Taxonomic, functional and phylogenetic diversity of forest mammals mainly associated with elevation range

doi:10.21203/rs.3.rs-2377434/v1

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Taxonomic, functional and phylogenetic diversity of forest mammals mainly associated with elevation range

https://doi.org/10.21203/rs.3.rs-2377434/v1

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published 07 Oct, 2023

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Forest mammal diversity has declined rapidly due to the widely loss and fragmentation of primary forest habitats, further threatening the functioning and biodiversity of forest ecosystems and emphasizes the importance of the research on forest mammal diversity. This study tried to link forest mammal diversity captured by camera traps with elevation range, contemporary climate, paleoclimate change and human activities in China, a country with diverse forest types, high mammal diversity, and massive anthropogenic disturbance on primary forest. The results showed that the camera traps in 72 Chinese forest sites captured 125 mammal species, accounting for 18% of all mammal species in China. In addition, the phylogenetic and functional diversity of these species accounted for 33% and 49% of all mammal species in China. Notably, forest sites with higher elevation range had higher species richness, higher functional diversity, over-dispersed functional structure, but clustered phylogenetic structure. Stable paleoclimate promoted species richness and functional diversity. Higher contemporary temperature and more precipitation promoted phylogenetic diversity and over-dispersed phylogenetic structure. In addition, anthropogenic factors (cropland area and human population size) had weak impacts on forest mammal diversity. These findings suggest that the mammal diversity in Chinese forest ecosystems is mainly associated with natural environmental variables, supporting the habitat heterogeneity hypothesis, history climate stability hypothesis, and tropical conservatism hypothesis. Meanwhile, the weak links between forest mammal diversity with anthropogenic activities may indicate the important role of forest ecosystems in conserving forest mammal diversity in China.

camera traps

functional diversity

habitat heterogeneity

mammal diversity

phylogenetic diversity.

Mammals play a vital role in forest ecosystems mainly through their effects on food web dynamics, micro-habitats formation, nutrients and biomass flow, pollination and seed dispersal (Fricke et al., 2022; Lacher et al., 2019). However, the sixth biological mass extinction is now underway (Ceballos et al., 2017; Cowie et al., 2022). Specifically, over 300 mammal species have died out since the Late Pleistocene (Davis et al., 2018). According to the IUCN Red List, 26% of mammals are at risk of extinction (IUCN, 2021). Notably, when compared to other habitats, forest habitat has the highest diversity of mammal species, as well as the highest proportion of threatened mammals (Cox et al., 2022). However, an assessment found that only 40% of the world's forests with high landscape level integrity were still existent in 2019 (Grantham et al., 2020). This massive loss and fragmentation of intact forest habitat has significantly reduced the forest mammal diversity and threatened the functioning and biodiversity of other taxon groups of forest ecosystems. For example, forest area and habitat configuration were the two important factors on the occurrence declining of koala (Phascolarctos cinereus), which has highly specific habitat requirements and low migration capacity (McAlpine et al., 2006). Additionally, the widespread mammal defaunation could significantly decrease the plant seed's dispersal ability to follow climate change (Fricke et al., 2022). Therefore, the understanding of forest mammal diversity distribution patterns and potential drivers is fundamental for ecosystem management and biodiversity conservation in forest ecosystems.

Recently, a growing number of studies have found that species richness alone cannot fully capture changes in biodiversity (Hillebrand et al., 2018). However, multi-dimensional diversity indices, such as phylogenetic diversity and functional diversity, make it possible to evaluate biodiversity in a comprehensive manner and to reflect the evolutionary history of the species and differences in ecosystem functions (Liu et al., 2021; Safi et al., 2011). For example, the extinction of functional redundant species had little effect on ecosystem functioning (Pimiento et al., 2020). However, if an ancestor or functionally unique species was lost, evolutionary history and ecosystem functioning would be more impacted (Brodie et al., 2021). In addition, the studies of phylogenetic diversity and functional diversity are critical for exploring the mechanisms of the community assembly (e.g. inter-specific competition or habitat filtering; Cardillo, 2011) and the mechanisms of geographic distribution patterns of biodiversity (Oliveira et al., 2016).

