Peng Zhang

Xianghong Dong

Gaël Grenouillet

Sovan Lek

Yichen Zheng

Jianbo Chang
email: changjb@whu.edu.cn

Species range shifts in response to climate change and human pressure for the world's largest amphibian

Keywords: range loss, Chinese giant salamander, human population density, climate change, species distribution model, nature reserve

especially considering the negative impact of human pressures in both contemporary periods and the near future. The species

1 Species range shifts in response to climate change and human pressure for the world's largest amphibian Abstract: The Chinese giant salamander, Andrias davidianus, the world's largest amphibian, is critically endangered and has an extremely unique evolutionary history.

Therefore, this species represents a global conservation priority and will be impacted by future climate and human pressures. Understanding the range and response to environmental change of this species is a priority for the identification of targeted conservation activities. We projected future range shifts of the Chinese giant salamander under the independent and combined impacts of climate change and human population density (HPD) variations by using ensemble species distribution models. We further evaluated the sustainability of existing nature reserves and identified priority areas for the mitigation or prevention of such pressures. Both climate change and increasing HPD tended to reduce the species range, with the latter leading to greater range losses and fragmentation of the range. Notably, 65.6%, 18.0% and 18.4% of the range loss were attributed solely to HPD change, solely to climate change and to their overlapping impacts, respectively. Overall, the average total and net losses of the species range were 52.5% and 23.4%, respectively, and HPD and climate changes were responsible for 71.4% and 28.6% of the net losses, respectively.

We investigated the stability of the remaining species range and found that half of the nature reserves are likely vulnerable, with 57.1% and 66.7% of them likely to lose their conservation value in 2050 and 2070, respectively. To effectively protect this salamander, conservation policies should address both pressures simultaneously,

Introduction

The species ranges and abundance of wildlife have been declining for centuries in many parts of the world because of habitat fragmentation, degradation and loss due to anthropogenic threats such as land use change (e.g., urbanization and intensive forestry), invasive species, aquatic pollution, and emerging diseases, with amphibians experiencing greater impacts than most other groups [START_REF] Wake | Are we in the midst of the sixth mass extinction? A view from the world of amphibians[END_REF]Vredenburg 2008, Yan et al. 2018). Climate change represents yet another threat to wild populations, and the current and future effects of climate change are likely to exacerbate existing stressors [START_REF] Hof | Additive threats from pathogens, climate and land-use change for global amphibian diversity[END_REF]. Amphibians are currently considered the most threatened class of vertebrates worldwide, with approximately one-third of all species imperiled [START_REF] Stuart | Status and trends of amphibian declines and extinctions worldwide[END_REF]). Amphibians, specifically salamanders, are particularly vulnerable to the effects of climate change and pressures from human activities due to their restrictive physiological requirements and low vagility [START_REF] Barrett | Differential responses of amphibians and reptiles in riparian and stream habitats to land use disturbances in western Georgia, USA[END_REF].

Unlike some species that have dispersal capabilities allowing them to track shifting environmental envelopes, amphibians will experience range contractions when parts of their range no longer exhibit climatic patterns suitable for the maintenance of stable populations [START_REF] Araújo | Climate warming and the decline of amphibians and reptiles in Europe[END_REF]). In addition, with respect to ongoing habitat destruction, the threats of climate change and other possible human pressures likely are not currently apparent; however, they represent the greatest threats to the persistence of populations in the future [START_REF] Corn | Climate change and amphibians[END_REF]. Therefore, it is necessary to understand how the species ranges of vulnerable amphibians will respond to future perturbations, including both climate change and human pressures, to ensure long-term conservation.

Correlative species distribution models (SDMs) that have practical advantages in predicting the impacts of changing environments [START_REF] Fraser | Integrated models to support multiobjective ecological restoration decisions[END_REF][START_REF] Gallego-Zamorano | Combined effects of land use and hunting on distributions of tropical mammals[END_REF]) have been widely used for the conservation management of land, forest, river and marine ecosystems due to their simplicity and flexibility [START_REF] Growns | Toward river health assessment using species distributional modeling[END_REF][START_REF] Marshall | Species distribution modelling to support marine conservation planning: The next steps[END_REF][START_REF] Booth | Species distribution modelling tools and databases to assist managing forests under climate change[END_REF][START_REF] Khoury | Modelled distributions and conservation status of the wild relatives of chile peppers[END_REF].

For example, SDMs have been used to examine the effects of climate change on the future spatial distribution of protected areas , identify areas of high conservation value for endangered species [START_REF] Wilson | Applying species distribution modelling to identify areas of high conservation value for endangered species: A case study using Margaritifera margaritifera (L.)[END_REF], guide the search for poorly known species [START_REF] Fois | Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions[END_REF], model the distributions of threatened species in protected areas [START_REF] Pěknicová | Application of species distribution models for protected areas threatened by invasive plants[END_REF], predict the invasion risks of alien species in local areas [START_REF] Bae | Temperature and hydrologic alteration predict the spread of invasive Largemouth Bass (Micropterus salmoides)[END_REF][START_REF] Dong | Spatial pattern and determinants of global invasion risk of an invasive species, sharpbelly Hemiculter leucisculus (Basilesky, 1855)[END_REF]) and support multiobjective ecological restoration decisions for endangered species [START_REF] Fraser | Integrated models to support multiobjective ecological restoration decisions[END_REF]. Although the use of SDMs for nonequilibrium species distributions under contemporary climate conditions is sometimes considered precarious [START_REF] Fitzpatrick | The projection of species distribution models and the problem of non-analog climate[END_REF], attempts have been made to reduce modeling uncertainties through novel techniques [START_REF] Kearney | Correlative and mechanistic models of species distribution provide congruent forecasts under climate change[END_REF]. These techniques include ensemble methods that average across many different modeling approaches [START_REF] Hao | A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD[END_REF] and data integration methods based on multiple sources [START_REF] Isaac | Data Integration for Large-Scale Models of Species Distributions[END_REF]). To date, SDMs have been widely explored to project the current and future distributions of salamanders in the context of climate change for local conservation efforts (Werner et al. 2013, Lyons and[START_REF] Lyons | Vanishing islands in the sky? A comparison of correlation-and mechanism-based forecasts of range dynamics for montane salamanders under climate change[END_REF]. However, research on the combined impacts of future climate change and human pressures, especially in areas where human pressures affect increasingly large regions and where market forces drive the overexploitation of salamanders, is limited.

