- Open Access
Evolution of seasonal land surface temperature trend in pond-breeding newt (Neurergus derjugini) in western Iran and eastern Iraq
Ecological Processes volume 12, Article number: 14 (2023)
Temperature, as one of the effective environmental stimuli in many aspects of species life and ecosystems, can affect amphibians in many ways. Knowing and predicting temperature change and its possible effects on the habitat suitability and movements of amphibians have led many researchers to use climate change scenarios and species distribution models (SDMs). One of the important remote-sensing products that received less attention of conservation biologists is the land surface temperature (LST). Due to the small difference between LST and air temperature, this component can be used to investigate and monitor the daily and seasonal changes of habitats. This study aims to investigate the seasonal trend of LST in the habitat suitability and connectivity of the critically endangered newt (Neurergus derjugini) in its small distribution range, using the MODIS LST time series (2003 to 2021) and with the help of SDMs, Mann–Kendall (MK) and Pettitt non-parametric tests.
In the last decade, the increasing trend of LST versus its decreasing trends is obvious. Based on MK and Pettitt tests, in the winter and spring, with the decrease in latitude of 35.45° and increase in longitude of 46.14°, the core populations which are located in the southeast have experienced an increase in temperature. Considering the period time of breeding and overwintering, the continuity of winter and spring can be effective on the survival of adult newts as well as larvae in the microclimate. Linkages with the highest current flow between core populations in the winter and summer are the most likely to be vulnerable. At the level of habitat, the increase in LST is proportional to the trend of thermal landscape changes, and all seasons have had an increase in LST, but in winter and summer, the largest area of the habitat has been involved. By continuing the current trend, many high-altitude southern habitats in Iran will be endangered, and the species will be at risk of local extinction.
The increasing trend of temperature in all seasons such as winter will affect many adaptations of the species and these effects are mostly evident in the southern parts of its distribution range therefore, captive breeding and reintroduction are recommended for the populations of these areas.
Temperature and its changes are one of the current climate challenges. Underestanding the effects of the environment on species and communities is a topic of utmost importance in ecology (Esparza-Orozco et al. 2020). Rising temperature acts as a stimulus for the decline, loss, and fragmentation of wildlife habitats. In dealing with rapid climate change, ecologists are increasingly challenged to understand the severity and frequency of weather events (Osland et al. 2021). Estimates show that arid and semi-arid regions in middle latitudes in North Africa, Middle East, and Central Asia will undergo the greatest impact in terms of intensity and fluctuation of climatic parameters (Li et al. 2013).
Amphibian physiology and also life history makes them sensitive to climate parameters (Stuart et al. 2004) and habitat loss (Sauer et al.2022). According to the IUCN, more than 40% of amphibians are at risk of extinction, and 128 species are listed in the "possibly extinct" category (IUCN 2017). Due to being biphasic, many amphibians need aquatic (for breeding and growth) and terrestrial (for feeding) habitats to complete their life cycle. They exchange water, electrolytes, and other environmental compounds through their moist skin (Hillman et al. 1992). Ample empirical evidence suggests that high temperatures lead to a male-biased sex ratio, while growth at low temperatures may increase the number of females after metamorphosis (Ruiz-García et al. 2021). Rising temperature can affect amphibian growth and size and leads to larger individuals feeding on smaller ones (Polis 1981).
Understanding how climate change affects biogeographic patterns helps to predict the possible effects on ecological goods and services resulting from changes in functional and phylogenetic diversity and evolutionary processes (Lourenço-de-Moraes et al. 2019). Today, SDMs, which are increasingly used to manage wildlife species, natural habitats, and landscapes for different purposes, are one of the most common tools for assessing and quantifying changes in species distribution (Bellard et al. 2012). Habitat connectivity is an essential feature of the landscape (Taylor et al. 1993), which, if lost, could endanger biodiversity. The main threat to amphibians that have recently undergone a significant decline worldwide is connectivity changes in their habitats (Stuart et al. 2004; Cushman 2006). Preserving and restoring landscape connectivity is a top priority for wildlife protection and an adaptation strategy to protect biodiversity against climate change (Heller and Zavaleta 2009; Lawler 2009). In their corridors, amphibians are dependent on the contexts that contain components such as moisture and water in their structure. Therefore, any change in temperature trend can be considered as a threat to connectivity of species communication.
Nowadays analyzing time-series data is a powerful way to conduct impact assessment in ecology (Wauchope et al. 2021). Time-series analysis describes the dynamic behavior of observations and links them to provide clues about their origin (Lange 2001). The data that can be processed in time series make it possible to analyze the trend and direction of spatial changes, overcome the limitations of information retrieval, and assess the relationship between species and the periodic fluctuations from time point of view (i.e., seasonally, monthly, and annually) and from spatial point of view (i.e., in habitats, home range). In general, time-series analysis includes the investigation of trends and seasonality in the data (Dimri et al. 2020).
Seasonality is a prevalent environmental particular in diverse ecological systems that is driven by periodic climatic conditions (Holt 2008). Although ecologists consider the seasonality of changes very important, in many cases the seasonality of phenomena is ignored. The use of seasonal data faces two problems, one is that the data must be collected throughout the year and for several years (Power et al. 2008) and the other one is that some advanced mathematical relationships are needed to analyze these complexities (White and Hastings 2020). Contrary to the difficulties in seasonal analysis, many ecological questions are answered in the context of seasonality (White and Hastings 2020). But there are space–time limitations for using time-series variables that are recorded as points. Point observations need interpolation and many interpolation methods are useful for gentle terrains (Ossa-Moreno et al. 2019), but it is difficult to measure temperature in high-elevation regions due to the high ruggedness. Many meteorological stations that collect the weather parameters as points are located within valleys, which increases the risk of using obtained data from these areas (Liu and Yan 2017). Moreover, the efficiency of interpolation methods remains a challenge (Steinacker et al. 2006).
The need for real-time access to spatial information and also new meteorological data have increased scientific interest in finding new satellite approaches to fill existing climate gaps (Espín Sánchez et al. 2022). Using big data in the form of time-series with remote-sensing is also possible (Wauchope et al. 2021). Satellite data, in theory, can overcome the information gap in highlands. Rapid data collection, frequent and intermittent data capture, and broad coverage make it possible to extract much information from satellite images, including LST which is the earth's surface temperature (Chehbouni et al. 1996), measured at 1.5 m above the ground level by radiation-shielded ventilated sensors (Mildrexler et al. 2011). LST is a key dynamic surface parameter that helps to grow the knowelege about energy balance, impact on regional climate, surface/ground water, vegetation phenology, human health, location of hotspots, land use/land cover features (Rani and Mal 2022), and estimation of some atmospheric parameters (Hereher 2019; Prakash et al. 2019; Singh et al. 2020) in mountainous regions where we face data shortage (Kuenzer and Dech 2013). In summary, LST is a critical variable for retrieving main climate parameters (e.g., air temperature and tropospheric vapor) (Hulley et al. 2019).
