Phylogeographic, population genetics and diversity analysis are crucial for local tea resource conservation and breeding programs. Lincang in Yunnan has been known as the possible place of domestication for tea worldwide, yet, its genetic makeup and unique Lincang origin are little understood. Here, we reported a large-scale whole-genome resequencing based population genomic analysis in eight main tea-producing areas of Lincang in Yunnan , and the first comprehensive map of tea genome variation in Lincang was constructed. Based on the population structure, tea sample in Lincang was divided into three subgroups, and inferred Xigui and Nahan Tea Mountain in Linxiang, Baiying Mountain Ancient Tea Garden in Yun, and Jinxiu Village of Xiaowan Town in Fengqing, which belong to the birthplace of the three subgroups, were all likely to be the origin center of Lincang tea. Meanwhile, the history population sizes analysis show that similar evolutionary patterns were observed for the three subgroups of Lincang. It also was observed that the hybrid among eight areas of Lincang was noticeable, resulting in insignificant genetic differentiation between geographical populations and low genetic diversity. The findings of this study clarified the genetic make-up and evolutionary traits of the local population of tea, which gave some insight into the development of Lincang tea. It is bic crops . In the ic crops .C. sinensis var. assamica (CSA) in temperate regions and C. sinensis var. sinensis (CSS) in tropical and subtropical regions. The large-leaf tea tree (CSA) originated in southwestern China or the Assam region of India, whereas the small-leaf tea originated in the south-eastern region of China  germplasm resources from around the world and identified candidate genes associated with differentiation traits during the domestication of hemp-type and drug-type cultivars of cannabis, revealing the domestication origin and evolutionary history of cannabis. In addition, viz., big-leaf tea  and smang No.10 ; Biyun , we divided the tea samples from eight tea regions in Lincang into three subgroups based on the population structure, and inferred three possible origins in Lincang. In addition, It also was observed that the hybrid among eight areas of Lincang was noticeable, resulting in insignificant genetic differentiation between geographical populations and low genetic diversity. A large number of variations identified not only provide deeper insights into the genetic evolution and structural characteristics of the local tea populations but also lay a foundation for conservation and breeding programs of tea resources in Lincang, Yunnan province, China.Camellia Cuspidata) was collected to use as an outgroup during phylogenetic analysis. The details about the sampled populations are presented in A total of 1,350 tea accessions were collected at diverse sites from Lincang, Yunnan, and other regions of China during the period from 2019 to 2020. Among them, 31 samples were collected in Cangyuan, 115 in Fengqing, 78 in Gengma, 235 in Linxiang, 291 in Shuangjiang, 187 in Yongde, 217 in Yun, 81 in Zhenkang, and 115 in Others. The Others group was used as a control for the samples of the eight geographic populations, so the sampling range of the Others group was widely dispersed , following the manufacturer’s protocol. Around 2 μg of the extracted DNA was used to construct the sequencing library for each accession using the NEBNext Ultra DNA Library Prep Kit , following the manufacturer’s instructions. Paired-end sequencing libraries with an insert size of approximately 400 bp were sequenced on the Illumina NovaSeq 6000 platform. We used fastp v0.12.2  for the Camellia sinensis  phylogenetic tree with 100 bootstraps using SNPhylo v20140701 . Camellir2 threshold of 0.5 to determine a pruned SNP set to be used in the population structure analysis. LD-based pruning reduces the effects of ascertainment bias in a relatively efficient manner , expected homozygosity (EH), inbreeding coefficient (F), the average pairwise diversity within a population (θπ), and Tajima’s D were calculated using the vcftools v0.1.13 . Meanwhile, we downloaded 4 environmental variables and two geographic variables available at each ecotype site (resolution 30 arc seconds) through WorldClimAmong the four environmental variables, we selected two temperature and two precipitation variables. The temperature variables  included the mean diurnal range (MDR) and the mean temperature of the wettest quarter (MTW). The precipitation variables (expressed to the nearest mm) included annual precipitation (AP) and precipitation of the wettest month PWM; . In addiThe geographic variables included LONG and LAT. These variables tended to show lower correlations with each other. We intended to reduce the redundancy inherent among highly associated variables . For theTo sort out the potential effects of environmental variables and geographical isolation on genetic variation in tea tree samples, the Mantel test  of correm) allowed in the model. Here we use m = 1 to m = 5  and nucleotide polymorphism (θπ) based cross approach to investigate the selection signals across the whole genome. A 50 kb sliding window with 10 kb step approach was applied to quantify STF and θπ by using VCFtools software (v0.1.13). The annotated genes living in these regions were