Notably, many classical theories have been proposed to explain the impacts of environmental factors on the geographic distribution patterns of biodiversity (Currie et al., 2004; Fjeldsaå and Lovett, 1997; McGlone, 1996). The energy related hypothesis proposes that contemporary temperature and precipitation could affect the biodiversity patterns through their effects on the speciation, extinction, evolutionary rate as well as resources availability, such as the productivity hypothesis and the water-energy hypothesis (Currie et al., 2004; Hawkins et al., 2003). For example, a study found that annual mean precipitation was the best predictor for small mammal phylogenetic diversity and functional diversity in Mount Kenya (Onditi et al., 2022). In addition, long term climate stability also has strong legacy on distributions of contemporary biodiversity (McGlone, 1996). For example, the history climate stability hypothesis proposes that the species richness increases with the increasing climate stability because of the lower extinction rate and the higher speciation rate (McGlone, 1996; Svenning et al., 2015). Moreover, habitat heterogeneity hypothesis proposes that regions with higher habitat heterogeneity could harbor higher species diversity by providing more ecological niches, more geographic isolation opportunities for speciation, as well as more refuges (Stein et al., 2015).

In addition to these natural environmental factors, the massive and rapid increasing anthropogenic activities, e.g., agricultural development, large human populations, urbanization and biological invasions, also have strong direct and indirect impacts on mammal diversity (Dirzo et al., 2014; Bogoni et al., 2020). For example, a study in Colombia found that mammal diversity and evenness deceased due to agriculture expansion (Boron et al., 2019). In addition, a study based on fossils of 351 mammal species, which were extinct since the beginning of the Late Pleistocene, found that the human population density can predict the past mammal extinction patterns with 96% accuracy (Andermann et al., 2020).

Since the 21st century, camera traps have been widely used by researchers to monitor wildlife as a non-invasive monitoring technology (Delisle et al., 2021). Camera traps have several advantages over other monitoring systems, including less expensive, unaffected by harsh environmental conditions, and are able to do large-scale and long-term monitoring (Hamel et al., 2013; Silveira et al., 2003). In recent years, camera traps have been widely used in assessing species richness (O’Brien, 2008), estimating population density (Hedges et al., 2015), monitoring behavior (Rowcliffe et al., 2014). In addition, global mammal monitoring databases by cameras traps have been established, such as TEAM (Jansen et al., 2014) and Emammal (Forrester et al., 2017). It is worth mentioning that there are also many studies using camera traps to monitor mammals and some established monitoring networks in China (Li, 2020). However, using mammal data monitored by camera traps to perform a analysis on geographic distribution pattern of forest mammal diversity have been less conducted, particularly in China.

China has high mammal species richness, diverse forest types, as well as wide climate range and elevation range (Lin et al., 2021). In addition, ten existing camera traps networks in China provide an ideal opportunity to evaluate the patterns and multiple drivers of geographic distributions of forest mammal diversity. Therefore, this study firstly searched the literatures with mammal species list recorded by camera traps in forest ecosystems in China. Then different biodiversity indices, including species richness, phylogenetic and functional diversity, phylogenetic and functional structure of these mammal communities were calculated. Finally, the relationships between these biodiversity indices and natural environmental factors (contemporary climate, paleoclimate changes, habitat heterogeneity) and anthropogenic factors (cropland area and human population size) were assessed.