The yellow-spotted mountain newt (Neurergus derjugini) also known as Kurdistan newt is critically endangered based on IUCN red list. The presence of this species has so far been confirmed in western Iran and eastern Iraq. Their life cycle includes migrations that occur seasonally between aquatic and terrestrial habitats. For many newts, migration between spring and autumn habitats for reproduction and hibernation is an essential cycle of vital activities (Dervo et al. 2016). Many studies on the impact of climate change on the Kurdistan newt (Khwarahm et al. 2021; Malekoutian et al. 2021; Karamiani 2021) and habitat modeling are available (Vaissi 2021; Barabanov and Litvinchuk 2015). However, no study has so far focused on quantifying the possible effects of seasonality trend of LST on the suitable habitat and connectivity of N. derjugini core populations, therefore this study aims to use LST time series to determine the temporal–spatial effectiveness of the habitat and linkages between the core populations by different seasons of the year and identify vulnerable populations in relation to the increase in temperature.
Materials and methods
Study area and localities
The boundary of this study with an area equal to 97,233.76 km2 is jointly located between Iran and Iraq. The minimum and maximum height in this area is 12 and 3580 m a.s.l., respectively. In order to collect presence points, during the years 2019–2021, field visits to the species habitats in Kermanshah, Kurdistan and west Azerbaijan provinces in the west of Iran were made in April, May, June and July. Most of the presence points were inside canals, streams and springs. Presence points in Iraq were collected using previously conducted studies (Afroosheh et al. 2016). In addition to these visits, the present data from the Department of Environment of Kermanshah and Kurdistan Provinces of Iran were also used. A total of 72 locations, which almost covered most of the habitats of the species in Iran and Iraq, were collected (Fig. 1). In terms of altitude, the distribution range of the species can be divided into two parts: cold and humid in the heights of Zagros and hot and dry at low altitudes shared by Iran and Iraq (Sharifi and Vaissi 2014). The Zagros Mountains in the west of the Iranian plateau, including the vast mountain ranges, after various geological periods, glacial eras, and profound climate change, now have rich fauna and flora. Many of its species are exclusive to Iran, and some are found only in this mountain range (Ghahremaninejad et al. 2021). Since basins with two factors of water and altitude can play a decisive role in forming movement contexts for amphibians and thus their connectivity, this study used basins to analyze habitat conditions and linkages within localities. The basins were analyzed using the Digital Elevation Model (DEM) and a set of commands in the Hydrological toolbox of the Spatial Analysis in ArcGIS 10.4.1 (Fig. 1).
Remote-sensing offers valuable tools and relevant information for studying biodiversity and the environment associated with biological communities (Esparza-Orozco et al. 2020). Considering the data shortage due to inadequate and uneven distributions, MYD11A1 (Daily LST from Aqua) was used with a spatial resolution of 1 km in the time period of 2003–2021. Due to its daily global coverage and high temporal frequency, MODIS LST is widely used for local and global studies (Bai et al. 2019; Minacapilli et al. 2016). Since LST (MODIS) is calculated only based on clear sky observations, so its bias is toward cloudless days (Benz et al. 2017). To apply the bias correction, the average (Benz et al. 2017) quarterly LST was calculated in the Google Earth Engine (GEE) with a scale factor of 0.02 in Kelvin (Gorelick et al. 2017). The use of mean data is also recommended to keep the temporal resolution (Zhao et al. 2022) and abnormal weather (Morovati et al. 2020) consistent with the habitat condition and therefore, for each season, LST was calculated as the average daily LST of that season.
The species' distribution was determined using bioclimatic, topographic, anthropogenic, vegetation index, and landform variables. The bioclimatic variables were prepared with a spatial resolution of 30 s from WorldClim (Version 2). DEM was also prepared with the same spatial resolution from the mentioned database. The vegetation index was prepared in GEE from NDVI on an annual average (2021 to 2022) using the 16-day MODIS product "MODIS/006/MOD13Q1". Due to the presence of the species at high elevation, the density of mountain tops was used. A 10-class landform variable was first prepared from DEM in SAGA-GIS. Then, using the reclassify, all classes except mountain tops (coded 1) were given a zero code. The density map was then calculated using the FocalStatistics in ArcGIS 10.4.1. Distance from sparse vegetation (tree, shrub, and herbaceous cover) and shrubland were prepared from the land use/land cover map at the Copernicus Climate Change Service database. The soil organic carbon was used to apply soil quality in the model. The soil organic carbon map at 0–30 cm depth with ton ha-1 unit of measurement was downloaded from FAO to a spatial resolution of 1 km (http://220.127.116.11/GSOCmap/). The human modification was used to assess the impact of human communities on the species' habitats, which can be downloaded under the title "Global Human Modification of Terrestrial Systems" at https://earthdata.nasa.gov. All variables with a spatial resolution of 1 km were entered into the modeling. A correlation test was run within all variables, and those variables with a correlation coefficient greater than 0.80 were excluded from the modeling process. In order to reduce the spatial autocorrelation, the spatial resolution of 1 km for habitat variables was considered, and all presence points with a less distance were not included in the modeling process. This was done using SDM Toolbox v2.5 (Brown et al. 2017).
The species distribution modeling was performed with the integrated approach of profile and presence/pseudo-absence models. Presence-only or profile models include One-Class SVM, Domain, and Bioclim. Since pseudo-absence is required to run the presence/pseudo-absence models, this study used the output of presence-only models to create pseudo-absence points (Piri Sahragard et al. 2021). After validation by Area Under the Curve (AUC), the outputs of the profile models were combined to prepare the presence-only ensemble binary model. The suitable habitat (Class 1) was excluded, and in the remaining unsuitable habitat (code 0), pseudo-absence points were created with the same number, 3 times and 5 times the number of presence points (Liu et al. 2019) as well as 1000 random points in QGIS 3.16.3. Then, using the presence and pseudo-absence points, the presence/pseudo-absence models, including BP-ANN, MaxEnt, MaxLik, CART, Rough Set, and Two-class SVM, were run. As the models used in this study are of classifier type, the criteria related to the classifier models including sensitivity, specificity, overall accuracy and Kappa index, were used to validate the models (Piri Sahragard et al. 2021; Karami and Tavakoli 2022). After validation, all the mentioned models were combined to prepare the ensemble presence/pseudo-absence model. Finally, the presence-only and the presence/pseudo-absence ensemble models were combined, and the final ensemble model (probability) was obtained. In all used models, 70% of the data were allocated for training and 30% for testing. All models were implemented in ModEco (Guo and Liu 2010). Then, to identify the affectability range of the suitable habitats from the LST trend and mean LST at the habitat level, the lowest presence threshold (LPT) was applied on the final ensemble model.
Integration of electrical circuit theory and least cost path (LCP) models in the Linkage Mapper toolbox was used to investigate the species' connectivity. In the electrical circuit theory, electrical nodes are the same as cells in a landscape connected to the neighboring cells by resistors. Resistance values are determined by the resistance/conductivity values of the cells in the landscape. The presence points were introduced as the nodes (or cores), and the inverse of the probability distribution map was used as the resistance for the connection (Ahmadi et al. 2017). After importing the input files (core habitat and resistance), the Linkage Mapper was run to prepare Least Cost Corridors (LCCs) and LCPs between each pair of core populations. By detecting neighboring core habitats based on distance and adjacency data, the Linkage Mapper establishes a network of core habitats and produces LCCs by calculating cost-weighted distance (CWD) and LCP. It also integrates the single corridors to obtain the normalized composite map of corridors (Dutta et al. 2016). Various metrics are determined by models to judge the linkage quality, among which this study used Current_Flow_Centrality. The metric helps estimate the importance of a connection in preserving the connectivity in the entire landscape (McRae 2012), which measures the current flowing between the corridors in amperes. Linkages that have a high current facilitate the connection path between the nodes.