considered candidate selected genes.RAiSD . Mapping of these reads with the reference genome resulted in an average alignment rate of 98.9 ± 2.65%  were used to analyze the population structure and differentiation. According to the calculated CV error value, when smallest . Therefosmallest  in Lincasmallest . The redIn the principal component analysis of tea accessions from Lincang , the thrC. cuspidata) as the outgroup, and the bootstrap value of 100. The result of regional phylogenetic analysis showed that no precise geographic or regional clustering was observed in the phylogenetic tree  method with KM6  of the tea populations varied between 9.04 (group 2) and 10.25% (group 3), while the observed heterozygosity (OH) of the tea populations ranged between 3.05% (group 2) and 3.43% (group 3). The inbreeding coefficient (F) of the tea populations varied between 65.52% (group 1) and 69.66% (group 3). It is worth noting that a degree of variability existed between subgroups in both EH and F (θπ) in the three subgroup were estimated at the individual level after the correction for sample size. The analysis found that group 1 (8.89 × 10−4) had the highest π value, while group 3 (3.31 × 10−4) had the lowest π value (−3), which is consistent with the expected heterozygosity and the observed heterozygosity. Otherwise, Tajama’D of Cangyuan is closest to 0 concerning the other eight populations, indicating that Cangyuan is the least selected in Lincang and preserves a large amount of tea germplasm, which is potential resources to expand the genetic resources of improvement. The lowest level of diversity was found for the Shuangjiang population (1.071 × 10−3), which resulted from a long history of breed formation and selective breeding more than in most other areas.The parameters HE and F . Nucleot π value . Meanwhi π value , CangyuaSTF between individual populations is presented in STF values between regions indicate a weak genetic differentiation between regional populations (The pairwise ulations .r1 = 0.1462), genetic distance, and geographic distance (r2 = 0.07843), are moderately related with alpha = 0.01 . The observed correlation  suggests that the matrix entries are positively associated. This means that the larger geographical distances between tea accessions lead to greater genetic differences, and the higher differences in environmental variables result in more genetic distance.In the Mantel tests, we can reject the null hypothesis that these three matrices, genetic distance and environment distance  after considering geographic distance. This result suggests that environmental selection has a weak but non-negligible effect in shaping genetic variation in wild tea germplasm after individual control distance isolation. It can be inferred that there may be introductions of the tea accessions from each other resulting in the weak regional characteristics of tea trees in these populations, which is corresponding to the results of regional phylogenetic analysis  is a non-random combination of alleles at different positions in a given population, and usually expressed as 2 values , which aare <0.4 . When thPopulation history includes events such as population bottlenecks, expansions, migrations and admixtures, which have important implications for the formation of genetic polymorphic patterns in populations. To reveal the evolutionary history of Lincang tea, we applied the multiple sequentially Markovian coalescent MSMC;  model toAlso, TreeMix was used for inferring historical segregation and admixture of populations based on genome-wide SNP allele frequency data. Regarding the changes in tea gene flow, To study the selective characterization of three subgroups of Lincang, 611, 1,328, and 2,090 selective sweep regions were both detected in different tea plant subpopulations, which harbored 65,145 and 201 candidate genes, respectively . In grouIn this study, we reported the characteristics of whole-genome SNPs and indels of 1,350 tea accessions, covering almost all varieties of tea in Lincang, Yunnan. The genomic variation data on the scale of this study is the largest ever reported for the tea plant. In total, our study generated 64,462,516,344 short reads and 5.967 billion SNPs, and together these datasets provide the most extensive genomic resource available for tea researchers.In the analysis of population structure, genetic stratification analysis by the Bayesian clustering model revealed the presence of three subpopulations. The study found that samples from group 1 were predominantly from Yun and Linxiang, and based on the geographical distribution of the sample, it can be inferred that the origin of group 1 is most likely from the Xigui and Nahan tea mountains located at the junction of Yun and Linxiang. It also has been identified that the wild tea plantation in the Xigui and Nahan tea mountains is one of China’s tea tree origin centers , which iC. sinensis var. assamica), and intermediate species of tea trees , and geographic (r2 = 0.07843) were not significantly associated, indicating that introgressive hybridization and artificial selection may have occurred between regions, in agreement with the results of gene flow analysis and local tea plantation policies. ln addition, the apparent introduction of Lincang tea trees and close geographical distances between sampling regions