2.1 Mammal community data

To collect the mammal species list captured by camera traps in Chinese forest ecosystems, we searched the literatures from Baidu Academic (https://xueshu.baidu.com/) using the Chinese translated keywords "camera trap" and "mammals". The studies in forest ecosystems were kept. To ensure the accuracy of the data and to include a relatively complete species list, studies with monitored time longer than one year were selected. The monitored time of these studies ranged from 12 months to 61 months. The number of cameras used in these studies ranged from 12 to 152. A total of 72 forest sites (most of which are located in nature reserves and forest parks; Fig. 1) with full mammal lists were gathered from 71 papers. The threatened status and endemic species information of these captured species were extracted from the Red List of Chinese vertebrates and the IUCN website (Jiang et al., 2016). The data of mammal checklists in the 72 forest sites was used to calculate biodiversity indices.

2.2 Phylogeny and trait dendrogram

To construct the phylogenies of the investigated species (125 species) and all Chinese mammal species (523 species), we pruned two new phylogenies from a phylogeny containing 5911 mammalian species (Upham et al., 2019). The obtained 1000 randomly distributed phylogenetic trees were used to construct a Maximum Clade Credibility tree, using the mean nodes heights method by the TreeAnnotator v1.8.2 software of the BEAST package (Drummond and Rambaut, 2007).

The mammal traits were collected from the collated global mammal trait database (Cooke et al., 2019), including body mass, flight ability, habitat width, diet, diel activity (diurnal/nocturnal) and litter size (number of female litters per litter). In addition, these traits were transformed to make them normality. Firstly, the body mass and litter size were log10 transformed. Then the habitat width was square rooted. Finally, all six traits were standardized (mean = 0 and standard deviation = 1). These six traits comprehensively reflect the mammal ecological niches such as resource capture and resource use. Gower's distance was used to calculate the distance of the traits among these species. The functional dendrogram was constructed using hierarchical clustering and choosing the "complete" method.

2.3 Explanatory variables

Explanatory variables included mean annual temperature (MAT), mean annual precipitation (MAP), glacial-interglacial temperature anomaly (Anomaly MAT), glacial-interglacial precipitation anomaly (Anomaly MAP), elevation range, area of forest reserves, number of cameras, cropland area and human population size. Climate data were downloaded from the WorldClim database (WorldClim, http://www.worldclim.org) (Hijmans et al., 2005). Anomaly MAT/MAP was calculated as the contemporary MAT/MAP minus the MAT/MAP in Last Glacial Maximum. The elevation range, area of forest reserves/parks and number of cameras were collected from the 71 papers. The cropland area and human population size data for the intermediate year 2015 based on the timing of forest mammal monitoring were used, which were extracted through the polygon of each forest nature reserve/park from the HYDE 3.2 (Klein Goldewijk et al., 2017). Then the cropland and human population data were divided by the area of each nature reserve/park to get the average values. Due to the inadequate information documented in the papers and inadequate polygon data, the final dataset included 51 reserves/parks with elevation range data, 69 reserves/parks with area data, 55 reserves/parks with the number of cameras, 51 reserves/parks with cropland area and human population number data.

2.4 Statistical analysis

Species richness (SR), standardized effect size of phylogenetic diversity (ses.pd), and net relatedness index (NRI) was used as biodiversity indices. Similar to phylogenetic diversity, functional diversity was also calculated by ses.pd and NRI.

$$ses.pd=({PD}_{obs}-mean{PD}_{rnd})/sd{PD}_{rnd}$$

Standardized phylogenetic/functional diversity (ses.pd) controls the effect of species richness on phylogenetic diversity. PD_obs is the observed phylogenetic diversity in forest sites, meanPDrnd is the mean PD value of the null models (999 times of shuffling distance matrix labels), and sdPD_rnd represents the standard deviation of PD of the null models.

$$NRI=-1\times ({MPD}_{obs}-meanM{PD}_{rnd})/sd{MPD}_{rnd}$$

Where MPDobs denotes the observed mean pairwise phylogenetic distance of mammals in a community. MeanMPD_rnd denotes the mean MPD of the null models (999 times of shuffling distance matrix labels), and sdMPD_rnd represents the standard deviation of MPD of the null models.