MK is a non-linear trend test to detect monotonous up- and downtrends over a specific period and check the similarity between two ranking sets assigned by the same dataset (Neeti and Eastman 2011). This study ran the MK test considering two aspects to evaluate the impact of LST on habitat suitability/connectivity and also detect the changing trend of LST with decreasing latitude in the species' areas of presence. In the first analysis, seasonal LSTs were entered into TerrSet software from 2003 to 2021, then grouped into raster time series, and the MK test was performed by seasons. The primary assumption of trend analysis using the MK test is the lack of correlation between input data; therefore, prewhitening is used to eliminate the influence of serial correlation on the Mann–Kendall test of trends. The standardized MK test statistic (Zmk) test was used to determine the significant levels of the changes. The positive Zmk value shows an uptrend and vice versa. Zmk fluctuations were reported by Kandya et al. (2021). To better evaluate and judge the decreasing and increasing trends, significance levels of 0.01 and 0.05 were considered. After identifying the level of changes by crossing these findings with the habitat suitability (derived from LPT), the areas that underwent the increasing/decreasing trends were estimated. The second MK test was performed to assess the changing trend of LST that may occur with decreasing latitude. First, the presence points were sorted in descending order of latitude and LST values were assigned to localities for each year using the Extract Values to Points command in ArcGIS. Finally, using MK and Sen's slope test, the trend in the data was analyzed separately in 76 runs at a significance level of 95%. It is should be noted that in each run Hamed and Rao's (1988) test was performed in XLSTAT for the autocorrelation analysis.
One of the objectives of this study is to identify the affectability threshold of the species' presence areas against the LST trends. After calculating the MK test at the level of localities, it is necessary to determine the breakpoint in the LST value occurred at the location of each site from which, the LST changes have become "increasing" or "decreasing". Therefore, the Pettitt test which is non-parametric test for determining break points introduced in 1979 was used to detect homogeneity and break point in the LST data. It divides a time series into two separate homogeneous groups and examines the significance of this separation. The Pettitt test is widely used in climate and hydrological studies (Mezger et al. 2022). In this test, the H0 hypothesis refers to the homogeneity of data, and the H1 hypothesis shows the presence of an ascending/descending trend, which its selection was subject to the implementation of the MK test at the level of the locality. After performing the MK test and determining the trend in XLSTAT, the H1 hypothesis in the Pettitt test was adopted to check any increasing or decreasing trend. If a breakpoint is observed, the localities will be divided into two groups by the Minimum Bounding Geometry in ArcGIS 10.4.1.
Table 1 lists the validation of each model. Most of the used models have good validity. Kappa index for MaxLik and CART models was higher than other models; however, the sensitivity value for the Rough set model was calculated to be 69%, which is low compared to the other models used in this study. Overall, the models had good accuracy, and all were used to create the ensemble model.
Figure 2 depicts the newts' habitat suitability for the presence-only and presence/pseudo-absence SDMs. There are differences between the models in identifying the suitable habitat of the species. One-class SVM, two-class SVM, and ANN models predicted the southern parts of the study area as suitable. The suitable habitat is shared between Iran and Iraq; and it seems that compared to the modeling boundary, the majority of the area of suitable habitats belongs to Iran. The suitable habitat of the Kurdistan newt has a small area and is confined to the highlands with a mountainous landscape covered by oak forests.
Figure 3 shows the average LST in the suitable habitat from 2003 to 2021. The mean LST in the suitable habitat is the highest in summer. Winter has the lowest average LST with an evident increasing trend of changes. This upward trend is also observed for spring. Spring and autumn are most similar in terms of average LST in 2006, 2008, 2014, 2015, and 2016. The average LST in the suitable habitat increased from 318.09° K in the summer of 2003 to 321.18° K in 2021. From 2003 to 2021, the average LST in the suitable habitat changed from 276.87° K to 284.28° K and from 298.84° K to 303.66° K for the winter and spring, respectively.
Figure 4 shows the area of suitable habitat underwent increasing and decreasing trends at the significance levels of 99% and 95%. The comparison of areas shows that the area of the increasing trend is much larger than the decreasing trend. Also, the seasonal pattern of increasing and decreasing LST trends is different. No decreasing trend of LST was observed in the suitable habitat in winter. In all seasons except winter, the habitat experienced both increasing and decreasing trends in LST, even to a small extent. In decreasing trends at the significance levels of 95% and 99%, summer and autumn have the largest decrease in LST with areas of 36.50, 47.44, 42.57, and 52.56 km2, respectively. The spring season has the lowest decreasing trend of LST at the 99% significance level with an area of 2.84 km2. In increasing trends, winter and summer have the highest increase in LST at the significance levels of 95% and 99%.
Trend analysis based on latitude
Table 2 shows the results of the MK test at the level of sites. In the spring of 2011, 2015, 2018, 2020, and 2021, the increasing trend of LST showed a significant relationship with increasing latitude. However, according to Sen's slope, the intensity of these changes was higher in 2015 than the previous years. In summer and autumn, no significant trend was observed with decreasing latitude. In winter, the results showed that the changes in LST were increasing, and Sen's slope also confirmed that the increase in LST in this season occurred more strongly in the areas of the species' presence points.
Sen's slope: this slope corresponds to the median of all the slopes calculated between each pair of points in the series. Kendall's tau measures the monotony of the slope. Kendall's tau varies between − 1 and 1; it is positive when the trend increases and negative when the trend decreases.
Among the years, 2020 was selected as an example to plot LST changes based on latitude and longitude. Figure 5 shows the results of LST distribution in spring and winter, respectively. As it turns out, LST is lower in high latitudes and eastern latitudes. As latitude decreases and longitude increases, LST will increase in both seasons.
After the trend analysis test and Sen's slope (Table 2), based on the presence of an increasing trend in the data, the H1 was considered, and the Pettitt test was performed. The results show a significant jump in the years mentioned in the table 2 (P-value < 0.05). This breakpoint is shown in Fig. 6, taking into account the spatial position of the localities separately for the significant years in Table 2. The observations were grouped based on the results of the Pettitt test. Considering the breakpoint, the groups are displayed in blue and yellow. According to the findings, a large part of the separations occurred at the latitude 35.45° and the longitude 46.14°.
Effects on linkages
The electrical circuit theory established 192 linkages between the localities with minimum 900 m and maximum 125,073 m distance. At the significance level of 99%, the corridors in the southern parts were surrounded by incremental LST changes in both summer and winter (Fig. 7). At a significance level of 95%, these two seasons also had the highest impact on the linkages. Compared to the upward trend, the downward trend in LST did not occur within the linkages (Fig. 8).
