smallest RMSE value, and the p-values of the paired sample t-tests for linear interpolation are all greater than 0.05, indicating no significant difference between the imputed data and the true data at a significance level of 5%. Therefore, this study adopts the linear interpolation method to impute the activity data of Crested Ibis CAFL003. Similarly, the missing values in the activity data of other Crested Ibises were also processed accordingly, and subsequent analyses were conducted based on the processed data.
Finally, the data need to be processed by transforming the meteorological data into categorical variables using the factors described in Table 4.
2.4. Statistical Analysis
Variance analysis is a parametric statistical method used to investigate whether there are significant differences in the outcomes produced by one or more factors [12]. Before conducting variance analysis, it is necessary to perform normality tests and homogeneity of variance tests on the data of Crested Ibis activities. It can be observed that the p-values for the normality test of the activity data for these five Crested Ibises are all less than 0, indicating a violation of the assumption of normality. Additionally, the activity data corresponding to the meteorological factors for these five Crested Ibises did not pass the test for homogeneity of variances among groups, and the results are shown in Table 5. Since the assumption of ANOVA (analysis of variance) is that the data should satisfy both normality and homogeneity of variances, ANOVA is not suitable in this case.
If the overall distribution of the data is unknown, or known but fails the normality and homogeneity of variance tests, it is unreasonable to conduct variance analysis at this time. This can easily lead to incorrect results. Non-parametric testing methods can solve this problem [13]. In light of this, this paper uses non-parametric testing methods to explore the impact of meteorological factors on the activities of Crested Ibis and implements it using the scheirerRayHare function and kruskal.test function in Rstudio 4.2.3.
3. Result
When investigating the impact of four meteorological factors, namely rainfall, temperature, humidity, and wind speed, on Crested Ibis activities, it is essential to consider not only the individual effects of each meteorological factor on Crested Ibis activity distances but also the interactions between pairs of factors. Therefore, this study employs the Scheirer–Ray–Hare test to examine whether the interaction effects of the two meteorological factors significantly influence Crested Ibis activity distances. If the interaction effect between two factors is significant and may interfere with the comparison of levels of one factor, a simple effect analysis is conducted. This involves fixing the other factor at a specific level and then performing the Kruskal–Wallis test for that factor. If the interaction effect between the two factors is not significant, the Kruskal–Wallis test is directly applied to each factor separately.
The rainfall (pre), temperature (tem), humidity (hum), and wind speed (wind) factors were combined in pairs, resulting in a total of 6 combinations. Scheirer–Ray–Hare test was performed on each pair of factors, and the results are shown in Table 6.
According to Table 6, for the Crested Ibis CAFL003 and Crested Ibis 4B04A0, the p-values for the interaction effects between rainfall, temperature, humidity, and wind speed are all greater than 0.05. This indicates that at a significance level of 5%, the interaction effects between these factor combinations do not have a significant impact on the activity distance of the Crested Ibis.
For Crested Ibis CAFL004, the interaction effect between temperature and humidity has a significant impact on its activity distance, as well as the interaction effect between temperature and wind speed. The interaction effects of the other factor combinations are not significant.
For Crested Ibis CAFL017, the interaction effect between rainfall and temperature has a significant impact on its activity distance, as well as the interaction effect between temperature and humidity. The interaction effects of the other factor combinations are not significant.
For Crested Ibis CAFL013, the interaction effect between rainfall and wind speed has a significant impact on its activity distance, and the interaction effect between rainfall and humidity also has a significant impact. The interaction effects of the other factor combinations are not significant.
Next, we separately explore the effects of rainfall, temperature, and humidity on Crested Ibis activity. If the interaction effect of two meteorological factors is significant, simple effect analysis is conducted on the factor combinations that have interaction effects. If the interaction effect of the two factors is not significant, we directly conduct Kruskal–Wallis tests on the two factors separately. The test results are as follows:
According to the results in Table 7, the meteorological factor rainfall has a significant impact on the activity range of Crested Ibis CAFL003 and Crested Ibis CAFL04, and the activity level is lower on sunny days compared to rainy days. For Crested Ibis CAFL017, under low temperature conditions, rainfall significantly affects the activity distance, and the average activity distance on sunny days is 2978 m more than on rainy days. For Crested Ibis CAFL013, under high humidity and strong wind conditions, rainfall significantly affects the activity distance, and the average activity distance on sunny days is 1313 m more than on rainy days. However, there is no significant difference in the activity distance of Crested Ibis 4B04A0 due to the meteorological factor rainfall.