When ses.pd is greater than 0, it indicates that the observed phylogenetic diversity is greater than the mean random phylogenetic diversity. When ses.pd is less than 0, it indicates that the observed phylogenetic diversity is less than the mean random phylogenetic diversity. When NRI is greater than 0, it indicates that the observed MPD is smaller than the randomly generated values and the phylogenetic structure of the community is more clustered. When NRI is less than 0, the phylogenetic structure is more over-dispersed.

To understand the influences of environmental factors on species richness, phylogenetic and functional diversity, regression analyses were performed with each diversity index as the dependent variable, and climate, elevation change, area of forest reserves/parks, number of cameras, cropland area and human population size as independent variables. All variables were standardized (mean = 0 and standard deviation = 1), and species richness was log-transformed to make species richness normality. Ordinary least squares (OLS) models and simultaneous autoregressive (SAR, spatial autocorrelation in the residuals could be controlled) models were used to analyze these relationships. All analyses were performed in R studio v4.0.3.

In this study, a total of 125 mammal species was captured by over 3011 cameras in the 72 forest sites, accounting for 18% of all mammal species in China. Phylogenetic diversity and functional diversity (not standardized) of these species accounted for 33% and 49% of total mammal diversity in China, respectively. Among captured species, there were 16 critically endangered species, 12 endangered species, 27 vulnerable species, and 20 near threatened species. And the endangered species accounted for 29.3% of the China’s endangered mammal species. In additional, there are 17 endemic species among recorded species, accounting for 11.3% of China’s endemic mammal species.

The results of SAR analyses were similar to the OLS results (Table 1, Table 2). The regression analyses results revealed that the factors significantly affecting species richness were elevation range (r² = 0.36), anomaly MAT (r² = 0.15), the number of cameras (r² = 0.14), and the population size (r² = 0.08). Species richness was positively correlated with elevation range and the number of cameras, and negatively correlated with anomaly MAT and population size (Table 1; Fig. 2).

Table 1

Regression (OLS and SAR) results of mammal diversity with environmental factors. MAT is mean annual temperature. MAP is mean annual precipitation. Anom MAT/MAP is glacial-interglacial anomaly in temperature/temperature. Area is area of forest reserves/parks. Cropland area is cropland area under the unit protection zone. Population size is population size under the unit protection zone. The two environmental factors with highest r² in each column were in bold. ^* p < 0.05, ^** p < 0.01.
	Species richness				Phylogenetic diversity				Functional diversity
	Coef_ols	r²_ols	Coef_SAR	r²_SAR	Coef_ols	r²_ols	Coef_SAR	r²_SAR	Coef_ols	r²_ols	Coef_SAR	r²_SAR
MAT	-0.01	0.00	-0.05	0.16	0.46	0.21^**	0.45	0.22^**	-0.03	0.00	-0.08	0.06
MAP	0.07	0.00	0.18	0.17	0.36	0.13^**	0.36	0.16^**	0.06	0.00	0.08	0.06
Anom MAT	-0.39	0.15^**	-0.40	0.22^*	-0.19	0.04	-0.26	0.12	-0.37	0.13^**	-0.36	0.14^**
Anom MAP	0.07	0.00	-0.002	0.16	-0.15	0.02	-0.09	0.10	0.13	0.02	0.08	0.06
Elevation range	0.61	0.36^**	0.61	0.36^**	-0.24	0.05	-0.12	0.10	0.64	0.38^**	0.68	0.38^**
Area	0.02	0.00	0.04	0.15	-0.20	0.04	-0.18	0.12	0.06	0.00	0.07	0.07
Number of cameras	0.37	0.14^**	0.32	0.22^**	-0.02	0.00	-0.02	0.05	0.26	0.06	0.25	0.09
Cropland area	-0.23	0.05	-0.15	0.24	0.23	0.05	0.20	0.12	-0.04	0.00	0.01	0.08
Population size	-0.28	0.08^*	-0.21	0.26	0.14	0.02	0.08	0.10	-0.07	0.01	-0.06	0.09