To predict the possible effects of temperature changes on distribution range and habitat suitability, researchers usually use databases such as WorldClim and CHELSA besides SDMs, but using data from different climatic databases in SDMs leads to differences in results (Datta et al. 2020) and their efficiency. In a study by Bobrowski et al. (2021) comparing the two databases, it was concluded that the CHELSA database performed better than the WorldClim database. These differences can occur, especially in areas with high orographic heterogeneity that are also highly sensitive to the prediction errors of plant species (Hanspach et al. 2011). This study is the first research on trends of LST in the distribution ranges and Linkages of newts. It was conducted to detect the trend of seasonal LST changes in the habitat of yellow-spotted mountain newt over the period of 2003 to 2021. LST was calculated using the MODIS product, the efficiency of which has already been confirmed for different purposes (Ermida et al. 2014; Guillevic et al. 2014). LST is different from air temperature and is strongly influenced by land covers such as forests, ice cover, lakes, and bare soil. Air temperature is more than a regional average and shows lower spatial and temporal gradients than LST. However, studies show that the trend of LST changes is similar to the air temperature changes (Li et al. 2016). In some cases, its difference with the air temperature is ± 1 K (Duan et al. 2018; Wan 2014). Therefore, despite some weaknesses, it can provide credible results. The use of cloud computing platform such as GEE has made it possible to process large data on different types of satellites at different time-spatial scales, therefore the use of this system and some prepared products such as LST in combination with the findings of SDMs can be useful for the conservation of endangered species.
At a significance level of 95%, an increasing trend of LST was observed in all seasons during the study period. Most of the incremental changes were in summer and winter. In a study by Jamali et al. (2022) on the relationship between LST and environmental variables in Iran, it was reported that most changes occurred in summer and winter. In summer, there has been an increasing trend in the northern and eastern parts of the suitable habitat. In winter, these changes have been in the southern and middle parts of the suitable habitat in Iran (within Kermanshah Province). As the current trend continues, the south and central parts of the distribution range (Fig. 2) will be highly affected and more threatened by the rising LST trends. However, in a study by Vaissi (2021) these areas were reported as habitats with constant suitability under different climate change scenarios, which contradicts this study's findings, showing the species' current distribution range will be affected by LST changes. Some studies suggest that the movement of amphibians to heights can be a solution to cope with rising temperatures (Barrett et al. 2014; Cemal Varol et al. 2016). For example, in a study by Shin et al. (2021) on Onychodactylus koreanus, the results showed that due to rising temperatures under climate change scenarios, the species' distribution range would tend to higher latitudes and altitudes. A study by Lourenço-de-Moraes et al. (2019) on amphibians' climate refuges yielded similar results. However, the upward trend of LST changes (at 99% and 95% significance levels) has occurred in many southern parts of the current distribution range (e.g., Shahuo, Bouzin and Markheil heights), which are also elevated. As the current trend continues, the effectiveness of this strategy will be challenged in these regions. The increasing LST trend in winter (at 95% significance level) involved suitable habitat areas with a minimum altitude of 470 m to a maximum height of 3439 m, which indicates that high altitudes in the region are not immune to rising temperatures. Elevated temperatures were also reported in a study by Knapp et al. (2011) as a barrier to creating suitable conditions for Batrachochytrium dendrobatidis. Notably, no decreasing trend of LST was observed in winter (Fig. 4), and LST in the suitable habitat was unchanged or increased in winter. It is supposed the daily temperature rise in winter affects the rainfall and snowline (Pepin et al. 2019). Rising temperature and decreasing rainfall causes increasing droughts and water loss. Water loss is inversely proportional to temperature, especially for wet-skinned species such as frogs and salamanders (Khwarahm et al. 2021), which require moist skin to breathe (Lertzman‐Lepofsky et al. 2020). In that case, the adaptations between "hibernation", "leaving the shelter", and "the presence of water resources" may be disturbed for the Kurdistan newts. These possible events are significant because N. derjugini is highly dependent on seasonal streams and aquatic ecosystems during the breeding and resting seasons.
The overlapping of the distribution range of the species with the Zagros forests can have negative and positive effects on the population. Small parts of the Zagros forests are still suitable and have not experienced any significant increase in LST (at 95% and 99% significance levels). The vegetation cover inside streams and groves of the distribution range, such as open oak woodland and deciduous dwarf, amygdale, and cushion scrublands (Henareh Khalyani et al. 2013), are some types of riparian ecosystem the canopy cover of which can moderate the temperature increase and block direct sunlight (Moore et al. 2005). But Zagros forests are mixed with bare soil in the distribution range of Kurdistan salamander, and this will lead to temperature heterogeneity in the region because bare soil and straw have a higher LST value than the forest (Sayão et al. 2020). This is why some southern localities in bare soil are subject to temperature increase. But one of the negative effects of temperature increase in Zagros forests, especially in summer, is the increase in the probability of wildfire, which has been observed repeatedly in the past few years in Kermanshah province (range of distribution of the species in basin 3). The importance of the effect of wildfire depends on the biophysical context and severity of the fire, the response of some streams is negligible and some heating dramatically (Isaak el al. 2010). Studies have shown that the loss of riparian canopies due to forest wildfire will increase the temperature between 0.8 and 15 °C (Warren et al. 2022). Therefore, managing mass forest areas with an emphasis on preventing the development of agricultural lands, deforestation, improper wood harvesting, and livestock grazing can effectively control temperature fluctuations in these areas.
According to the Linkage Mapper results, the paths connecting the middle and lower latitude-localities in the distribution range have a higher current flow, which is in line with Malekoutian et al. (2020). High mountains and high rainfall in the species distribution range in Iran (Karami 2021), along with snowmelt, facilitate the formation of seasonal streams. These conditions have caused the connectivity between the core populations in this region to establish a higher current flow than in other areas. Considering the movement ability of Kurdistan newt, which is estimated at 49.19 ± 71.75 m (Sharifi and Afroosheh 2014), most of the movements in basin 3 are done at short distances and probably with the help of temporary springs and streams that can play the role of stepping-stones. The role that springs and streams play in the distribution of populations reveals the dependence of amphibians on parameters such as humidity and water as a context for movement. Linkages located in basin 3 are more strongly affected by the increasing trend of LST than the other two basins. In the last few decades, increasing landuse change and development of agricultural lands through clear-cutting, overgrazing, and the presence of nomads have been among the concerns which led to changes in vegetation cover and land uses in the distribution range of the species. According to the study of Olson and Van Horne (2017), no-harvest streamside buffers have a positive effect on maintaining the water temperature inside the stream compared to harvested stands (6 m buffer width) and this distance will be more for sensitive salamanders (> 15 m). With low temperature, metabolic rates will also be low and the need for food will be less. If the temperature rises, it can also affect the fluctuation of water in canals and springs in highlands, which will lead to competition among populations. Findings show that water levels in streams or ponds increase or decrease competition for position and oxygen in the water (Loman 2002). Increasing the temperature reduces water-dissolved oxygen and can also affect swimming and invasion performance during the tadpoles (Bickford et al. 2010). Any changes that affect seasonal streams and springs can affect breeding success and its census and conservation monitoring program (Heydari et al. 2021).