have led to no clear classification between geographical regions, making the classification of Lincang tea somewhat challenging. In the coming years, further worldwide sampling and analysis will help resolve the current debates on tea taxonomy.In addition, MSMC analysis identify a dramatic expansion and contraction of effective population size of the different tea subgroups of Lincang. It is noted that the Pu people began domesticating and using wild tea trees in Yunnan 3,000 years ago during the Shang and Zhou periods, which may account for the lower effective population. On the genetic variability of the Lincang tea, it was found the significant genetic differences in the three subgroups and the moderate level of genetic differentiation among eight regional populations. Meanwhile, genetic difference and environment  under the accession number PRJCA011312. In addition, the sequencing data are also accessible from the tea database (http://teabase.ynau.edu.cn/index/download/index).The raw sequencing data reported in this paper have been deposited in the BIG Data Center , Yunnan; Yunnan provincial key programs of Yunnan Eco-friendly Food International Cooperation Research Center project (grant number 2019ZG00908); and Yunnan Provincial Science and Technology Department Project .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Salt stress is one of the major environmental stress factors that affect and limit wheat production worldwide. Therefore, properly evaluating wheat genotypes during the germination stage could be one of the effective ways to improve yield. Currently, phenotypic identification platforms are widely used in the seed breeding process, which can improve the speed of detection compared with traditional methods. We developed the Wheat Seed Vigour Assessment System (WSVAS), which enables rapid and accurate detection of wheat seed germination using the lightweight convolutional neural network YOLOv4. The WSVAS system can automatically acquire, process and analyse image data of wheat varieties to evaluate the response of wheat seeds to salt stress under controlled environments. The WSVAS image acquisition system was set up to continuously acquire images of seeds of four wheat varieties under three types of salt stress. In this paper, we verified the accuracy of WSVAS by comparing manual scoring. The cumulative germination curves of wheat seeds of four genotypes under three salt stresses were also investigated. In this study, we compared three models, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4. We found that YOLOv4 was the best model for wheat seed germination target detection, and the results showed that the model achieved an average detection accuracy (mAP) of 97.59%, a recall rate  of 97.35% and the detection speed was up to 6.82 FPS. This proved that the model could effectively detect the number of germinating seeds in wheat. In addition, the germination rate and germination index of the two indicators were highly correlated with germination vigour, indicating significant differences in salt tolerance amongst wheat varieties. WSVAS can quantify plant stress caused by salt stress and provides a powerful tool for salt-tolerant wheat breeding. Wheat is one of the world’s major food crops and its physiological research has been a hot topic in agricultural research worldwide . In receTraditional seed germination tests usually employ methods to destroy seed samples and rely on manual measurements, which often limit the efficiency, scale and accuracy of the tests . Image pIn recent years, with the development of computer technology, the use of machine learning to detect seed vigour has become a hot topic in seed nondestructive testing . ClassicAt present, convolutional neural networks (CNN) are increasingly and widely used for plant and animal image detection . The secThis paper presents WSVAS, an accurate evaluation platform based on the CNN model YOLOv4 for automatic imaging of wheat seeds and rapid detection of germination. WSVAS integrates seed germination chamber hardware to control the seed culture experimental environment  and continuous acquisition of seed germination imaging, and a core algorithm based on the YOLOv4 CNN for detecting germinating wheat seeds and translating the image information into seed germination vigour under salt stress. With this platform, we have incubated and monitored four genotypes of seeds under three salt solutions, collected a series of real-time images of seed germination and analysed and quantified the germination profile plots of seeds under different salt stresses. WSVAS provides a powerful method to automatically evaluate plant salt tolerance by analysing the response of wheat seed germination to salt stress.22.1via the GigE connection. Seed germination images are displayed on the user interface and saved in local storage. It can be used for training neural network models in an industrial computer database. Finally, a series of single-group wheat seed germination test maps are created.WSVAS was designed for continuous monitoring of seed germination, as shown in via the camera’s gigabit network (GigE). The edge computer can immediately process the images according to the processing pipeline and visualise the results to the user for real-time monitoring.The image-processing-based monitoring module has an industrial camera  with an image resolution of 2448 × 2048 pixels. 