Table 2

Regression (OLS and SAR) results of phylogenetic NRI and trait NRI with environmental factors. Phylo NRI refers to phylogenetic net relatedness index, and Trait NRI refers to functional trait net relatedness index. The two environmental factors with highest r² in each column were in bold. ^* p < 0.05, ^** p < 0.01, ^** p < 0.001.
	Phylo NRI				Trait NRI
	Coef_ols	r²_ols	Coef_SAR	r²_SAR	Coef_ols	r²_ols	Coef_SAR	r²_SAR
MAT	-0.42	0.18^**	-0.40	0.19^**	0.12	0.01	0.10	0.02
MAP	-0.30	0.09^**	-0.30	0.14^*	0.12	0.01	0.10	0.02
Anom MAT	0.11	0.01	0.17	0.10	0.10	0.01	0.11	0.02
Anom MAP	0.09	0.01	0.05	0.09	-0.03	0.00	-0.02	0.01
Elevation range	0.34	0.10^*	0.22	0.13	-0.52	0.22^**	-0.53	0.23^**
Area	0.18	0.03	0.19	0.13	-0.09	0.01	-0.09	0.03
Number of cameras	0.05	0.00	0.04	0.03	-0.28	0.07	-0.29	0.09^*
Cropland area	-0.28	0.07	-0.24	0.09	-0.02	0.00	-0.02	0.00
Population size	-0.29	0.07	-0.23	0.09	0.07	0.01	0.07	0.01

The factors significantly affecting phylogenetic diversity were MAT (r² = 0.21) and MAP (r² = 0.13). Phylogenetic diversity was positively correlated with MAT and MAP (Table 1; Fig. 2). In addition, the factors significantly affecting the phylogenetic structure were MAT (r² = 0.18), elevation range (r² = 0.10), and MAP (r² = 0.09). The phylogenetic structure was positively associated with elevation range, and negatively associated with MAT and MAP (Table 2; Fig. 3).

The factors significantly affecting functional diversity were elevation range (r² = 0.38) and the anomaly MAT (r² = 0.13). Functional diversity had a positive correlation with elevation range, and negative correlation with anomaly MAT (Table 1; Fig. 2). In addition, the factor significantly affecting the functional structure was the elevation range (r² = 0.22). The relationship between the functional structure and elevation range was negative (Table 2; Fig. 3).

4.1 Composition characteristics of mammal species captured by camera traps

China is one of the richest country in the world in terms of mammal diversity, and endemic mammal species account for 21% of Chinese mammals (Jiang et al., 2017). The results show that 125 mammal species were monitored by camera traps in 72 forest sites, accounting for 18% of the mammal species in China, 33% and 49% of the phylogenetic diversity and the functional diversity of mammals in the country. And 13.6% of these monitored species were endemic species. Furthermore, China has several monitoring networks, with approximately 20,000 infrared cameras and approximately 17,000 infrared camera survey sites in 238 monitoring sample areas (Li, 2020). In addition, a recent study also finds that over 290 understory bird species are monitored by camera traps in Chinese nature reserves and forest parks, accounting for 21% of all Chinese bird species, 28% and 51% of phylogenetic and functional diversity of all Chinese bird species (Meng et al., 2021). And there is also a study using camera trap data from five continents to assess the response of mammalian carnivore to environmental and anthropogenic changes, which found carnivore richness was positively correlated with prey availability (Rich et al., 2017). These studies indicate that the monitored mammal species list is representative of the species diversity both in China and worldwide. The monitored data is well suited to be applied in the field of biodiversity and animal conservation.