MK test at the level of localities showed that with decreasing latitude, significant changes in LST occurred in spring and winter, and the LST trend in summer was not significant. These findings indicate that the alarm of rising temperatures in the spring has been sounded for areas where the species is present. Sen's slope showed that the trend of LST increase was lower in spring than in winter (Table 2) and the grouping in Fig. 6 shows that these changes are almost evident in the southern localities. As there is no time lag between spring and winter, the species' future seems to face the challenges of severe temperature rise in these habitats, which is expected to exacerbate by summer droughts. Most local and regional extinctions will likely occur in the southern range and basin 3. The physiology and behavior of amphibians are related to temperature and loss of water-related parameters (Kearney and Porter 2009; Pirtle et al. 2019). By affecting water, the temperature can disrupt the survival of larvae (Maciel and Juncá 2009). The effect of temperature on the developmental rates of N. derjugini has also been confirmed (Sharifi et al. 2017). The temperature rise is even effective on the rate of cannibalism in the Kurdistan salamanders (Vaissi and Sharifi 2016). By increasing temperature that currently exists in the south of the distribution range, the probability of this cannibalism will increase. With the increasing extinction rates, the number of amphibians entering captive breeding programs is high (Conde et al. 2013). The distribution pattern of the Kurdistan newt, its low dispersion capability, and short movement distances are among the reasons that make the development of conservation programs necessary for the population of this species. These programs can be presented in the form of captive breeding and reintroduction to higher latitudes and the management of suitable habitats. Some of the ancestral taxa of Neurergus, scattered throughout Europe and the Mediterranean, moved to the south for better climatic conditions, resulting in a limited population expansion to the Zagros and nearby areas (e.g., Iran, Iraq, and Turkey) (Steinfartz et al. 2000). Therefore, the highlands between Iraq and Turkey may have habitats for reintroduction. In a study by Sharifi and Vaissi (2014) on the experimental release of N. derjugini, the results showed that captive-raised N. derjugini, released into the wild after metamorphosis, can survive until the second growing season. This finding could provide a life-stage choice for the reintroduction plans. When using SDMs to identify reintroduction sites, it is necessary to include dynamic variables such as temperature, humidity, and vegetation covers in the modeling and consider their changes as a trend in locating and selecting reintroduction sites. Using SDMs predictions based solely on Bioclim variables as a baseline map does not yield reliable results. In a study by Vaissi et al. (2019), those areas exposed to rising temperatures were introduced as suitable sites for reintroduction of N. derjugini.
Despite studies on the genetics of the species and its populations (e.g., Afroosheh et al. 2019; Malekoutian et al. 2020), fine-scale research on the ecology of the species at different stages of its life cycle is limited (e.g., Sharifi and Afroosheh 2014; Farasat and Sharifi 2014; Afroosheh et al. 2016). No information is available on temperature fluctuations in springs and streams in the larval stage, fluctuations in water parameters due to seasonal temperature changes, and the trend of land use/land cover change in the species distribution range. The information about the habitat of the Kurdistan salamanders in Iraq is also limited. These factors have led to insufficient knowledge of the species to implement conservation plans in the future. More detailed studies at fine scales are suggested for investigating the habitats of Kurdistan newts. According to the studies, the research findings will be more in line with the ecological characteristics of newts at this scale (Ficetola et al. 2018).
Many environmental factors that can affect the habitat of plant and animal species can dynamically be retrieved from satellite images. It is possible to provide them on fine or coarse scales depending on the type of satellite. This information leads to a complete view of habitat conditions, a finding that may not be detectable using just SDMs due to the frequency and up-to-datedness of this data, especially in the case of amphibians, which are highly sensitive to climate and its changes. The trend analysis tests, examining homogeneity to identify data breakpoints, provide very efficient results that can reveal differences in species distribution range when combined with environmental data. The findings clearly showed the difference between the results of SDMs and the current conditions in terms of the species vulnerability to temperature. The complex relationships between temperature and habitat parameters and different stages of amphibian life reveal the possibility of predicting unsuitable conditions for the species in this study. Kurdistan newt in the southern part of its distribution area, i.e., in Iran, experiences more severe conditions in terms of temperature increase, and this increasing trend in seasons such as winter can lead to the disruption of behavioral adaptations. Relying on the findings of this study and such fine-scale studies about the effect of temperature or other dynamic variables such as humidity and vegetation on different life stages and habitats of newts, the species populations can be managed by captive breeding and reintroduction in areas with no trends.
Availability of data and materials
Data are available upon reasonable request.
Afroosheh M, Akmali V, Esmaili S, Sharifi M (2016) Distribution and abundance of the endangered yellow spotted mountain newt Neurergus microspilotus (Caudata: Salamandridae) in western Iran. Herpetol Conserv Biol 11(1):52–60
Afroosheh M, Rödder D, Mikulicek P, Akmali V, Vaissi S, Fleck J, Schneider W, Sharifi M (2019) Mitochondrial DNA variation and Quaternary range dynamics in the endangered yellow spotted mountain newt, Neurergus derjugini (Caudata, Salamandridae). J Zoolog Syst Evol Res 57(3):580–590. https://doi.org/10.1111/jzs.12275
Ahmadi M, Nezami Balouchi B, Jowkar H, Hemami MR, Fadakar D, Malakouti-Khah S, Ostrowski S (2017) Combining landscape suitability and habitat connectivity to conserve the last surviving population of cheetah in Asia. Divers Distrib 23(6):592–603. https://doi.org/10.1111/ddi.12560
Bai L, Long D, Yan L (2019) Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resour Res 55(2):1105–1128. https://doi.org/10.1029/2018WR024162
Barabanov AV, Litvinchuk SN (2015) A new record of the Kurdistan Newt (Neurergus derjugini) in Iran and potential distribution modeling for the species. Russ J Herpetol 22:107–115. https://doi.org/10.30906/1026-2296-2015-22-2-107-115