2.22.2.1A total of 400 wheat seeds of uniform fullness and size were carefully selected, sterilised them with 2.5% sodium hypochlorite for 10 min, rinse them five times using distilled water and air-dry them in the shade afterwards. Then, the treated wheat seeds were soaked in distilled water for 12 h, the seeds were taken out to dry and placed on a blue filter paper waiting to be germinated. The bottom plate was a plastic tray with the size of 25 cm × 25 cm × 2 cm , a total of 100 seeds were randomly placed on each tray and the same experiment was repeated four times. The seed germination Petri dishes were placed in a crop seed germination monitoring incubator with consistent environmental settings (24 h of light and 25°C room temperature). An RGB camera was set up on the developed user interface to take and collect pictures of all wheat seeds during germination every 30 minutes. Each experiment was conducted for 5 days and there were 48 images collected each day. A total of 960 images were collected during the entire experiment. All the captured images were saved in.jpg format on a local disk and the image resolutions were all 2448 × 2048 pixels, as shown in 2.2.2The data set used to train the seed germination detection model consisted of processed images and manual labels for each seed.Data screening: Amongst the 960 images collected, there were images with severe root overlap of wheat seeds at the late germination stage, thereby affecting data labelling. After data cleaning, a total of 880 images were finally obtained for labelling.Data augmentation: In deep learning training, data augmentation is used to expand the sample size to improve the robustness and generalisation ability of the model. The methods of data enhancement commonly used in target detection tasks are image scaling, flipping, cropping, colour transformation, adding noise, dithering and so on. Seed placement method and several seeds on model recognition accuracy, image cropping, random flip, and brightness transformation were used to perform offline data enhancement on 880 images to avoid the influence of brightness, as shown in Data annotation: Manual annotation was performed using the open-source tool LabelImg. The labels were set to two types: “sprouting” and “not sprouting”. We considered wheat seeds germinating when the roots were visible and reached half the length of the seed itself, as shown in Data set partitioning: The data sets were randomly partitioned based on the ratio of 8:2, in which 80% of the training set was used for model training  and 20% of the test set was used for model performance evaluation . During the training process, 10% of the validation set was divided into the training set for cross-validation to improve the model generalisation ability and prevent the occurrence of overfitting, which was used for the correction in model training . 2.32.3.1With the development of computer image technology, several neural networks for target detection and classification, such as YOLO, SSD, RetinaNet, Fast R-CNN and Faster R-CNN have been proposed. YOLO series is a classic representative of one stage detection algorithm, whose main idea is to use a single CNN to process images. The main idea is to use a single CNN to process the image and directly output the regression border coordinates and prediction categories thereby reducing the number of operations and increasing the detection speed. Therefore, YOLOv4, which has superior performance in small target detection and faster detection speed, is chosen in this study to meet the accuracy while satisfying real-time monitoring, and the model structure is shown in We used CSPDarknet-53 as the backbone network, SPP as an additional module, PAN path aggregation network as the neck and YOLOv3 as the head to form the overall structure of YOLOv4 . The mod2.3.2Deep learning network model training usually requires a high configuration of the training platform, which can be trained on a CPU or GPU. Given that the computational power of GPU is higher and its training cost is lower than that of CPU, thus training on GPU is chosen. This study uses Windows 10 operating system, processor AMD Ryzen 9 5950X at 4.9GHz, graphics card GeForce RTX 3070, memory 64G, CUDA 10.1, cuDNN 7.6.5 and CMake 3.16 configured in Pycharm2019 development software. Using Python 3.8 freeze training can speed up the training efficiency and prevent the overfitting problem, we divide the whole training process into two phases, including the freeze training phase and unfreeze training phase. The hyperparameters include iteration number epoch, batch_size, learning rate, momentum, weight_ decay and so on. This model training mainly discusses the learning rate, batch_size and freezing epoch, the detailed discussion results are shown in schedule S1. Finally, different training parameters are adjusted to obtain the loss function change curve of the model. The untrained test set data are used to evaluate the performance of different models and compare the best detection model. The specific parameters are shown in 2.3.3Accuracy (Precision), Recall , Average Precision (AP), mAP (mean Average Precision) and Intersection Over Union (IOU) are metrics widely used in the field of CNN to evaluate the quality of model training results. In this paper, we