The Chinese giant salamander, Andrias davidianus (CGS), which is the world's largest amphibian and is endemic to China, is categorized as critically endangered on the IUCN Red List and is a priority for international amphibian conservation because of its extremely unique evolutionary history [START_REF] Isaac | Phylogenetically-informed priorities for amphibian conservation[END_REF]. Once thought to be widespread throughout most of China, this species has been declining in the wild because of human-induced habitat destruction and harvesting for luxury food and is currently critically endangered at the national level [START_REF] Yan | The Chinese giant salamander exemplifies the hidden extinction of cryptic species[END_REF]. Effective conservation measures, such as the establishment of nature reserves, have been implemented to protect wild populations. However, conservation interventions are hindered by limited range-wide data because this species is difficult to detect, and only site-level data are available for cryptic species that occur across large geographic areas [START_REF] Chen | Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus)[END_REF]. Since large-scale surveys remain impossible for this species, SDMs provide promising tools with which to explore the range of the giant salamander, which is considered a priority activity for spatial conservation planning [START_REF] Isaac | Phylogenetically-informed priorities for amphibian conservation[END_REF]. A recent study revealed that future climate change will severely reduce habitat suitability for the CGS [START_REF] Zhang | Future climate change will severely reduce habitat suitability of the Critically Endangered Chinese giant salamander[END_REF]; however, human pressures that can considerably influence species range shifts [START_REF] Turvey | Imminent extinction in the wild of the world's largest amphibian[END_REF] were not considered in the modeling work. In the present study, we explored and quantified the combined impacts of future climate change and human pressure on the CGS distribution. The spatial range shifts in response to the independent and superimposed impacts of these factors were mapped and analyzed, and the near-future sustainability of nature reserves was evaluated. This research can provide insight into the conservation management of the world's largest salamander in both the current period and future periods.

Methods

Species occurrence and conservation status

With a length of two meters and weight of 60 kg, the CGS is the largest recognized extant species of amphibian; it is one of three living cryptobranchid species, which diverged from other amphibians during the mid-Jurassic period.

Historically, the species was widely distributed throughout most parts of China; however, it currently mainly lives in central and southern China in three major basin systems: the Yellow, Yangtze, and Pearl River basins (Fig. 1). The sharp decline in population size over the past two decades was largely due to the loss of primary habitat and consumption by humans [START_REF] Chen | Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus)[END_REF], Yan et al. 2018). The species was awarded 'Class II' protection under the Chinese Conservation Law in 1988, and it is currently listed as critically endangered by the IUCN and in Appendix I of CITES.

As a conservation measure, several municipal, provincial and national nature reserves have been established, and artificial breeding has been encouraged by the government as a possible conservation measure.

In this study, occurrence data were compiled from three resources: (1) the Global Biodiversity Information Facility (GBIF; http://www.data.gbif.org/) data set; (2) an open-source geodatabase that lists the nature reserves in China (https://www.osgeo.cn/data/); and (3) literature that surveyed and reviewed CGS distribution sites [START_REF] Wen | Discussion on the geographical distribution of wild Chinese giant salamander[END_REF], Turvey et al. 2018). The time span of these data covers the second half of the 20th century, when the CGS was widely distributed in China, to the early 21st century. After removing duplicate and apparently erroneous presence records (e.g., records outside of China and terrestrial records far from water), 259 records remained, including records in 23 provincial and national reserves (Supporting Information Table S1). To diminish the effect of spatial autocorrelation originating from the spatial clustering of presence records (i.e., more than one presence record in one environmental grid cell with a 30 arc-second spatial resolution, ca. 1.0 km 2 at the equator), a spatial thinning or rarefying approach similar to that used by [START_REF] Boria | Spatial filtering to reduce sampling bias can improve the performance of ecological niche models[END_REF] was applied to our presence dataset, selecting at most one record in each environmental grid cell [START_REF] Feng | Collinearity in ecological niche modeling: Confusions and challenges[END_REF]. Correspondingly, 253 presence records were finally retained. Since presence-absence algorithms are known to perform better than presence-only algorithms [START_REF] Elith | Novel methods improve prediction of species' distributions from occurrence data[END_REF]) and true absence records for the CGS at a national scale are impossible to confidently identify, we instead generated 253 pseudo-absence records spanning the whole country and its adjacent areas using a randomization method conditioned by excluding pixels where presences are known [START_REF] Barbet-Massin | Selecting pseudo-absences for species distribution models: how, where and how many?[END_REF]. Afterwards, we combined these pseudo-absence records with those retained presence records, and then shuffled the whole dataset to obtain the ultima species presence and pseudo-absence dataset for posterior analysis [START_REF] Senay | Novel three-step pseudo-absence selection technique for improved species distribution modelling[END_REF].

Predictor variable selection

The choice of predictor variables can have a strong effect on the quantification of the realized niche and therefore on SDM transferability in time and space [START_REF] Peterson | Transferability and model evaluation in ş ecological niche modeling: a comparison of GARP and Maxent[END_REF]). We initially selected nineteen climate variables and eleven non-climate variables considered ecologically meaningful for the spatial distribution of this species as candidate predictors (Table 1). The climate variables are commonly used bioclimatic variables (BIO1-BIO19) in the field of ecological niche modeling [START_REF] Hijmans | Very high resolution interpolated climate surfaces for global land areas[END_REF]) from the WorldClim database (http://www.world clim.org), including eleven temperature-related variables and eight precipitation-related variables. The eleven nonclimate variables were land environmental and human-related factors, including land elevation (LAE), waterbody area (WTA), flood hazard frequency (FHF), cropland area (CLA), development pressure index (DPI), human population density (HPD), human population count (HPC), human influence index (HII), human impervious area percentage (HIP), river fragmentation index (RFI), and dam density (DD). Detailed information about these data can be accessed through the sources provided in Table 1.

We used two steps to select the predictor variables. First, the variance inflation factor (VIF) for each candidate predictor variable was calculated, and the predictor variables with VIF values greater than 10 were removed to avoid overfitting and eliminate the effects of collinearity (De Marco and Júnior 2018). Based on this criterion, fifteen climate variables and two non-climate variables, LAE and HPC, were excluded, with temperature seasonality (BIO4, TS), mean temperature in the coldest quarter (BIO11, MTCQ), precipitation seasonality (BIO15, PS), precipitation in the warmest quarter (BIO18, PWQ) and other non-climate variables retained for further selection. Then, we performed an initial round of modeling using the remaining variables and checked the contribution and the shape of the response curve of each variable. Based on variable importance and reasonability criteria, the variables were further selected. The variables with contribution less than 0.01 were removed (i.e., FHF, CLA, DPI and HII). However, two variables with high contribution values, RFI and DD, were left out because their response curves showed that they were unreasonably positively correlated with occurrence probability. Consequently, seven variables, namely, TS, MTCQ, PS, PWQ, WTA, HPD and HIP, were finally selected and used for model projection and prediction in the present study.

Ensemble species distribution modeling and prediction

Ensemble models are often considered superior to single algorithms because their data for which were obtained from WorldClim 1.4 (http://www.world clim.org). The projections were predicted under three representative concentration pathways (RCP 2.6, 4.5 and 8.5) from three widely used global circulation models (GCMs)

(BCC-CSM1-1,

Quantifying the impacts on species range shifts

The results of species distribution modeling represent the probabilities of species occurrence or habitat suitability, and they include some noise and background information. However, in conservation and environmental management practices, information presented based on species presence/absence may be more practical than that presented as a probability or suitability. Therefore, a threshold is needed to transform probability or suitability data to presence/absence data [START_REF] Liu | Selecting thresholds of occurrence in the prediction of species distributions[END_REF]).

This study takes the mean value of the predicted probabilities of species presence, which is regarded as biologically meaningful [START_REF] Cramer | Logit models from economics and other fields[END_REF], as the threshold to transform the projected habitat suitability maps to binary maps, with 1 and 0 representing species presence and absence areas, respectively. Species range shifts in response to climate change and HPD change were evaluated by calculating the difference between the species ranges projected for future and current statuses, with the resulting values of 1, 0, and -1 representing species range expansion, retention and loss, respectively. The areas of total range loss (TRL) and total range expansion were calculated by summing the areas of the grids with values of -1 and 1, respectively.