Barrett K, Nibbelink NP, Maerz JC (2014) Identifying priority species and conservation opportunities under future climate scenarios: Amphibians in a biodiversity hotspot. J Fish Wildl Manag 5(2):282–297. https://doi.org/10.3996/022014-JFWM-015
Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol Lett 15(4):365–377. https://doi.org/10.1111/j.1461-0248.2011.01736.x
Benz SA, Bayer P, Blum P (2017) Global patterns of shallow groundwater temperatures. Environ Res Lett 12(3):034005. https://doi.org/10.1088/1748-9326/aa5fb0
Bickford D, Howard SD, Ng DJ, Sheridan JA (2010) Impacts of climate change on the amphibians and reptiles of Southeast Asia. Biodivers Conserv 19(4):1043–1062. https://doi.org/10.1007/s10531-010-9782-4
Bobrowski M, Weidinger J, Schickhoff U (2021) Is new always better? frontiers in global climate datasets for modeling treeline species in the Himalayas. Atmosphere 12(5):543. https://doi.org/10.3390/atmos12050543
Brown JL, Bennett JR, French CM (2017) SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. PeerJ 5:e4095. https://doi.org/10.7717/peerj.4095/table-1
Cemal Varol TOK, koyun M, Çiçek K, (2016) Predicting the current and future potential distributions of Anatolia Newt, Neurergus strauchii (Steindachner, 1887), with a new record from Elazığ (Eastern Anatolia, Turkey). Biharean Biol 10(2):104–108
Chehbouni A, Seen DL, Njoku EG, Monteny BM (1996) Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sens Environ 58(2):177–186. https://doi.org/10.1016/S0034-4257(96)00037-5
Conde DA, Colchero F, Gusset M, Pearce-Kelly P, Byers O, Flesness N, Browne RK, Jones OR (2013) Zoos through the lens of the IUCN Red List: a global metapopulation approach to support conservation breeding programs. PLoS ONE 8(12):e80311. https://doi.org/10.1371/journal.pone.0080311
Cushman SA (2006) Effects of habitat loss and fragmentation on amphibians: a review and prospectus. Biol Conserv 128(2):231–240. https://doi.org/10.1016/j.biocon.2005.09.031
Datta A, Schweiger O, Kühn I (2020) Origin of climatic data can determine the transferability of species distribution models. NeoBiota 59:61–76. https://doi.org/10.3897/neobiota.59.36299
Dervo BK, Bærum KM, Skurdal J, Museth J (2016) Effects of temperature and precipitation on breeding migrations of amphibian species in southeastern Norway. Scientifica 4:1–8. https://doi.org/10.1155/2016/3174316
Dimri T, Ahmad S, Sharif M (2020) Time series analysis of climate variables using seasonal ARIMA approach. J Earth Syst Sci 129(1):1–16. https://doi.org/10.1007/s12040-020-01408-x
Duan SB, Li ZL, Wu H, Leng P, Gao M, Wang C (2018) Radiance-based validation of land surface temperature products derived from Collection 6 MODIS thermal infrared data. Int J Appl Earth Obs Geoinf 70:84–92. https://doi.org/10.1016/j.jag.2018.04.006
Dutta T, Sharma S, McRae BH, Roy PS, DeFries R (2016) Connecting the dots: mapping habitat connectivity for tigers in central India. Reg Environ Change 16(1):53–67. https://doi.org/10.1007/s10113-015-0877-z
Ermida SL, Trigo IF, DaCamara CC, Göttsche FM, Olesen FS, Hulley G (2014) Validation of remotely sensed surface temperature over an oak woodland landscape—The problem of viewing and illumination geometries. Remote Sens Environ 148:16–27. https://doi.org/10.1016/j.rse.2014.03.016
Esparza-Orozco A, Lira-Noriega A, Martínez-Montoya JF, Pineda-Martínez LF, de Jesús Méndez-Gallegos S (2020) Influences of environmental heterogeneity on amphibian composition at breeding sites in a semiarid region of Mexico. J Arid Environ 182:104259. https://doi.org/10.1016/j.jaridenv.2020.104259
Espín Sánchez D, Olcina Cantos J, Conesa García C (2022) Satellite thermographies as an essential tool for the identification of cold air pools: an example from SE Spain. Eur J Remote Sens 55(1):586–603. https://doi.org/10.1080/22797254.2022.2133744
Farasat H, Sharifi M (2014) Food habit of the endangered yellow-spotted newt Neurergus microspilotus (Caudata, Salamandridae) in Kavat Stream, western Iran. Zool Stud 53(1):1–9. https://doi.org/10.1186/s40555-014-0061-z
Ficetola GF, Lunghi E, Canedoli C, Padoa-Schioppa E, Pennati R, Manenti R (2018) Differences between microhabitat and broad-scale patterns of niche evolution in terrestrial salamanders. Sci Rep 8:10575. https://doi.org/10.1038/s41598-018-28796-x
Ghahremaninejad F, Hoseini E, Jalali S (2021) The cultivation and domestication of wheat and barley in Iran, brief review of a long history. Bot Rev 87(1):1–22. https://doi.org/10.1007/s12229-020-09244-w
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Guillevic PC, Biard JC, Hulley GC, Privette JL, Hook SJ, Olioso A, Göttsche FM, Radocinski R, Román MO, Yu Y, Csiszar I (2014) Validation of Land Surface Temperature products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) using ground-based and heritage satellite measurements. Remote Sens Environ 154:19–37. https://doi.org/10.1016/j.rse.2014.08.013
Guo Q, Liu Y (2010) ModEco: an integrated software package for ecological niche modeling. Ecography 33(4):637–642. https://doi.org/10.1111/j.1600-0587.2010.06416.x
Hamed KH, Rao AR (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204(1–4):182–196. https://doi.org/10.1016/S0022-1694(97)00125-X
Hanspach J, Kühn I, Schweiger O, Pompe S, Klotz S (2011) Geographical patterns in prediction errors of species distribution models. Glob Ecol Biogeogr 20(5):779–788. https://doi.org/10.1111/j.1466-8238.2011.00649.x
Heller NE, Zavaleta ES (2009) Biodiversity management in the face of climate change: a review of 22 years of recommendations. Biol Conserv 142(1):14–32. https://doi.org/10.1016/j.biocon.2008.10.006
Henareh Khalyani A, Mayer AL, Falkowski MJ, Muralidharan D (2013) Deforestation and landscape structure changes related to socioeconomic dynamics and climate change in Zagros forests. J Land Use Sci 8(3):321–340. https://doi.org/10.1080/1747423X.2012.667451
Hereher ME (2019) Estimation of monthly surface air temperatures from MODIS LST time series data: application to the deserts in the Sultanate of Oman. Environ Monit Assess 191(9):1–11. https://doi.org/10.1007/s10661-019-7771-y
Heydari N, Yousefkhani SH, Faizi H (2021) Comments on the distribution and population estimation of Neurergus derjugini (Urodela, Salamandridae) in western Iran. J Wildl Biodiver 5(4):68–81. https://doi.org/10.22120/jwb.2021.130245.1227
Hillman SS, Hillyard SD, Jackson DC, Mcclanahan LL, Withers PC, Wygoda ML (1992) Exchange of water, ions, and respiratory gases in terrestrial amphibians. Environ Physiol Amphib 34:125–150
Holt RD (2008) IJEE Soapbox: Habitats and seasons. Isr J Ecol Evol 54(3–4):279–285
Hulley GC, Ghent D, Göttsche FM, Guillevic PC, Mildrexler DJ, Coll C (2019) Land surface temperature. Steps towards Integrated Understanding of Variability and Change, Taking the Temperature of the Earth. https://doi.org/10.1016/B978-0-12-814458-9.00003-4
Isaak DJ, Luce CH, Rieman BE, Nagel DE, Peterson EE, Horan DL, Parkes S, Chandler GL (2010) Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network. Ecol Appl 20(5):1350–1371. https://doi.org/10.1890/09-0822.1
IUCN (2017) Summary Statistics. IUCN, Gland, Switzerland. iucnredlist.org/about/summary-statistics. Accessed 5 Mar 2018.