set “sprout” as positive and “not sprout” as negative in the target classification, and the four cases are shown in The index evaluation of seed germination target identification results was calculated based on Equations (1–4).s” and “gs” represent Precision, Recall, a parameter with a function of r, feature part of the seeds identified by the network model and features of the seeds tagged in the label, respectively.where “p”, “r”, “p(r)”, “p2.3.4Seed vigour is the sum of all characteristics that determine the activity and expression of the seed or seed germ during germination in various environments . Germinawhere “Nt”, “N”, “Gt” and “Dt” denote the number of germinating seeds on the day “t”, the total number of seeds tested, the number of germinating seeds on the day “t” and seed growth period, respectively. Three biological replicates were measured for each treatment.33.1This study will compare the evaluation performance indices and detection effect plots under three models to select the best model for wheat seed germination target detection, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLO v4. In this study, the best iterative models under the training of three CNNs, VGG16 + Faster R-CNN, ResNet50 + Faster R-CNN and YOLOv4 were selected to test four different test sets, which represent the pre-, mid-, post-, and data-enhanced images of seed germination. In As shown in The correct detection rate, missed detection rate and over detection rate of the three models can be derived based on the above discussion, as shown in 3.2Seed germination is the beginning stage of plant growth and the most sensitive and vulnerable stage to salt stress . TherefoSeeds under salt stress often show reduced germination and vigour and even death in severe cases, and such phenomena are mainly caused by water stress and ion imbalance . FigureThe variability of manual and platform counts was compared comprehensively to verify the accuracy of this seed germination platform. Three sets of trials were used to conduct manual counts according to image sequences Figure 9A large number of machine learning evaluation model metrics were used including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), absolute coefficient (R2) and Pearson correlation metric (r). The results were generated as shown in 4In this study, a YOLOv4 CNN-based wheat seed salt tolerance germination vigour detection method was proposed to test the germination vigour of four different wheat species under three salt stresses and analyse the differences in salt tolerance of different wheat varieties to achieve an accurate assessment of wheat seed germination vigour under salt stress environment and real-time detection.(1) The method uses YOLOv4 CNN to achieve accurate and rapid detection of a wheat seed number. In terms of model detection accuracy, the mean value of accuracy mAP and recall of the wheat seed germination detection model were 97.59% and 97.35%, respectively; in terms of detection speed, the speed of seed germination target detection model was designed based on YOLOv4 was as high as 6.82 frames per sec. Comparing the evaluation performance indices under the two models of YOLOv4 with VGG16 + Faster R-CNN and ResNet50 + Faster R-CNN, YOLOv4 outperforms the VGG16 + Faster R-CNN and ResNet50 + Faster R-CNN models in detection accuracy by 11.02% and 7.18%; in terms of detection speed, respectively. The seed germination target detection model designed based on YOLOv4 is the fastest, up to 6.82 frames/second. In addition, the experimental results show that different types of wheat seeds and salt stress environments do not greatly affect the quantitative detection results, indicating that this paper is feasible for accurate detection of wheat seeds by the lightweight CNN YOLOv4.(2) The optimal detection model of wheat germinating seed target identification based on the YOLOv4 network was used to count the number of germinating seeds in the pictures. The germination curve of wheat seed germination number with time was plotted, and the germination index and germination rate of wheat under salt concentration were obtained and then the effect of salt stress on the germination vigour of different genotypes of wheat seeds was analysed. The differences between the number of germination and manual counts based on the crop seed germination monitoring system were also compared. A linear regression analysis was performed to provide technical support for the establishment of seed germination vigour ratings and wheat seed breeding for salt tolerance. In addition, this study can provide some technical references for studying the germination vigour of more seeds and establish a perfect seed vigour rating model.WSVAS can meet more parameters of detection by further developing and optimising different users’ collection needs, which provides a powerful tool for studying diverse seed early growth performance and predicting future yield with high process value.The original contributions presented in the study are included in the article/XF: Conceptualization, Data curation, Software. BH: Software, Validation, Writing-original draft. SL: Supervision, Investigation, Resources. JZ: Supervision, Methodology, Resources. HWZ: Supervision, Investigation, Resources. HW: Supervision, Investigation, Resources. HZ: Supervision, Investigation, Resources. ZO: Supervision, Investigation, Resources. All authors contributed to the article and approved the submitted version.