Therefore, the TRL change and net range loss (NRL) change relative to the species range area in the current period were calculated by

, = ∆ , × 100% (1) , = ∆ , ∆ , × 100% (2)
where ∆A , and ∆A , are the areas of TRL and range expansion, respectively;

, and , are the percentages of TRL and NRL, respectively;

A is the range area in the current period; and the subscript f can be p, c, or pc, which ./00, 1 2 33, > 1 × 50% 6&(7, 1 89 :3! , > 1 × 50% $%&');, 1 <8=:! , ≥ 1 -1 @A)7;B&');, /%ℎ;B ;)0;

, = ∆ , ∆ , ∆ , = , , , (3)
(8

)
where 1 is the total number of climate change scenarios (N=9) and 1 2 33 ,

Results

Model performance

The predictive ability of the four modeling algorithms was consistently excellent, with AUC values ranging from 0.89 to 0.98 and TSS values ranging from 0.72 to 0.89 for all the basal models (Fig. 2). RF and GBM were the two best predictive algorithms, with median AUCs of 0.952 and 0.948 and TSSs of 0.815 and 0.827, respectively. Among the 40 basal models, 35 models with high AUCs and TSSs were used to develop the ensemble SDMs. The ensemble model surpassed all of the individual algorithms, with the highest AUC and TSS values of 0.98 and 0.86, respectively (Fig. 2).

Predominant predictors

The model results indicated that MTCQ, HPD, PWQ, and TS were the most predominant factors affecting the range of the CGS, with importance values of 0.288±0.045, 0.249±0.053, 0.186±0.031, and 0.125±0.067, respectively, whereas PS (0.041±0.008), HIP (0.026±0.005) and WTA (0.018±0.004) played less pivotal roles. Notably, the contribution of HPD to the CGS distribution was nearly equivalent to or greater than the contributions of the other climate variables. The shapes of the response curves of different algorithms for the four most important variables were similar to each other (Fig. 3). More specifically, the occurrence probability of the CGS showed a unimodal response to TS, MTCQ and PWQ, with optimal ranges for the presence of the CGS of approximately 6~10°C, -4~9°C and 400~950 mm, respectively. However, occurrence probability showed a decreasing trend with increasing HPD, suggesting that the species distribution is mainly restricted to areas with HPD lower than approximately 400 people per km 2 .

Species distribution shifts

The mean value of suitability based on all the occurrence data calculated from the ensemble model was 0.89, and it was used as the threshold for species presence or absence. The species was shown to be distributed mainly in the Yangtze River Basin (Fig. 1), and the total area of the species range was 1.

Species range loss

Although some gains in the species range were observed under all scenarios, overall reductions were identified throughout the area. Table 2 shows the average percentages of TRL and NRL, combined impacts of climate and HPD changes in 2050 and 2070 (the range losses under all scenarios are shown in Supplementary Table S2).

The TRL and NRL exhibit a slight increase in 2070 compared with 2050 under the impact of HPD change. Compared to current conditions, HPD change would lead to a significant reduction in the species range, with the mean percentage of TRL being 44.2% in the two periods. Although the species range would increase in some regions (Fig. 4a,4b), the negative impacts of HPD change would dominate, ultimately with an average NRL of 16.7% in 2050 and 2070.

Generally, both TRL and NRL in the HPD change scenarios were higher than those in the climate change scenarios (18.1% and 6.7% on average for TRL and NRL, respectively), indicating that HPD change would likely have a more negative impact on the species range. Aggravated loss of the species range was observed under the combined impacts of HPD and climate changes (52.5% on average for TRL).

However, 9.7% of the TRL would result from the overlapping effect of HPD and climate changes. Under the combined impacts of HPD and climate changes, 65.6%, 16.0% and 18.4% of the TRL resulted from solely HPD change, solely climate change and their overlapping impacts (calculated by Eqs. ( 3)-( 5)), respectively. Overall, the NRL decreased by 23.4% with HPD change, and climate change explained 71.4% and 28.6% of this loss (calculated by Eqs. ( 6)-( 7)).

Stability of nature reserves

The stability maps of the species range under the impacts of climate change with and without HPD changes in 2050 and 2070 are shown in Figure 6. Climate change results in the loss of habitat along the boundaries, with the discontinuous mixing of vulnerable and newly colonized habitats. After integrating the impacts of HPD changes, the extensive stable species range was reduced and fragmented. Based on these maps, the stability of each national and provincial nature reserve site under the independent or combined impacts of HPD and climate changes was evaluated. Habitat stability changes over time and under different scenarios are clearly shown in Table 3.

Importantly, two reserves are not recognized as suitable habitat under both current and future environmental conditions. A total of 57.1% of the existing reserves suitable for CGS protection would lose suitable habitat in both 2050 and 2070 in response to future HPD changes. Climate change would cause 23.8% and 28.6% of the reserves to be vulnerable in 2050 and 2070, respectively, and 4.3% and 9.5% of the reserves would lose their habitat. Under the combined impacts of climate and HPD changes, 14.3% of the reserves would become vulnerable and 57.1% and 66.7% of the reserves would likely lose their conservation value in 2050 and 2070, respectively. However, four of the existing reserves would remain stable under the impact of future climate change and HPD change (i.e. Zhangjiajie National Nature Reserve, Xishui National Nature Reserve, Fanjingshan National Nature Reserve, and Luonan Provincial Nature Reserve of CGS).

Discussion

Factors influencing species range shifts

Climate change impacts are likely to be profound for amphibians because they are ectothermic with life cycle and life history attributes that are tightly linked to climate [START_REF] Bartelt | Modeling amphibian energetics, habitat suitability, and movements of western toads, Anaxyrus (= Bufo) boreas, across present and future landscapes[END_REF]. As ectotherms, amphibians are highly dependent on ambient thermal environments, which influence the physiology, locomotor performance, behavior, habitat use and range-wide distribution of species via their effects on body temperature. This study suggests that the temperature in the coldest quarter and precipitation in the warmest quarter primarily form the climate niche of the CGS. Similar to most amphibians, this species needs to hibernate, which provides protection from cold environments and avoids problems related to food unavailability.

Although warming temperatures in winters would provoke earlier emergence from hibernation than usual, increasing temperatures should result in increased energetic expenditure, decreased growth rates, and decreased resource allocation during overwintering periods [START_REF] Caruso | Widespread rapid reductions in body size of adult salamanders in response to climate change[END_REF]). These pressures from warming winters may drive the species to move toward cool regions. This species feeds mainly on fish and crustaceans, and its reproductive season is in June and July, when temperatures are warm. As the quantity and temporal patterns of precipitation play an important role in the productivity and species richness of river ecosystems by shaping hydroecological processes [START_REF] Grimm | The impacts of climate change on ecosystem structure and function[END_REF], sufficient precipitation could provide enough food and a suitable water environment for successful reproduction. Recent research has shown that variation in seasonal climatic conditions also strongly influences the life history, phenology, and geographic location of salamanders [START_REF] Kirk | Seasonal differences in climate change explain a lack of multi-decadal shifts in population characteristics of a pond breeding salamander[END_REF]).