According to the results in Table 8, the meteorological factor temperature has a significant impact on the activity range of Crested Ibis CAFL003 and Crested Ibis 4B04A04, and the activity level is higher in high temperature compared to that in low temperature. For Crested Ibis CAFL004, under low humidity and strong wind conditions, high temperature significantly affects the activity distance, and the average activity distance in high temperature is 4578.38 m more than in low temperature. For Crested Ibis CAFL017, under low humidity and sunny weather conditions, high temperature significantly affects the activity distance, and the average activity distance in low temperature is 4666.138 m more than in high temperature. However, for Crested Ibis CAFL013, with unequal sample sizes, the p-value of the significance test is 0.05137, which is less than 0.05, indicating no significant difference in activity distance under high temperature at a significance level of 5%. But when 60 samples were randomly selected from all high-temperature samples, ensuring equal sample sizes for each level, it was found that in three random samples, two of them had a p-value less than 0.05 in the significance test, indicating a significant impact of high temperature on activity distance at a 5% significance level. Additionally, the activity level is lower in high temperature compared to low temperature. In conclusion, high temperature also has an impact on the activity distance of Crested Ibis CAFL013.
According to the results from Table 9, humidity, as a meteorological factor, has a significant impact on the activity range of Crested Ibis CAFL003 and Crested Ibis 4B04A04. The activity level is higher under low humidity compared to high humidity. For Crested Ibis CAFL004, under high temperature conditions, humidity significantly affects activity distance. The average activity distance is 3498.628 m higher under low humidity than under high humidity. For Crested Ibis CAFL017, under low-temperature conditions, humidity significantly affects activity distance. The average activity distance is 2785.593 m higher under low humidity than that under high humidity. In addition, under low temperature conditions, humidity also has a significant impact on the activity distance of Crested Ibis CAFL017. However, in this case, the average activity distance is 4619.563 m higher under high humidity than that under low humidity. For Crested Ibis CAFL013, under sunny weather conditions, humidity significantly affects the activity distance. The average activity distance is 2486.496 m higher under high humidity than under low humidity. In conclusion, humidity has an impact on the activity range for all five Crested Ibis individuals.
4. Discussion
The results of the non-parametric tests indicate significant differences in the activity levels of five Crested Ibises under high- and low-temperature conditions. This is because low temperatures can affect the metabolism and living state of the Crested Ibis. They usually adapt to the cold by changing their behavior, such as reducing the time allocated to behavioral activities to lower energy consumption, similar to most birds in cold environments [14]. Studies by Liu Li, Wang Zijian, and others [15] using the instant scan method have shown differences in the resting and foraging behaviors of the Crested Ibis during the overwintering and wandering periods, indicating that temperature has a certain impact on the behavior of the Crested Ibis, which is consistent with the findings of this study. Additionally, the study’s results show that humidity and rainfall significantly impact the activity of the Crested Ibis. Since their diet mainly consists of small fish, shrimp, loaches, beetles, and other small vertebrates and invertebrates, typically foraging in rice fields and muddy areas, they mainly inhabit wetland environments near marshes, rice fields, and rivers. Liu Jiajie [16] used the MaxEnt model to find that the least rainfall in the driest quarter and seasonal variations in precipitation are the dominant factors in habitat selection for the Crested Ibis. The spatial–temporal pattern simulation of suitable habitats for the Crested Ibis by Xia Zhuoyi and others [17] also concluded that annual average rainfall significantly affects habitat selection, indicating that areas with less rainfall and humidity are not the preferred choice for the Crested Ibis’s habitats and foraging grounds. Therefore, rainfall and humidity do influence their foraging activities. Thus, the results of the non-parametric tests are valid, showing that meteorological factors significantly impact the activities of the Crested Ibis.