4.2 Habitat heterogeneity and mammal diversity

Larger elevation range usually represents higher habitat heterogeneity (Stein et al., 2015; Wen et al., 2020). The results showed that habitat heterogeneity was positively correlated with species richness, functional diversity and more over-dispersed functional structure. This is consistent with the results of previous studies, which find the number of habitat types and elevation variability are positively related with mammal species richness and functional diversity in Africa and high-energy regions of the continental United States (Barr and Biernat, 2020; Kerr and Packer, 1997; Mortelliti and Brehm, 2020). Higher habitat heterogeneity could provide more refuges, ecological niches and geographic isolation (Moreno-Rueda and Pizarro, 2007; Stein et al., 2015), reducing species extinction and facilitating species formation and coexistence (Flantua and Hooghiemstra, 2018; Trew and Maclean, 2021). In addition, regions with high habitat heterogeneity have a weaker environmental filtering effect, promoting the over-dispersion of functional traits (García-Llamas et al., 2019). And the new ecological opportunities provided by the mountains, i.e., the Andes, could promote species adaptive radiation (Stroud and Losos, 2016).

On the contrary, the results showed that forest ecosystems with greater habitat heterogeneity had more clustered phylogenetic structure, i.e., more closely related species. This result is consistent with a recent study, which finds that the bird phylogenetic structure is more clustered with increasing elevation range in China (Wang et al., 2020). The large number of ecological niches and geographic isolation generated by the newly uplifted mountains will also promote in situ diversification of native species, leading to a more clustered phylogenetic structure (Perrigo et al., 2020). For example, low phylogenetic diversity of terrestrial vertebrates also exists in the Hengduan mountains of China, a region with a recent evolutionary history and a large number of younger species (Hu et al., 2021). And the alpine plant communities are phylogenetically clustered in the Hengduan mountains between 4300 and 5500 meters (Li et al., 2014). The occurrence of this phenomenon is associated with the rapid uplift of the Hengduan Mountains, which proves our findings (Xing and Ree, 2017).

4.3 Stable paleoclimate promotes the taxonomic and functional diversity

The results showed that paleoclimate change was negatively related with species richness and functional diversity. This is similar to the results of a study in Europe, which finds that southern Europe has higher mammal species richness and functional diversity than northern Europe due to its more stable paleoclimate (Santos et al., 2020). Large/rapid climate change could result in species migration and even extinction (Lister and Stuart, 2008), decreasing species richness (Blois and Hadly, 2009). This study found that paleoclimate stability could promote the accumulation of functional diversity. This is consistent with previous finding, which discovered that mammal functional diversity increased with climate stability in Australia's humid tropics (Oliveira et al., 2020). On the one hand, when the paleoclimate is unstable, there will be a non-random filtering of mammal functional traits (Blois and Hadly, 2009; Mouillot et al., 2013). For example, smaller size and more mobile species will appear in areas with high climate change (Bhagwat and Willis, 2008). On the other hand, due to the migration lag effect occurring after the glacier fades, the mammals could not fill the functional trait gaps in the community in time (Dullinger et al., 2012; Ordonez and Svenning, 2015). These reasons may lead to higher functional diversity in areas with stable paleoclimate.

4.4 Contemporary climate and mammal diversity

The results showed that phylogenetic diversity would be higher and phylogenetic structure would be more over-dispersed where contemporary temperature and precipitation are higher. This is similar to the results of a global mammal study, which finds that higher modern temperature is associated with higher mammalian phylogenetic diversity (Barreto et al., 2019). This could be explained by the tropical conservatism hypothesis, which states that the majority of extant clades are originated in the tropics and that only a few clades have evolved the ability to adapt to cold climates as the temperate zone area expanded 30–40 million years ago (Wiens and Donoghue, 2004). For example, a global bird study finds that the dispersal model and diversification time of birds are consistent with the assumptions of the tropical conservatism hypothesis (Duchêne and Cardillo, 2015). Meanwhile, one study in the New World finds that recent species formation and extinction rates of mammals are higher at high latitudes, which may lead to lower phylogenetic diversity and more clustered phylogenetic structure (Weir and Schluter, 2007).