Jamali AA, Kalkhajeh RG, Randhir TO, He S (2022) Modeling relationship between land surface temperature anomaly and environmental factors using GEE and Giovanni. J Environ Manage 302:113970. https://doi.org/10.1016/j.jenvman.2021.113970
Kandya AN, Sarkar J, Chhabra A, Chauhan S, Khatri D, Vaghela AD, Kolte S (2021) Statistical assessment of the changing climate of Vadodara City, India During 1969–2006. Eur J Environ Sci 3(1):1–18. https://doi.org/10.34154/2021-EJCC-0015-01-18/euraass
Karami P (2021) Identifying and Analyzing Distribution of Habitat's Hotspots of Salient Vertebrates from Landscape Perspective in Kermanshah Province. PhD Thesis of Environmental Science, Faculty of Natural Resources and Environment, Malayer University. pp 421. (In Persian)
Karamiani R (2021) Effects of climate change on habitat suitability and distribution model of the critically endangered newt, Neurergus derjugini Nesterov, 1916 (Urodela: Salamandridae) from contemporary period to 2030. J Taxon Biosyst 13(46): 93–110. https://doi.org/10.22108/tbj.2021.128230.1158
Karami P, Tavakoli S (2022) Identification and analysis of areas prone to conflict with wild boar (Sus scrofa) in the vineyards of Malayer County, western Iran. Ecol Model 471:110039. https://doi.org/10.1016/j.ecolmodel.2022.110039
Kearney M, Porter W (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol Lett 12(4):334–350. https://doi.org/10.1111/j.1461-0248.2008.01277.x
Khwarahm NR, Ararat K, Qader S, Sabir DK (2021) Modeling the distribution of the Near Eastern fire salamander (Salamandra infraimmaculata) and Kurdistan newt (Neurergus derjugini) under current and future climate conditions in Iraq. Ecol Inform 63:101309. https://doi.org/10.1016/j.ecoinf.2021.101309
Knapp RA, Briggs CJ, Smith TC, Maurer JR (2011) Nowhere to hide: impact of a temperature-sensitive amphibian pathogen along an elevation gradient in the temperate zone. Ecosphere 2(8):93. https://doi.org/10.1890/ES11-00028.1
Kuenzer C, Dech S (2013) Theoretical background of thermal infrared remote sensing. Thermal infrared remote sensing. Springer, Dordrecht, pp 1–26
Lange H (2001) Time-series analysis in ecology. Encycl Life Sci. https://doi.org/10.1038/npg.els.0003276
Lawler JJ (2009) Climate change adaptation strategies for resource management and conservation planning. Ann N Y Acad Sci 1162(1):79–98. https://doi.org/10.1111/j.1749-6632.2009.04147.x
Lertzman-Lepofsky GF, Kissel AM, Sinervo B, Palen WJ (2020) Water loss and temperature interact to compound amphibian vulnerability to climate change. Glob Change Biol 26(9):4868–4879. https://doi.org/10.1111/gcb.15231
Li Y, Cohen JM, Rohr JR (2013) Review and synthesis of the effects of climate change on amphibians. Integr Zool 8(2):145–161. https://doi.org/10.1111/1749-4877.12001
Li Y, Zhao M, Mildrexler DJ, Motesharrei S, Mu Q, Kalnay E, Zhao F, Li S, Wang K (2016) Potential and actual impacts of deforestation and afforestation on land surface temperature. J Geophys Res Atmos 121(24):14–372. https://doi.org/10.1002/2016JD024969
Liu X, Yan L (2017) Elevation-dependent climate change in the Tibetan Plateau. Oxford Research Encyclopedia of Climate Science. Oxford University Press, Oxford, pp 1–13
Liu C, Newell G, White M (2019) The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo-absences or background sites. Ecography 42(3):535–548. https://doi.org/10.1111/ecog.03188
Loman J (2002) Temperature genetic and hydroperiod effects on metamorphosis of brown frogs Rana arvalis and R. temporaria in the field. J Zool 258:115–129. https://doi.org/10.1017/S0952836902001255
Lourenço-de-Moraes R, Campos FS, Ferreira RB, Solé M, Beard KH, Bastos RP (2019) Back to the future: conserving functional and phylogenetic diversity in amphibian-climate refuges. Biodivers Conserv 28(5):1049–1073. https://doi.org/10.1007/s10531-019-01706-x
Maciel TA, Juncá FA (2009) Effects of temperature and volume of water on the growth and development of tadpoles of Pleurodema diplolister and Rhinella granulosa. Zool Bespozvon 26:413–418. https://doi.org/10.1590/S1984-46702009000300005
Malekoutian M, Sharifi M, Vaissi S (2020) Mitochondrial DNA sequence analysis reveals multiple Pleistocene glacial refugia for the Yellow-spotted mountain newt, Neurergus derjugini (Caudata: Salamandridae) in the mid-Zagros range in Iran and Iraq. Ecol Evol 10(5):2661–2676. https://doi.org/10.1002/ece3.6098
Malekoutian M, Sharifi M, Vaissi S (2021) Potential impacts of climate change on the distribution of the Yellow-spotted mountain newt Neurergus derjugini (Nesterov, 1916). Environ Sci 19(2):78 (In Persian)
McRae BH (2012) Centrality Mapper Connectivity Analysis Software. https://circuitscape.org/linkagemapper/linkage-mapper-tools/
Mezger G, De Stefano L, González del Tánago M (2022) Analysis of the evolution of climatic and hydrological variables in the Tagus River Basin. Spain Water 14(5):818. https://doi.org/10.3390/w14050818
Mildrexler DJ, Zhao M, Running SW (2011) A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. J Geophys Res Biogeosci 116:G03025. https://doi.org/10.1029/2010JG001486
Minacapilli M, Consoli S, Vanella D, Ciraolo G, Motisi A (2016) A time domain triangle method approach to estimate actual evapotranspiration: Application in a Mediterranean region using MODIS and MSG-SEVIRI products. Remote Sens Environ 174:10–23. https://doi.org/10.1016/j.rse.2015.12.018
Moore RD, Spittlehouse DL, Story A (2005) Riparian microclimate and stream temperature response to forest harvesting: a review. J Am Water Resour Assoc 41(4):813–834. https://doi.org/10.1111/j.1752-1688.2005.tb03772.x
Morovati M, Karami P, Bahadori Amjas F (2020) Accessing habitat suitability and connectivity for the westernmost population of Asian black bear (Ursus thibetanus gedrosianus, Blanford, 1877) based on climate changes scenarios in Iran. PLoS ONE 15(11):e0242432. https://doi.org/10.1371/journal.pone.0242432
Neeti N, Eastman JR (2011) A contextual Mann-Kendall approach for the assessment of trend significance in image time series. Trans GIS 15(5):599–611. https://doi.org/10.1111/j.1467-9671.2011.01280.x
Olson DH, Van Horne B (2017) People, forests, and change: lessons from the Pacific Northwest. Island Press, Washington, DC, USA. https://doi.org/10.5822/978-1-61091-768-1
Osland MJ, Stevens PW, Lamont MM, Brusca RC, Hart KM, Waddle JH, Langtimm CA, Williams CM, Keim BD, Terando AJ, Reyier EA (2021) Tropicalization of temperate ecosystems in North America: The northward range expansion of tropical organisms in response to warming winter temperatures. Glob Change Biol 27(13):3009–3034. https://doi.org/10.1111/gcb.15563