At the same time, the results of the non-parametric tests show that meteorological factors have varying degrees of impact on the activity distance of individual Crested Ibises. Crested Ibises CAFL003, 4B04A0, and CAFL004 are relatively more active in high temperatures and rainy weather, whereas CAFL017 and CAFL013 are more active in low temperatures and sunny weather. As shown in Figure 3, the activity trends of CAFL003 and 4B04A0 in Tongchuan City are roughly the same, mainly active in spring and summer, with the highest activity in May and June. The three Crested Ibises in Yang County show significant individual differences in their activity trajectories, with CAFL004 being more active in spring and less so in winter, while CAFL017 and CAFL013 are more active in autumn and winter and less in spring and summer. Li Xinhai and others [18] used univariate analysis of variance to study the activity levels of six Crested Ibis populations in two periods, finding significant differences in activity between the six populations in autumn and winter. Ducan pairwise comparisons found that most populations were significantly less active in winter than in summer, with only one population being more active in winter, similar to the findings of this study. The reason mainly lies in the fact that the behavior of the Crested Ibis is not only influenced by the climate, but also related to factors such as the abundance of food in their habitat, activity space, human activities, and their physiological conditions. Moreover, the temperature range that the same species can tolerate at different developmental stages varies [16], so it is reasonable that meteorological factors have individual differences in their impact on the activities of the Crested Ibis.
This paper verifies that high temperature, humidity, and rainfall are the main meteorological factors affecting the activity of the Crested Ibis based on non-parametric testing methods. By exploring the climatic characteristics of typical habitats of the Crested Ibis, it provides reference for the selection of suitable habitats for the Crested Ibis in China and scientific basis for the reintroduction of the Crested Ibis, as well as theoretical foundations for the habitat research of other birds (such as the tea eagle, tea eagle, etc.). However, the environmental factors affecting the movement trajectory of the Crested Ibis include not only climate factors such as temperature and relative humidity, but also other factors such as altitude, roads, topography, and vegetation coverage [19,20]. Therefore, in future studies on the factors affecting the activity of the Crested Ibis, it is possible to consider climate factors, topography, and human activities comprehensively to provide greater reference value for the selection of suitable habitats for the Crested Ibis.
Conceptualization, F.L. and X.J.; methodology, X.L. and F.L.; validation, D.L. and L.G.; writing—review and editing, F.L. and X.L.; visualization, L.G. and X.L.; funding acquisition, X.J. All authors have read and agreed to the published version of the manuscript.
All data collected as part of this study were approved by the National Bird Banding Center of China. Field work was approved by the State Forestry Administration.
Not applicable.
These data are available upon request from the corresponding author.
The fieldwork was supported by the Shaanxi Hanzhong Crested Ibis National Nature Reserve.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Visualization of missing value replacement using six imputation methods.
Enlarge this image.Figure 3. The distribution maps of Crested Ibis activities in Tongchuan City and Yang County.
Example of Crested Ibis’s trajectory data.
| Terminal | 22: CAFL003 | 22: CAFL003 | 22: CAFL003 |
|---|---|---|---|
| Time | 31 March 2020 6:00 | 31 March 2020 8:00 | 31 March 2020 10:00 |
| Longitude | E108.82488 | E108.82478 | E108.82490 |
| Latitude | N35.04734 | N35.04731 | N35.04737 |
| Speed | 0 | 0 | 0 |
| Altitude | 183.3 | 849.1 | 178.5 |
| Heading direction | 9.5 | 163.9 | 0 |
| Accuracy | A | A | B |
The Basic Situation of the Five Crested Ibises.
| ID | Sex | Birth Year | Body Mass (g) | Transmitter Weight (g) | Transmitter Weight/Body Mass | Duration |
|---|---|---|---|---|---|---|
| 4B04A0 | Female | 2012 | 1347 | 25 | 1.9% | 20140716~ |
| CAFL003 | Male | 2013 | 1930 | 25 | 1.3% | 20150210~ |
| CAFL004 | Male | 2015 | 1518 | 25 | 1.6% | 20150722~ |
| CAFL013 | Male | 2017 | 1290 | 25 | 1.9% | 20170608~ |
| CAFL017 | Female | 2017 | 1223 | 25 | 2.0% | 20170608~ |
Accuracy values of the seven imputation methods.
| Methods | RMSE | p-Value |
|---|---|---|
| Mean imputation | 961.4696 | 9.90 × 10−6 |
| Median imputation | 867.4984 | 0.4788 |
| Linear interpolation | 203.2544 | 0.8695 |
| Cluster imputation | 2727.564 | 2.20 × 10−16 |
| Weighted k-nearest neighbor imputation | 1219.352 | 0.0103 |
| Multiple imputation | 977.318 | 0.3490565 |
| Random forest imputation | 1839.528 | 0.05504 |
Data Processing.