Unlike previous global studies, our results showed that the contemporary precipitation and temperature did not significantly relate with mammal species richness. Previous studies have found that regions with higher precipitation and temperature can provide more resources and energy, thus having higher species richness (Hawkins et al., 2003). However, on the other hand, it was discovered that the factors affecting mammal species richness were different in various scales (Hortal et al., 2008). Meanwhile, a study finds that mammal species richness is influenced by different factors in various regions, with contemporary temperature being the strongest impacting factor in southern America, and paleoclimate stability having the biggest impact on mammal species richness in eastern Asia (Barreto et al., 2019), which is consistent with our results. On the other hand, a study in China discovers that the mammal species richness of different habitat groups is influenced by different factors, with forest mammals being significantly influenced by elevation range but grassland and desert mammals being mainly impacted by temperature annual range (Xu et al., 2015).

4.5 Anthropogenic factors have a weak impact on diversity

We found that the human population size was significantly negatively correlated with mammal species richness. This result was similar to the previous studies, which finds the human population density is the key cause of mammal becoming threatened (Andermann et al., 2020; McKee et al., 2013). Particularly, megafauna extinction has been demonstrated to be more closely linked to human activities than climate change (Prescott et al., 2012; Sandom et al., 2014). A larger human population always means more hunting pressure and environmental changes, which reduces the diversity of mammals (Kerr and Currie, 1995). But human population size and cropland area were not significantly correlated with phylogenetic and functional diversity and structure in this study. On one hand, cropland and human population may have little impact on the mammal diversity in protected areas. One study finds that the endangerment probability of mammals increases with increasing populated croplands, but decreases with increasing remote croplands (Pekin and Pijanowski, 2012). Low-intensity agricultural land use in protected areas may increase habitat heterogeneity to some extent (Mander et al., 1999). Therefore, the impact of cropland area on biodiversity may be not significant. On the other hand, changes in cropland area and human population size do not have significant effect on functional and phylogenetic diversity. According to one study in Myanmar, human activities have a negative influence on mammal richness but have no effect on functional composition except for the loss of apex predators because human activities have a greater impact on large carnivores (Cremonesi et al., 2021).

To sum up, this study explored the relationships between mammal diversity distribution patterns and environmental factors in Chinese forest ecosystems by analyzing data captured by camera traps. Through the analyses, we found that the camera traps could track mammals with more phylogenetic and functional diversity, as well as those that are endangered or endemic. This technique is suited to studying the mechanisms of mammal diversity distribution and maintenance. In addition, the mechanisms of the geographic distribution of forest mammal diversity in China were well explained by the habitat heterogeneity hypothesis, history climate stability hypothesis, and tropical conservatism hypothesis. In addition, anthropogenic factors have relatively weak impacts on mammal diversity in these forest ecosystems, suggesting the important role of these forest ecosystems in conserving forest mammal diversity in China.

Conflict of interests The authors declare that they have no conflict in interest.

Author contributions GF conceived the idea; LF collected the data; GF, LF and XM analyzed the data, and wrote the paper.

Availability of data and material Available upon on request to the authors.

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Taxonomic, functional and phylogenetic diversity of forest mammals mainly associated with elevation range

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Abstract

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1 Introduction

2 Materials And Methods

2.1 Mammal community data

2.2 Phylogeny and trait dendrogram

2.3 Explanatory variables

2.4 Statistical analysis

3 Results

4 Discussions

4.1 Composition characteristics of mammal species captured by camera traps

4.2 Habitat heterogeneity and mammal diversity

4.3 Stable paleoclimate promotes the taxonomic and functional diversity

4.4 Contemporary climate and mammal diversity

4.5 Anthropogenic factors have a weak impact on diversity

5 Conclusions

Declarations

References

Additional Declarations

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