Aquarius paludum, which was sampled across its entire distribution in Eurasia and used these datasets in combination with ecological niche modeling (ENM) to elucidate the influence of the Holocene and future climate warming on its population structure and demographic history. We found that A. paludum consisted of two phylogeographic lineages that diverged in the middle Pleistocene, which resulted in a “west–east component” genetic pattern that was probably triggered by Central Asia-Mongoxin aridification and Pleistocene glaciations. The diverged western and eastern lineages had a second contact in the Holocene, which shaped a temporary hybrid zone located at the boundary of the arid–semiarid regions of China. Future predictions detected a potentially novel northern corridor to connect the western and eastern populations, indicating west–east gene flow would possibly continue to intensify under future warming climate conditions. Further integrating phylogeographic and ENM analyses of multiple Eurasian temperate taxa based on published studies reinforced our findings on the “west–east component” genetic pattern and the predicted future northern corridor for A. paludum. Our study provided a detailed paradigm from a phylogeographic perspective of how transcontinental temperate species differ from cold-adapted taxa in their response to climate warming.Holocene climate warming has dramatically altered biological diversity and distributions. Recent human-induced emissions of greenhouse gases will exacerbate global warming and thus induce threats to cold-adapted taxa. However, the impacts of this major climate change on transcontinental temperate species are still poorly understood. Here, we generated extensive genomic datasets for a water strider,  After the last ice age ended, our planet entered a period with a stable, warm climate during the Holocene . This waPica pica, Meles meles, Argiope bruennichi, Eurasia is a supercontinent that joins the European and Asian continents and accounts for ∼36.2% of Earth’s total land area . This suAquarius paludum (Fabricius), a semiaquatic insect, has a wide distribution in the low and middle latitudes (1–50°N) of Eurasia, where it is geographically distributed from western Europe (England) to East Asia (Japan), which is typical of transcontinental temperate species , whole mitochondrial genome , double-digest restriction site-associated DNA sequencing (ddRAD-seq), and whole-genome sequencing (WGS) to infer population structure and demographic history, whereas searching for outlier loci associated with lineage differentiation of A total of 1,280 bp of the mitochondrial PCGs were obtained from 390 individuals, including segments of the COI (647 bp) and COII (633 bp) genes. The mitogenome contained 13 typical mitochondrial PCGs, and the lengths of the mitochondrial PCGs ranged from 156 bp (ATP8) to 1,713 bp (ND5). The concatenated matrix of the 13 mitochondrial PCGs included 10,960 aligned nucleotide sites representing 112 individuals from 22 populations.Gerris buenoi resulted in an average depth and breadth of coverage of 7.16 and 99.4%, respectively, with an average mapping rate of 44.2%. We finally identified a total of 510,262 high-quality SNPs after filtering.For the ddRAD-seq dataset, 249 individuals were sequenced, which resulted in 1,375,147,138 raw reads. The number of reads that were mapped to each individual ranged from 1,494,153 to 22,973,092, with an average of 5,522,679 reads per individual. A total of 1,364,149,347 clean reads were retained after the low-quality reads were filtered out. The ddRAD_95 SNPs dataset contained 1,179 ddRAD-seq loci, 32,114 SNPs and 2.98% missing sites, from which 1,179 unlinked SNPs were randomly selected and constituted the ddRAD_95 USNPs dataset (generated by ipyrad software). For the whole-genome sequencing dataset, the mapping of 21 resequenced data points to the reference genome of OH, EH, and Sπ estimated based on the 32,114 SNPs of the ddRAD_95 SNPs dataset are shown in Sπ value was higher in the eastern lineage, whereas the OH and EH values of the western lineage exhibited relatively high heterozygosities. Our BayeScan analysis identified 74 outlier SNPs that had significantly elevated FST values  revealed a distinct peak at K = 2 , genetic and environmental distances , and genetic and cost distances    online. nditions  online. nditions  online.The demographic model selection strongly indicated that model M6 was the best fit to the observed data with Akaike information criterion (AIC) weight = 1  online, Ne values of the western lineage  were much higher than those of the eastern lineage , both of which were much higher than the estimates for the ancestor of the two lineages . The estimated mean times indicated that the western lineage first split from the eastern lineage 383,708 ya , and the two diverged lineages had a second contact of 2,870 ya . Estimates of the migration probabilities per generation between the two lineages were asymmetric, with high probabilities observed for migration of the eastern lineage into the western lineage at 1.2E−6 (1.1E−6–1.3E−6) and low probabilities for migration of the western lineage into the eastern lineage at 3.6E−7 (3.4E−7–3.8E−7).Point estimates