However, our research indicates that future HPD changes will be mainly responsible for the range loss of the CGS. Salamanders possess a variety of characteristics (e.g., cutaneous respiration and low vagility) that make them sensitive to environmental modifications, such as stream alterations and urbanization [START_REF] Price | Effects of urbanization on occupancy of stream salamanders[END_REF]. The range loss and fragmentation due to HPD changes in both 2050 and 2070 could result from future urban expansion with the economic development of a large number of small and medium-sized cities. As urbanization progresses, land-cover patterns change, and the habitats of species can be destroyed and shrink, especially their wild habitats. The urbanization of catchments not only modifies stream hydrology but also contaminates water [START_REF] Price | Effects of urbanization on occupancy of stream salamanders[END_REF], which could expose the species to an unsuitable environment, as it prefers living in fast-flowing and clean water [START_REF] Chen | Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus)[END_REF]). In addition, growing human populations are likely to have increased consumption demands for this species, which is widely believed to provide precious nutrients. A deeper impact of future HPD change revealed in this study is habitat fragmentation, which could disrupt movement between formerly contiguous habitats [START_REF] Marsh | Forest roads as partial barriers to terrestrial salamander movement[END_REF], decrease genetic diversity among populations and ultimately contribute to long-term extinction [START_REF] Noël | Impact of urban fragmentation on the genetic structure of the eastern red-backed salamander[END_REF]. The finding that human pressures are primarily responsible for the range losses of the CGS is consistent with the current belief that the decline in the wild population is mostly attributed to overexploitation.

Our study reveals that other two non-climate variables, WTA and HIP, are not most important (variable importance are less than 0.03) for CGS distribution compared with climate variables and HPD. We treated them as unchanging in future prediction as their reliable projections are not available. Actually, the use of static non-climate variables (e.g., slope, soil type and land use/land cover) when predicting climate change impacts in SDM studies is widely accepted [START_REF] Peterson | Future projections for Mexican faunas under global climate change scenarios[END_REF], and previous work revealed that models including both static variables and dynamic variables perform better than or as well as those either masking or excluding the static variables [START_REF] Iverson | Predicting abundance of 80 tree species following climate change in the eastern United States[END_REF]Prasad 1998, Stanton et al. 2012). Therefore, although we considered WTA and HIP as static in our study, the future range shifts of the species would be hardly modified. However, it should be noted that if a non-climatic variable is dominate for species distribution, such as HPD in our study, the future dataset should be used in future prediction to ensure model transferability. In addition, to improve model reliability and thereby model transferability in space and time, selection of model testing and evaluation scheme and spatial autocorrelation checking of the observation and environmental predictors should be particularly concerned.

This point is of vital importance not only for presence-only models, but also for presence/absence models used in our study, and should paid more attention in our future work.

Implications for conservation

The threatened CGS is a global conservation priority, but identification of targeted conservation activities for this species is often impeded by limited baseline data on its status and distribution [START_REF] Fellowes | Can science in China do more for conservation?[END_REF]). Since the 1980s, nature reserves have been established in many provinces to protect wild CGS populations.

However, some of the reserves have experienced inefficient management or increased human access to the salamander's habitats [START_REF] Pan | Using local ecological knowledge to assess the status of the Critically Endangered Chinese giant salamander Andrias davidianus in Guizhou Province, China[END_REF]. Our research found that over half of the existing reserves would be unsuitable for the CGS under the combined pressures of climate and HPD changes. If further protection is not implemented, some of the reserves will lose their functionality and value for the conservation of the species. We show the stability shifts of each reserve under the independent impacts of climate change and human pressures (Table 3), and these results could be applied to develop specific mitigation strategies for different reserves to maintain sustainability for the conservation of the CGS. Our study reveals that human pressures will be primarily responsible for the future range losses of this species. This pattern is consistent with the wild population decline being mostly attributed to overexploitation. Therefore, a public information campaign aiming to better educate local inhabitants regarding the status and plight of this important endemic species should be developed, and stricter legislation should be implemented by the government to prohibit the harvesting of wild CGSs. In addition, relevant agencies and bureaus should coordinate and cooperate to ensure effective management. Only in these ways can the largest salamander in the world have long-term and sustainable conservation.

In recent decades, CGS conservation activities have mainly involved the government-supported release of farmed salamanders. However, this strategy may be harmful to wild populations by mixing genetic lineages and spreading pathogens, thereby driving extinction via genetic homogenization [START_REF] Turvey | Imminent extinction in the wild of the world's largest amphibian[END_REF]. Therefore, it is still imperative to enhance conservation strategies to protect the wild habitat and populations of the CGS. The identification of targeted conservation activities for this species is often impeded by limited baseline data on its status and distribution [START_REF] Fellowes | Can science in China do more for conservation?[END_REF]). The species range shifts over space and time predicted by this research could help guide long-term surveys throughout the species' range, detect undiscovered populations and key habitats, understand the life history and genetic evolution of the species, and protect this large and ancient salamander from extinction.

Conclusion

This study used a set of SDMs and an ensemble model approach to predict range Table 3. Stability changes of the CGS reserves under the climate change and HPD change 724 scenarios. S: stable; V: vulnerable; L: lost habitat. The two reserves with bold font do not 725 contain CGS habitatn in current and future conditions, and the four reserves with gray 726 backgrounds would remain stable under future scenarios. Detailed information on the reserves 727 can be found in Supplementary Table S1.

  represent future scenarios impacted by only HPD change, only climate change or their combined changes, respectively. The contributions of the independent and overlapping impacts of HPD and climate change to range loss were calculated by

  where, , , , and , represent the range loss change that was driven by only HPD change, only climate change and overlapping impacts, respectively, and ∆A , and , represent the overlapping TRL and the percentage of the loss that was negatively impacted by both climate change and HPD in the scenario considering the combined impacts, respectively. The separate overall impacts of HPD change and climate change where !" , and !" , represent the negative impacts of HPD change and climate change on species range loss, respectively. The stability of each grid area under the impact of climate change with and without HPD change was evaluated by

  1 89 : 3! , and 1 <8=:! represent the numbers of climate change scenarios under which the species range is lost, expands, and is retained in all climate change scenarios, respectively. The grid areas in which no range loss, retention or expansion would occur in over half of the scenarios were regarded as vulnerable to future environmental change. Specifically, the ability of habitats in the provincial and national nature reserves to sustain the presence of this species under the impacts of climate change and human pressures was explored based on the evaluation of the stability of the species range.Landscape indices were used to evaluate the impacts of climate change and HPD change on species distribution patterns. The dynamics of habitat patterns within a landscape are normally defined as habitat fragmentation, a process whereby a contiguous patch of habitat is transformed into a number of smaller and disjunctive patches[START_REF] Cumming | Statistical models of landscape pattern metrics, with applications to regional scale dynamic forest simulations[END_REF]. We used patch density and edge density to assess range distribution changes for the CGS. These variables are recognized as effect indices with which to evaluate habitat fragmentation[START_REF] Wang | Measuring habitat fragmentation: An evaluation of landscape pattern metrics[END_REF]) and are given by[START_REF] Mcgarigal | Spatial pattern analysis program for categorical and continuous maps[END_REF]) patch density (/km 2 ), FD is edge density (m/km 2 ), E is the total range area (m 2 ), 7 is the number of species range patches, and ; H is the total length (m) of edges for the Kth patch. As patch density and edge density measure the degrees of range aggregation or conjunction, higher values of both indices represent greater fragmentation of the species range. All of the above calculations were performed in the open-source statistical software R 3.6.1.