Ossa-Moreno J, Keir G, McIntyre N, Cameletti M, Rivera D (2019) Comparison of approaches to interpolating climate observations in steep terrain with low-density gauging networks. Hydrol Earth Syst Sci 23(11):4763–4781. https://doi.org/10.5194/hess-23-4763-2019
Pepin N, Deng H, Zhang H, Zhang F, Kang S, Yao T (2019) An examination of temperature trends at high elevations across the Tibetan Plateau: the use of MODIS LST to understand patterns of elevation-dependent warming. J Geophys Res Atmos 124(11):5738–5756. https://doi.org/10.1029/2018JD029798
Piri Sahragard H, Ajorlo M, Karami P (2021) Landscape structure and suitable habitat analysis for effective restoration planning in semi-arid mountain forests. Ecol Process 10:17. https://doi.org/10.1186/s13717-021-00289-2
Pirtle EI, Tracy CR, Kearney MR (2019) Hydroregulation: A neglected behavioral response of lizards to climate change? In: Behavior of Lizards. CRC Press, pp 343–374
Polis GA (1981) The evolution and dynamics of intraspecific predation. Annu Rev Ecol Syst 12:225–251. https://doi.org/10.1146/annurev.es.12.110181.001301
Power ME, Parker MS, Dietrich WE (2008) Seasonal reassembly of a river food web: floods, droughts, and impacts of fish. Ecol Monogr 78(2):263–282. https://doi.org/10.1890/06-0902.1
Prakash S, Shati F, Norouzi H, Blake R (2019) Observed differences between near-surface air and skin temperatures using satellite and ground-based data. Theor Appl Climatol 137(1):587–600. https://doi.org/10.1007/s00704-018-2623-1
Rani S, Mal S (2022) Trends in land surface temperature and its drivers over the High Mountain Asia. Egypt J Remote Sens 25(3):717–729. https://doi.org/10.1016/j.ejrs.2022.04.005
Ruiz-García A, Roco ÁS, Bullejos M (2021) Sex differentiation in amphibians: effect of temperature and its influence on sex reversal. Sex Dev 15(1–3):157–167. https://doi.org/10.1159/000515220
Sauer EL, Cruz J, Crone E, Lewis C, Plumier E, Cwynar B, Drake D, Herrick BM, Preston DL (2022) Multiscale drivers of amphibian community occupancy in urban ponds. Urban Ecosyst 25(5):1469–1479. https://doi.org/10.1007/s11252-022-01239-2
Sayão VM, dos Santos NV, de Sousa MW, Marques KP, Safanelli JL, Poppiel RR, Demattê JA (2020) Land use/land cover changes and bare soil surface temperature monitoring in southeast Brazil. Geoderma Reg 22:e00313. https://doi.org/10.1016/j.geodrs.2020.e00313
Sharifi M, Afroosheh M (2014) Studying migratory activity and home range of adult Neurergus microspilotus (Nesterov, 1916) in the Kavat Stream, western Iran, using photographic identification (Caudata: Salamandridae). Herpetozoa 27(1–2):77–82
Sharifi M, Vaissi S (2014) Captive breeding and trial reintroduction of the endangered Yellow-spotted Mountain Newt Neurergus microspilotus in western Iran. Endanger Species Res 23(2):159–166. https://doi.org/10.3354/esr00552
Sharifi M, Karami P, Akmali V, Afroosheh M, Vaissi S (2017) Modeling geographic distribution for the endangered yellow spotted mountain newt, Neurergus microspilotus (Amphibia: Salamandridae) in Iran and Iraq. Herpetol Conserv Biol 12(2):488–497
Shin Y, Min MS, Borzée A (2021) Driven to the edge: Species distribution modeling of a Clawed Salamander (Hynobiidae: Onychodactylus koreanus) predicts range shifts and drastic decrease of suitable habitats in response to climate change. Ecol Evol 11(21):14669–14688. https://doi.org/10.1002/ece3.8155
Singh RP, Paramanik S, Bhattacharya BK, Behera MD (2020) Modelling of evapotranspiration using land surface energy balance and thermal infrared remote sensing. Trop Ecol 61(1):42–50. https://doi.org/10.1007/s42965-020-00076-8
Steinacker R, Ratheiser M, Bica B, Chimani B, Dorninger M, Gepp W, Lotteraner C, Schneider S, Tschannett S (2006) A mesoscale data analysis and downscaling method over complex terrain. Mon Weather Rev 134(10):2758–2771. https://doi.org/10.1175/MWR3196.1
Steinfartz S, Veith M, Tautz D (2000) Mitochondrial sequence analysis of Salamandra taxa suggests old splits of major lineages and postglacial recolonizations of Central Europe from distinct source populations of Salamandra salamandra. Mol Ecol 9(4):397–410. https://doi.org/10.1046/j.1365-294x.2000.00870.x
Stuart SN, Chanson JS, Cox NA, Young BE, Rodrigues AS, Fischman DL, Waller RW (2004) Status and trends of amphibian declines and extinctions worldwide. Science 306(5702):1783–1786. https://doi.org/10.1126/science.1103538
Taylor PD, Fahrig L, Henein K, Merriam G (1993) Connectivity is a vital element of landscape structure. Oikos 89:571–573. https://doi.org/10.2307/3544927
Vaissi S (2021) Design of protected area by tracking and excluding the effects of climate and landscape change: a case study using Neurergus derjugini. Sustainability 13(10):5645. https://doi.org/10.3390/su13105645
Vaissi S, Sharifi M (2016) Variation in food availability mediate the impact of density on cannibalism, growth, and survival in larval yellow spotted mountain newts (Neurergus microspilotus): Implications for captive breeding programs. Zoo Biol 35(6):513–521. https://doi.org/10.1002/zoo.21327
Vaissi S, Farasat H, Mortezazadeh A, Sharifi M (2019) Incorporating habitat suitability and demographic data for developing a reintroduction plan for the critically endangered yellow spotted mountain newt Neurergus derjugini. Herpetol J 29(4):281–293. https://doi.org/10.33256/hj29.4.282294
Wan Z (2014) New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens Environ 140:36–45. https://doi.org/10.1016/j.rse.2013.08.027
Warren DR, Roon DA, Swartz AG, Bladon KD (2022) Loss of riparian forests from wildfire led to increased stream temperatures in summer, yet salmonid fish persisted. Ecosphere. 13(9):e4233. https://doi.org/10.1002/ecs2.4233
Wauchope HS, Amano T, Geldmann J, Johnston A, Simmons BI, Sutherland WJ, Jones JP (2021) Evaluating impact using time-series data. Trends Ecol Evol 36(3):196–205. https://doi.org/10.1016/j.tree.2020.11.001
White ER, Hastings A (2020) Seasonality in ecology: progress and prospects in theory. Ecol Complex 44:100867. https://doi.org/10.1016/j.ecocom.2020.100867
Zhao X, Xia H, Liu B, Jiao W (2022) Spatiotemporal comparison of drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 using various drought indices in Google Earth Engine. Remote Sens 14(7):1570. https://doi.org/10.3390/rs14071570
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Karami, P., Tavakoli, S. & Esmaeili, M. Evolution of seasonal land surface temperature trend in pond-breeding newt (Neurergus derjugini) in western Iran and eastern Iraq. Ecol Process 12, 14 (2023). https://doi.org/10.1186/s13717-023-00426-z
- Seasonal change
- Local extinction
- Climate refuge