| Factor | Levels | Conditions |
|---|---|---|
| pre | Rainy day | Greater than 0 |
| Sunny day | Equal to 0 | |
| tem | High temperature | Greater than or equal to 25 |
| Low temperature | Less than or equal to 5 | |
| hum | High humidity | Greater than or equal to the upper quartile |
| Low humidity | Less than or equal to the lower quartile | |
| wind | Strong wind | Greater than or equal to the upper quartile |
| Gentle breeze | Less than or equal to the lower quartile |
Normality test and homogeneity of variance test.
| Crested Ibis Identification Number | p-Value of the Normality Test | p-Value of the Homogeneity of Variance Test | |||
|---|---|---|---|---|---|
| Pre | Tem | Hum | Wind | ||
| CAFL003 | 0.0000 | 0.8282 | 0.0091 | 0.3631 | 0.0000 |
| 4B04A0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.4326 |
| CAFL004 | 0.0000 | 0.8805 | 0.0000 | 0.03241 | 0.6233 |
| CAFL017 | 0.0000 | 0.01009 | 0.2797 | 0.1565 | 0.1446 |
| CAFL013 | 0.0000 | 0.9215 | 0.9444 | 0.01867 | 0.4454 |
Scheirer–Ray–Hare test Analysis of Variance table.
| Two Factors | Sources of Variance | Significance p-Value | ||||
|---|---|---|---|---|---|---|
| CAFL003_p | 4B04A0_p | CAFL004_p | CAFL017_p | CAFL013_p | ||
| Rainfall and temperature | Rainfall | 0.1752 | 0.5566 | 0.9849 | 0.3657 | Number of cases with zero occurrences for factor combination |
| Temperature | 0.0000 | 0.0000 | 0.0000 | 0.0000 | ||
| Interaction effect | 0.2017 | 0.3819 | 0.1970 | 0.0042 | ||
| Rainfall and wind speed | Rainfall | 0.6979 | 0.1197 | 0.0000 | 0.0045 | 0.1102 |
| Wind speed | 0.0327 | 0.0395 | 0.0649 | 0.7976 | 0.6181 | |
| Interaction effect | 0.5250 | 0.4469 | 0.6294 | 0.5262 | 0.0149 | |
| Rainfall and humidity | Rainfall | 0.0012 | 0.0028 | 0.0000 | 0.0000 | 0.1194 |
| Humidity | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0001 | |
| Interaction effect | 0.2380 | 0.1842 | 0.5286 | 0.2237 | 0.0128 | |
| Temperature and humidity | Temperature | 0.0000 | 0.0000 | 0.0000 | 0.0002 | 0.2010 |
| Humidity | 0.0506 | 0.6685 | 0.4936 | 0.5464 | 0.3920 | |
| Interaction effect | 0.4184 | 0.0562 | 0.0013 | 0.0001 | 0.2687 | |
| Temperature and wind speed | Temperature | 0.0000 | 0.0078 | 0.0000 | 0.0000 | 0.0941 |
| Wind speed | 0.1548 | 0.1721 | 0.1288 | 0.0439 | 0.2820 | |
| Interaction effect | 0.3416 | 0.5875 | 0.0060 | 0.2528 | 0.8451 | |
| Humidity and wind speed | Temperature | 0.0008 | 0.0000 | 0.5025 | 0.4746 | 0.0904 |
| Wind speed | 0.3876 | 0.3287 | 0.1477 | 0.7207 | 0.6006 | |
| Interaction effect | 0.2568 | 0.0810 | 0.8886 | 0.4725 | 0.3509 | |
The effect of rainfall on Crested Ibis activity.