  11×10 6 km 2 . The spatial distribution shifts are shown in Figure4a-f. In the HPD change scenarios (Fig.4a, 4b), the species range obviously expanded toward the north, and a large part of the habitat moved to a previously less suitable area in both 2050 and 2070. The lost distribution areas were widely and discontinuously distributed throughout the area affected by future HPD change. In contrast, both the loss and gain of distribution areas were mainly observed along the internal and external boundaries of the current range under the future climate change scenarios (Fig.4c, 4d). Most of the habitat loss occurred in the northern and eastern regions, and habitat expansion mainly occurred in the southern region. Under the combined impacts of climate and HPD changes, the spatial distribution of the losses and gains was similar to that in the solely HPD change scenario, but both the lost and expanded ranges increased (Fig.4e, 4f).Habitat fragmentation analysis showed that the patch density and edge density of the species range obviously increased by 35.7% and 17.7% on average, respectively, in both 2050 and 2070 under the impact of HPD change (Fig.5). Climate change fragmented the species range, with the patch density and edge density slightly increasing by 8.8% and 3.4%, respectively, on average. Under the combined impacts of HPD and climate changes, species range fragmentation still noticeably increased compared with that under current conditions, despite the edge density being reduced compared with that in the HPD change scenario. These results indicate that although both climate change and HPD change would have negative impacts on the species range of the CGS, HPD change would likely be the dominant factor driving future distribution shifts and range loss.

  shifts of the world's largest salamander under the single and combined impacts of future changes in climate and HPD. The results reveal that the ensemble model has the best performance in forecasting the distribution of the CGS and that HPD is almost as important as climate factors in driving the distribution of this endangered species. Future predictions revealed that HPD changes would play the most important role in species range loss and noticeably fragment the habitat compared to the effects of climate change. In addition, over half of the existing nature reserves are likely to lose their suitable habitat under the combined impacts of climate change and HPD change. In light of this, reliable prevention and management strategies should be formulated and implemented, in which lessening the impacts of human-related pressures should be especially addressed. The results provide baseline data that can be used to help protect the wild habitat and populations of this species. Overall, the approach used in this work might provide a valuable reference for research on other endangered species aiming to predict and evaluate future range shifts, especially that on species whose habitat and survival have been strongly affected by

  Figures

  Figure 1. Locations of provincial and national conservation reserves,

Figure 5 .

 5 Figure 5. Comparison of habitat fragmentation indices between different

  CCSM4, and MIROCESM-CHEM) in Asia. As no datasets are available for future HIP and WTA, we kept these two variables static in the future predictions. Future HPD was obtained from global downscaled population projection grids at a resolution of 1 km for 2050 and 2070[START_REF] Jones | Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways[END_REF]. This dataset

is a downscaled version of the Global One-Eighth Degree Population Projection Grids that is both quantitatively and qualitatively consistent with shared socioeconomic pathways (SSPs). SSPs were developed to support future climate and global change research and the IPCC Sixth Assessment Report (AR6)

[START_REF] Gao | Global Population Projection Grids Based on Shared Socioeconomic Pathways (SSPs), Downscaled 1-km Grids[END_REF]

).

Table 2 .

 2 Changes in the total range loss (TRL) and net range loss (NRL) (%) for the CGS under the independent and combined impacts of human population density (HPD) change and climate change (CC)

	Loss type	Scenarios	2050	2070	Mean
		HPD	42.3	46.0	44.2
	Total	CC	17.9	18.3	18.1
	range loss	Combined	51.0	54.1	52.5
		Overlap	9.2	10.2	9.7
	Net range loss	HPD CC Combined	15.2 7.1 22.3	18.2 6.2 24.4	16.7 6.7 23.4

Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (No. 51709187), the Key Projects in Technology Innovation of Hubei Province, China (No. 2019ACA154) and the China Scholarship Council (CSC). The Evolution et Diversité Biologique laboratory was supported by 'Investissementd'Avenir' grants (CEBA, ref. ANR-10-LABX-0025; TULIP, ref.

ANR-10-LABX-41). We thank two anonymous reviewers for their constructive suggestions that greatly improved our work.

Acknowledgments

This study was financially supported by the National Natural Science Foundation of China (No. 51709187), the Key Projects in Technology Innovation of Hubei Province, China (No. 2019ACA154) and the China Scholarship Council (CSC). The 'Investissementd'Avenir' grants (CEBA, ref. ANR-10-LABX-0025; TULIP, ref.

ANR-10-LABX-41). We thank two anonymous reviewers for their constructive suggestions that greatly improved our work.

https://www.worldclim.org/data/worldclim21.html Nonclimate LAE Land Elevation, extracted from STRM DEM Collinearity http://srtm.csi.cgiar.org/download WTA Waterbody Area Noncollinear and important http://sedac.ciesin.columbia.edu/data/sets/browse FHF Flood Hazard Frequency Unimportant http://sedac.ciesin.columbia.edu/data/sets/browse CLA Cropland Area Unimportant http://sedac.ciesin.columbia.edu/data/sets/browse

combined framework can reduce the uncertainties of individual algorithms and provide more robust and reliable projections [START_REF] Grenouillet | Ensemble modelling of species distribution: the effects of geographical and environmental ranges[END_REF]. We opted to use four different algorithms that have been frequently applied in the field of ecological niche modeling to establish the basal models required by the ensemble model. These algorithms were a generalized linear model (GLM), generalized boosting model (GBM), random forest (RF) model and multiple adaptive regression splines (MARS) model, and all these algorithms were fit using the default settings of the biomod2 package [START_REF] Thuiller | BIOMOD-a platform for ensemble forecasting of species distributions[END_REF] in the open-source statistical software R 3.6.1. A random 70% of the total presence and absence records were selected as the training set to calibrate the algorithms; the remaining 30% were withheld for evaluating algorithm performance. This process was replicated 10 times to account for individual algorithm variabilities, avoid bias from the dataset split, and add rigor to the results.

Model performance was assessed based on two metrics: the area under the receiver operating characteristic curve (AUC) and the true skill statistic (TSS). Basal models with TSS values over 0.75 and AUC values over 0.90 were selected to develop committee-averaged ensemble models and ensure that the developed ensemble model had the optimal predictive ability for occurrence probability [START_REF] Guisan | Predicting species distributions for conservation decisions[END_REF]. The ensemble models were used to predict habitat suitability for the CGS under current climate conditions and the independent and combined impacts of future climate and HPD changes.

We generated projections for the current climate and future climates for 2050 (average for 2041-2060) and 2070 (average for 2061-2080), the

Tables

Table 1 Description of tested and selected variables with the selection and exclusion criteria. Variables in bold font were selected for projection and prediction in this study.


Francesco Ceraudo
email: francesco.ceraudo@cea.fr

Aline Meuris

Eric Doumayrou

Luc Dumaye

Alain Goetschy

Tony Lavanant

Michel Lortholary

Isabelle Le Mer

François Nico

Frédéric Pinsard

Marin Prieur

Diana Renaud

Benjamin Schneider

Thierry Tourrette

François Visticot

Norbert Meidinger

Danilo Mießner

Diego Götz

Characterization of the detection chain of the Micro-channel X-ray Telescope

Keywords: SVOM, MXT, pnCCD, X-ray Spectroscopy, Space Instrumentation

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The Space-based multi-band astronomical Variable Object Monitor (SVOM) is a Chinese-French space astronomy mission for the study of Gamma-Ray Bursts (GRBs) and other transient astrophysical phenomena [START_REF] Wei | The Deep and Transient Universe in the SVOM Era: New Challenges and Opportunities -Scientific prospects of the SVOM mission[END_REF]. To be launched at the end of 2021, SVOM will achieve high sensitivity from MeV photons to Near Infra-Red wavelengths. This will allow the detection of all types of GRBs, for which it will provide fast and reliable positions as well as spectral and temporal evolution.

Part of the French contribution to the scientific payload of the satellite, the Micro-channel X-ray Telescope or MXT [START_REF] Mercier | MXT instrument on-board the French-Chinese SVOM mission[END_REF], depicted in Fig. 1, will be the first instrument for space astronomy to mount Micro-Pore Optics (MPOs) arranged in a lobster-eye configuration. The optics have been developed and built by the University of Leicester, using MPO plates from Photonis France [START_REF] Feldman | Testing and modelling of the SVOM MXT narrow field lobster-eye telescope[END_REF]. With a 1.15 m focal length, the telescope will operate in the 0.2 -10 keV range and feature a 10 arcmin angular resolution along with an effective area of 23 cm 2 at 1 keV. MXT will have a twofold goal: [START_REF] Wei | The Deep and Transient Universe in the SVOM Era: New Challenges and Opportunities -Scientific prospects of the SVOM mission[END_REF] to provide < 2 arcmin source localization accuracy in less than 10 min for > 90 % of GRBs; [START_REF] Mercier | MXT instrument on-board the French-Chinese SVOM mission[END_REF] to study the afterglow emission with an energy resolution of ∼ 80 eV at 1.5 keV.

To ensure its objectives, the focal plane of MXT will be equipped with a 450 µm-thick fully-depleted frame-store Charge-Coupled Device entirely based on pn-junctions (pnCCD). Heritage of XMM/EPIC-pn [START_REF] Strüder | The European Photon Imaging Camera on XMM-Newton: The pn-CCD camera[END_REF] and provided by the Max Planck Institute for Extraterrestrial Physics (MPE), it was originally developed as one of the earlier detector models for the eROSITA mission [START_REF] Meidinger | Next generation of pnCCDs for X-ray spectroscopy and imaging[END_REF]. Composed of a matrix of 256×256 75 µm-side pixels, the pnCCD is read out by two CAMEX 128MJD ASICs (ENC ≈ 2 e - rms ) [START_REF] Herrmann | Mixed signal pnCCD readout ASIC for the future X-ray astronomy mission eROSITA[END_REF][START_REF] Herrmann | Camex readout asics for pnccds[END_REF]. The device is visible in Fig. 1c.

MXT will be the first experiment to operate a pnCCD in the harsh environment of Low-Earth Orbit. This presents a highly variable thermal radiative background, dominated by periodic occultations and Earth's infrared and albedo emissions. In addition, the orbit features intense particle radiation, due to the fact that the SVOM satellite will cross several times per day the South Atlantic Anomaly, a region with high concentration of protons and electrons trapped in the Earth's magnetic field. The design of MXT is further affected by its mass (35 kg) and power (50 W) budgets, very tight when compared to instruments with similar purposes such as SWIFT/XRT, with its mass of ≈ 200 kg [START_REF] Burrows | The Swift X-Ray Telescope[END_REF]. All those constraints force the MXT detector to function at ≈ -65 • C, a far higher temperature than the -90 • C of missions such as XMM/EPIC-pn and eROSITA, both of which use similar pnCCDs.

It is therefore mandatory to study the spectral performances of the MXT detector and to verify the compliance with the scientific requirements, i.e. energy resolution of ∼ 80 eV at 1.5 keV and low-energy threshold of 200 eV.

The following sections present an overview of the working principle of the MXT detector and the subsystems allowing its operation ( § 2). Afterwards, the analysis algorithms of the data of the MXT pnCCD are detailed ( § 3).

Finally, the tests and results carried out on the Performance Model of the MXT detector are discussed ( § 4).

MXT detection chain

The MXT detector

By taking advantage of the principle of sidewards depletion [START_REF] Gatti | Semiconductor drift chamber -An application of a novel charge transport scheme[END_REF], the application of a set of voltages to the electrodes (cathodes) at the front and back sides of the detector allows the silicon bulk of the MXT Charge Coupled Device to be depleted over its entire 450 µm-thickness. Full depletion and back-illumination grant the sensor a > 90 % quantum efficiency over the 1 -10 keV range. Featuring a monolithic back-side also ensures a 100 % fill factor. The front-side of the detector is segmented into electrodes (called shift registers or phases) grouped into a matrix of pixels. Two active regions can be identified (whose purpose is defined below): an image area, made out of 256×256 pixels measuring 75 µm×75 µm (active area of 19.2 mm×19.2 mm), and a frame-store region, having 256 × 256 pixels measuring 75 µm × 51 µm.

Acquisition and readout

During the integration phase (lasting t int = 100 ms in MXT), the image region of the detector is exposed to X-rays which enter from the back-side and deposit their energy via photoelectric effect creating electron-hole clouds.

While holes are absorbed by the cathodes, electrons migrate to local potential minima under the pixels near the front-side of the pnCCD. The charges thus distributed below the pixels of the matrix collectively make up a frame. After integration, periodic changes of the electrical potential of the shift registers allow the charge packets to be moved pixel by pixel towards the bottom of the image area and into the frame-store region, which is shielded against X-rays, where they await readout. The frame shift takes t transf = 230 µs in MXT, thus reducing the probability of out-time-events2 to t int /t transf ≈ 0.2 %. The readout process occurs in 10 ms in a column-parallel fashion, meaning that no pixel serialization is present, but each CCD column is provided with its own anode and connected to one input channel of the two CAMEX ASICs (Fig. 1c). Both the pnCCD and the two CAMEX are mounted on an alumina (Al 2 O 3 ) carrier board coupled with a copper-molybdenum (MoCu) substrate and kept at the nominal operating temperature of ≈ -65 • C by an active cooling system made out of three Thermo-Electric Coolers (TECs).

Front-End Electronics

A warm Front-End Electronics (FEE, Fig. 1b) ensures: (1) power supply of the focal plane and analog-to-digital conversion of the CAMEX output;

(2) readout clocks and sequencing; (3) on-line pre-processing for photon extraction and communication with the on-board computer (the MXT Data Processing Unit or MDPU). Two modes of operation are possible: (1) in fullframe mode whole 256 × 256-pixel frames exit the FEE without treatment; in event mode the FEE output consists of a stream of hit pixels extracted via on-line pre-processing by the electronics itself (algorithms are detailed in § 3).