| Crested Ibis ID | Number of Samples in Sunny Weather | Number of Samples in Rainy Weather | Significance p-Value | Difference | |
|---|---|---|---|---|---|
| Sunny Weather–Rainy Weather | |||||
| CAFL003 | 1466 | 473 | 0.0295 | −119.8355 | |
| 473 (Random sampling) | 473 | 0.0017 | −254.6487 | ||
| 473 (Random sampling) | 473 | 0.0196 | −227.2629 | ||
| 473 (Random sampling) | 473 | 0.013 | −226.3191 | ||
| 4B04A0 | 1398 | 414 | 0.2061 | —— | |
| 414 (Random sampling) | 414 | 0.2453 | |||
| 414 (Random sampling) | 414 | 0.5741 | |||
| 414 (Random sampling) | 414 | 0.2638 | |||
| CAFL004 | 1301 | 475 | 0 | −878.5491 | |
| 475 (Random sampling) | 475 | 0.0007 | −834.4071 | ||
| 475 (Random sampling) | 475 | 0.0059 | −490.2824 | ||
| 475 (Random sampling) | 475 | 0 | −942.3337 | ||
| CAFL017 | Fixed high temperature | 228 | 75 | 0.0523 | —— |
| Fixed low temperature | 144 | 12 | 0.002 | 2978.734 | |
| CAFL013 | Fixed low humidity and strong wind | 32 | 5 | 0.0756 | —— |
| Fixed high humidity and strong wind | 43 | 33 | 0.021 | 1312.816 | |
| Fixed high humidity and gentle wind | 20 | 21 | 0.1238 | —— | |
| Fixed low humidity and gentle wind | Less than 2 cases in the combination level. | ||||
Impact of high temperature on Crested Ibis activity.
| Crested Ibis ID | Number of Samples in Low Temperature | Number of Samples in High Temperature | Significance p-Value | Difference | |
|---|---|---|---|---|---|
| Sunny Weather–Rainy Weather | |||||
| CAFL003 | 515 | 209 | 0.0000 | 498.1974 | |
| 209 (Random sampling) | 209 | 0.0000 | 514.8805 | ||
| 209 (Random sampling) | 209 | 0.0000 | 513.9448 | ||
| 209 (Random sampling) | 209 | 0.0000 | 558.8142 | ||
| 4B04A0 | 518 | 184 | 0.00002 | 1112.882 | |
| 184 (Random sampling) | 184 | 0.00419 | 1029.529 | ||
| 184 (Random sampling) | 184 | 0.0019 | 1070.9 | ||
| 184 (Random sampling) | 184 | 0.0008 | 1067.867 | ||
| CAFL004 | Fixed low humidity and strong wind | 12 | 84 | 0.0000 | 4578.38 |
| Fixed low humidity and gentle wind | 5 | 16 | 0.05754 | —— | |
| Fixed high humidity and gentle wind | 35 | 10 | 0.6558 | —— | |
| Fixed high humidity and strong wind | Number of cases for the level combination is 0 | ||||
| CAFL017 | Fixed low humidity and sunny weather | 24 | 105 | 0.0000 | −4666.138 |
| Fixed high humidity and sunny weather | 29 | 2 | 0.8094 | —— | |
| Fixed high humidity and rainy weather, | 7 | 11 | 0.2576 | —— | |
| Fixed low humidity and rainy weather | Number of cases for the level combination is 0 | ||||
| CAFL013 | 60 | 75 | 0.05137 | —— | |
| 60 | 60(Random sampling) | 0.04553 | −1075.104 | ||
| 60 | 60(Random sampling) | 0.03266 | −1104.173 | ||
| 60 | 60(Random sampling) | 0.07693 | —— | ||
Impact of humidity on Crested Ibis activity.
| Crested Ibis ID | Number of Samples in High Humidity | Number of Samples in Low Humidity | Significance p-Value | Difference | |
|---|---|---|---|---|---|
| High Humidity–Low Humidity | |||||
| CAFL003 | 512 | 490 | 0.00047 | −213.2576 | |
| 490 (Random sampling) | 490 | 0.00136 | −193.8026 | ||
| 490 (Random sampling) | 490 | 0.00045 | −213.8217 | ||
| 490 (Random sampling) | 490 | 0.00069 | −202.7011 | ||
| 4B04A0 | 466 | 454 | 0.00000 | −1135.057 | |
| 454 (Random sampling) | 454 | 0.00000 | −1135.37 | ||
| 454 (Random sampling) | 454 | 0.00000 | −1184.897 | ||
| 454 (Random sampling) | 454 | 0 | −1110.658 | ||
| CAFL004 | Fixed high temperature | 12 | 181 | 0.0017 | −3498.628 |
| Fixed low temperature | 72 | 38 | 0.1413 | —— | |
| CAFL017 | Fixed high temperature | 36 | 24 | 0.0027 | −2785.593 |
| Fixed low temperature | 13 | 126 | 0.0016 | 4619.563 | |
| CAFL013 | Fixed sunny weather | 45 | 80 | 0.0000 | 2486.496 |
| Fixed rainy weather | 41 | 8 | 0.7868 | —— | |
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