Due to the performance limitations of the ATMEL FPGA on the FEE, the frequency of the Spacewire link is limited to 20 MHz, which requires to slow down the data transfer rate in full frame mode, and is achieved by increasing the integration time to 200 ms (5 frame s -1 ). On the contrary, in event mode the nominal 100 ms (10 frame s -1 ) exposure time can be respected, with an output of up to 5000 hit pixels per frame.

Analysis algorithms

Written entirely in Python 3.7, the algorithms for image and spectral analysis follow the same logic as those developed by the MPE for the characterization of the eROSITA detector [START_REF] Andritschke | Data analysis for characterizing PNCCDS[END_REF]. Nonetheless, the codes have been improved to enhance robustness to outliers and execution speed, most notably via the implementation of a multi-threading mode of execution whenever possible.

The algorithms require two kinds of frames: dark frames, i.e. images in which the detector has not been exposed to X-rays, and photon frames in which the X-ray source is present. Before energy calibration is performed (to be described in § 3.3), all data are to be considered in analog-to-digital units (ADU).

Frame reduction is performed completely off line in full-frame mode ( § 2.3). In event mode, only photon extraction ( § 3.2) is carried out during the acquisition and it requires offset ( § 3.1) and threshold ( § 3.2) maps to be calculated off line and then loaded into the FEE. In every case, the spectral analysis takes place after all the data have been gathered. As a reference, in space, both offset and threshold maps will be computed on board and uploaded by the MXT Data Processing Unit.

Dark frame reduction

The voltages of the analog channels of the readout electronics determine an image-wide offset that must be subtracted 3 . Given a succession of dark frames S, so that a pixel at row i and column j on frame k may be identified as S ijk , the offset map O is defined as the pixel-by-pixel median along the temporal direction:

O ij = [S ijk ] 50 % k , (1)
where [•] 50 % k represents the median along the time axis, i.e. frame number, as frames are acquired sequentially. It is worth noting that the median has an important advantage over, say, the mean, as it is more robust against outliers, e.g. cosmic ray traces in the dark frames.

Due to the CCD's parallel readout ( § 2.2), fluctuations in the references of the analog inputs affect all the pixels in a row, while varying from row to row. This phenomenon is called common-mode noise CM and quantified as the median along each CCD row of an offset-subtracted frame:

CM ik = [S ijk -O ij ] 50 % j , (2)
where the notation retains its meaning from eq. ( 1). It is worth noting that the common-mode noise is derived separately for the two CAMEX, i.e.

two medians are calculated per row, each one over 128 pixels. Failing to take this noise source into account may negatively affect the spectroscopic performances of the detector.

After the correction of both offset and common mode, a frame is referred to as a flat frame F :

F ijk = S ijk -O ij -CM ik . (3)
Finally, the noise map N is taken as the pixel-by-pixel difference between the 84.13 %-percentile and the median (which should be zero after all the corrections) of the distribution of counts along the time axis of a succession of flat dark frames4 :

N ij = [F ijk ] 84.13 %-50 % k . ( 4
)
This approach retains the robustness to outliers of the median while at the same time staying well defined even in case of an asymmetric and/or non-Gaussian distribution, unlike other estimators such as the standard deviation.

Photon frame reduction

Flat photon frames F are obtained by applying eq. ( 3) to photon frames S, with the offset map O resulting from the analysis of dark frames and the common-mode CM from the offset-subtracted photons frames themselves according to eq. ( 2). The pixels are then compared to the noise level by applying a threshold map T equal to a multiple of the noise map N , i.e. T = nN . A pixel in position (i, j) on frame k is therefore considered to be hit if

F ijk > T ij . (5)
The value of n = 4 is used hereafter, as tests carried out by the MXT team have verified it ensures the best spectral resolution in the case under study.

This algorithm mimics the operations of the FEE in event mode and is thus representative within the wider scope of the instrument performances. The main difference between the two processes is the method by which commonmode noise is estimated, which is implemented in an approximated form in the FEE due to performance limitations of the available FPGA.

As a last step, a pattern-recognition routine groups neighboring pixels sharing at least one side and classifies them according to the XMM/EPIC convention for photon and non-photon events (this is done off line regardless of the mode of operation). As a matter of fact, photons create spherical charge clouds of dimensions smaller than (but still comparable to) the pixels and therefore can be collected by up to four pixels arranged in a square, implying that only certain configurations can be accepted as results of photon interactions. Customarily, photon events are referred to by their multiplicity, i.e. the number of pixels among which the signal is shared, hence one considers single, double, triple or quadruple events.

Energy calibration

Since each CCD column is provided with its own analog channel, energy calibration must be performed column-wise. Single events are sorted according to the readout channel and 256 uncalibrated spectra are thus calculated.

If necessary an additional low-energy threshold (spectral threshold ) may be applied to the spectra, a functionality that is found also in the final flight configuration, where the MDPU can add a second threshold to the one used for event extraction by the FEE, i.e. the extraction threshold of eq. ( 5).

Gaussian profiles are then fitted to some reference peaks of the column-bycolumn spectra and a linear calibration is performed for each CCD channel.

Both a gain and an offset are calculated, the second one being supposedly zero, unless non-linear effects are present.

CTI correction

As electron packets are transferred towards the anodes for readout, a fraction of the charge may be left behind: this phenomenon is described by the Charge Transfer Inefficiency CTI or its complement the Charge Transfer Efficiency CTE = 1-CTI. CTI affects pixels more as the number of transfers increases. To correct for this effect, after energy calibration, single events are grouped according to the CCD row and 256 row-by-row spectra are thus calculated. Line profiles are fitted similarly to the previous case. Here, however, the centers of the reference lines are then considered as a function of the number of transfers and the following function is fitted

E n = E 0 CTE n , ( 6
)
where E n is the line center after n transfers, E 0 is left as a free parameter and CTE is the Charge Transfer Efficiency. After the estimation of the CTE,

A ij = A ij E 0 Eγ CTE (E γ ) n i , (7)
where n i is the number of transfers corresponding to the i-th row and E γ is the true photon energy at which the CTE, which is itself energy dependent, is calculated.

Iterative approach

If the CTI is not properly accounted for, the gain estimation will be slightly off, which in turn affects the determination of the CTI. This is tackled with an iterative approach. ADU values are energy-calibrated and employed to evaluate the CTE, which is used to correct the original ADU data and allow for a better calibration. After that, a new relative estimate of the CTE is derived, i.e. from already CTI-corrected data, and used again to correct the original measurements. The procedure is repeated until a satisfying level of precision is reached: in practice a couple of repetitions ensure that the variation of the fitting parameters between iterations stays below the uncertainty of the fit.

Results

In the following, the results of the laboratory tests carried out on the Performance Model of the MXT detector are presented. The device is tested in a dedicated cryostat and photons are generated via an X-ray tube illuminating a composite fluorescent target, whose materials are chosen to generate a series of spectral lines sampling much of the energy range of operation of MXT (Tab. 1). A residual Bremsstrahlung emission is also present in the spectrum.

All measurements are taken at a temperature of ≈ -65 • C, representative of in-orbit thermal conditions. In line with the flight configuration, the FEE is kept at room temperature outside the cryostat. Both full-frame and event modes of operation are tested and, unless otherwise specified, the following considerations and comments apply to both.

Noise

Fig. 2 shows the histogram of the pixels of the noise map. The noise is uniform across the detector with a median of 3.2 e - rms and 99.73 % (i.e. 3σlimit) of pixels below 4.1 e - rms . A single isolated bright spot near the center of the CCD is measured, whose associated noise is 12.3 e - rms . This is is likely due to a defect on the detector itself and can be safely flagged and ignored in the analysis.

If the spectral calibration of the chain is considered (to be discussed below), the figures above translate to an average pixel-by-pixel threshold of 47 eV, with a 99.73 % upper limit at ≈ 60 eV. This is fully compatible with the 200 eV requirement on the low-energy threshold of MXT.

Intensity map

Fig. 3 shows the cumulative map of the photons detected over 2000 frames over the entire energy range generated by the X-ray tube. The response is uniform across the whole detector.

Energy resolution

Examples of the detector-wide spectra obtained using single events, double events whichever the orientation, and all events regardless of the multiplicity are shown in Fig. 4.

To calibrate single-event spectra, a model consisting of five Gaussian line profiles and a fourth-degree polynomial continuum is fitted on each columnwise and row-wise spectrum. This allows to achieve better estimation of both line centers and widths by taking into account deformations resulting from the continuous Bremsstrahlung emission of the X-ray generator.

Higher multiplicity spectra are calibrated and corrected by using CTI and spectral gain and offset estimated from single events. The spectra are then shifted by an energy-independent offset to align them with respect to the spectrum of single events. This procedure is performed to compensate for a multiplicity-dependent energy shift already observed in XMM/EPIC-pn and similarly addressed [START_REF] Valtchanov | EPIC-pn Energy Scale for Large Window Mode: long-term CTI and pattern corrections[END_REF]. Applying the shift allows to improve the energy resolution of the spectrum of all events by ≈ 20 %, so that the dominant remaining noise contribution is electronic noise. This approach to calibration is preferred to employing the same algorithm used for singles to all multiplicities because it entails a smaller number of calibration parameters, i.e. This phenomenon is likely to be the consequence of a small channeldependent non-linear response of the readout electronics. As the analysis algorithms are based on a linear spectral calibration, the aforementioned deviations are currently unavoidable and further studies are necessary to mitigate this effect, in relation to the requirement of < 20 eV-variations of the energy scale across the whole detector.

Charge Transfer Inefficiency

Estimates of the Charge Transfer Inefficiency at the five main spectral lines are presented in Tab. 4. The results are in agreement with the literature [START_REF] Meidinger | Next generation of pnCCDs for X-ray spectroscopy and imaging[END_REF], where a value of CTI (6 keV) = 10 -5 is reported.

Summary and conclusions

This paper reports on the first performance tests and characterization of the detection chain of the Micro-channel X-ray Telescope on board the SVOM mission. Despite the higher operating temperature (≈ -65 • C according to the mission baseline) that sets MXT apart from previous missions mounting similar detectors, we demonstrate an energy resolution of 82 eV at 1.49 keV on the sum spectrum built with single events in full-frame mode, as well as a < 200 eV low-energy threshold, all of which show the compliance of the measurements taken in full-frame mode with the performance requirements of the instrument. Further optimization is required to decrease the mismatch between the results taken in full-frame and event modes, in order to bring the latter closer to the mission specifications. A similar care must be dedicated to the energy scale of the detector, both from a software and from a hardware point of view, in order to reduced the measured deviations.

The detector studied in this paper is currently integrated in a camera to be mounted on the Performance Model of the whole telescope for end-to-end tests at the PANTER X-ray facility.

Figure 1 :

 1 Figure 1: The Micro-channel X-ray Telescope (MXT). a) Overview of the telescope. b) The MXT camera, housing the detector and the Front-End Electronics box. c) Close-up of the front-side of the MXT detector mounted on its carrier board: its working principle and main components are detailed in § 2.

Figure 2 :

 2 Figure 2: Histogram of the noise values. A median value of 3.25 e - rms (47 eV) is measured, with 99.73 % (i.e. 3σ-limit) of pixels below 4.10 e - rms (60 eV). A single noisy pixel is detected.

Figure 3 :

 3 Figure 3: Cumulative map of the photons detected over 2000 frames. The response appear to be uniform across the device.

Figure 4 :Figure 5 :

 45 Figure4: Spectrum of single, double and all events. The reader is referred to Tab. 1 for an identification of the spectral features, and to Tab. 2 and 3 for the values of the spectral resolution at the five most prominent lines.

Figure 6 :

 6 Figure 6: Distribution of the energy shifts across the columns at each reference energy. The solid black curve represents the median, while the inner (respectively outer) region indicates where 68 % (respectively 95 %) of the results lie. The dotted lines mark the ±20 eV interval.

Fig. 6

 6 Fig. 6 shows the distribution across all columns of the deviations of the fitted line centers of the five main spectral lines from their respective reference energies. Median shifts are always between ±20 eV from zero and 68 % of cases are always within ±30 eV. The spread of the deviations around the median is also energy-dependent, with absolute shifts reaching 70 eV for energies around 4.5 keV.

Table 1 :

 1 Composition of the fluorescence target and corresponding relevant spectral lines. Precise percentages of the materials are not known and optimization of the line intensities is completely empirical.

	Element Spectral line
		eV
	Al	1486
	Sn	3443 3772
	Ti	4509 4932
	Cr	5412 5947
	Fe	6395 7058
	Ni	7472
	Cu	8040 8905
	the pixel amplitude A

ij in ADU or keV may be corrected for this effect by putting

Table 4 :

 4 Measured Charge Transfer Inefficiency.

	Line	Energy	CTI
		eV	×10 -5
	Al-K α	1486	2.3 ± 0.8
	Ti-K α	4509	1.3 ± 0.2
	Cr-K α	5412	1.5 ± 0.2
	Fe-K α	6395	1.2 ± 0.1
	Cu-K α	8040	1.0 ± 0.1
	4.5. Non-linearity

Out-time-events are photons recorded when the charges are being shifted towards the anodes: their arrival positions are not correctly measured and they cause trails in the image along the transfer direction.

In the case at hand, the leakage current of the detector is so low that its build-up in the CCD pixels is a negligible contribution to the offset when compared to the analog channels.

The values of 84.13 % is chosen to match the standard deviation in case of a symmetric Gaussian. Indeed, given a Gaussian distribution of mean µ and standard deviation σ, the integral from -∞ to µ + σ equals 0.8413.

Acknowledgements

The authors would like to thank Karine Mercier and her instrument system team at the French space agency (CNES) for their technical, quality and financial support.

This research was carried out as part of the MXT Performance Assessment study funded by CEA and CNES and in the framework of a PhD thesis funded by CEA-Irfu (CEA Saclay).

In all cases, a 200 eV spectral threshold is imposed to the photon events, i.e. reconstructed patterns.

The Full Width at Half Maximum (FWHM) of the five most prominent lines are reported in Tab. 2 for full-frame operation and Tab. 3 for event mode. In the former case, results are compatible with the spectral energy resolution requirement of 80 eV at 1486 eV at the beginning of the mission. In the latter scenario, the compliance is missed and improvements are ongoing to achieve a better agreement with the mission requirements. Indeed, the mismatch of the spectral performances between the two modes of operation likely results from the different implementation of the common-mode noise correction algorithm in the FEE, as anticipated in § 3.1 and § 3.2.