of the demographic parameters for model M6 were provided with 95% confidence intervals (CIs)  online. a). The pairwise sequential Markovian coalescent (PSMC) analysis indicated that the demographic history of A. paludum could be traced back to ∼1 Ma, and the western and eastern lineages both experienced similar demographic histories, which exhibited recent population declines that were most likely caused by the LGM, which supports the results of the stairway plot (b). However, when the analysis of the demographic dynamics accounted for the mitochondrial data, the BSP plots could not reject population stability since the LGM for the two genetic lineages , respectively, and eventually maintained stable effective population sizes until now a. The paway plot b. Howevelineages  online.a). However, the niche overlap test (NOT) and niche divergence test (NDT) might be inconclusive due to the relatively high potential niche truncation index of our analyses (PNTI), which suggested that the measured occupied niche might not reflect the species’ fundamental niche due to niche truncation driven by the limited available E-space and might have led to the inability to obtain appropriate background data. Therefore, we further explored the five most informative climatic variables identified by Humboldt. We found that the western lineage of A. paludum tended to occupy habitats with lower temperatures and less precipitation than the eastern lineage (b). The hybrid populations  fell into the intermediate environmental space with respect to the mean temperature of wettest quarter (b). A principle component analysis (PCA) of the pooled environmental variables revealed reduced significant components, thus defining a realized niche space occupied by the two lineages and hybrid populations (c). The first three components of the PCA together explained 92.2% of the overall variance. The first component (PC-1) was closely associated with the mean temperature of the wettest quarter and precipitation seasonality, whereas the second component (PC-2) was associated with the precipitation of the driest month  by using a false discovery rate (FDR) of 0.05 based on the three cut-off methods    online. ast Asia . The unsast Asia . Integraow 50°N) . The futow 50°N) . Under tow 50°N)  online.  Plateau  online.  Plateau  online.  Eurasia  online.A. paludum consisted of two phylogeographic lineages , and AMOVA analyses identified strong genetic differentiation when the populations were divided into the two lineages. In addition, our selected demographic model strongly favored an initial divergence between the western and eastern lineages with no gene flow, and the divergence between the western and eastern lineages occurred at 383,708 ya  in the middle Pleistocene. These results reinforced the “west–east component” genetic pattern of A. paludum, which was consistent with the conclusion from previous phylogenetic studies using a 425 bp COI gene fragment , and it is likely that these species would have been affected by the Pleistocene glaciations and forced to migrate into the southern warmer refuges, which further limited gene flow between the western and eastern populations and deepened the “west–east component” genetic pattern. Although we could not test this hypothesis by using ENM analysis due to a lack of paleoclimatic data for the early and middle Pleistocene, the potentially suitable areas we simulated under the LGM in the late Pleistocene might provide analogous evidence, thus indicating that the suitable areas for all six Eurasian temperate species contracted greatly toward the southern refuges  in western Europe and East Asia during the LGM period. Combined with the above analyses, we suggest that the broad, arid habitats of Central Asia-Mongoxin acted as an isolating barrier together with the cold, dry climate in the Pleistocene inducing western and eastern population divisions for the Eurasian temperate species, which finally shaped a “west–east component” genetic pattern.A. paludum experienced a recent secondary contact at 2,870 ya . Further STRUCTURE and DAPC analyses detected two populations  that showed hybrid types incorporating the western and eastern lineages, and they provided direct genetic evidence for the contact between the western and eastern populations under the Holocene climate warming conditions. The detected second contact area belongs to a transition zone that lies at the boundary of the arid–semiarid regions of China near the “Heihe-Tengchong Line,” which is characterized by a variety of complex environmental landscapes  and methane account for more than 90% of these gases . Furthermore, three of the five Eurasian temperate species, D. magna, L. dispar, and P. pica, also exhibited similar or expanded corridors as suitable areas between western and eastern Eurasia under future climatic scenarios. Coupled with the results from the ENM and population genetic analyses, we found that the west–east communications for the Eurasian temperate species would possibly continue to intensify under future warming climate conditions, and the hybrid zone due to the second contact might continue to expand or even homogenize the present “west–east component” genetic pattern.Since the beginning of the Anthropocene, humans have both directly and indirectly driven new climatic trajectories, which have resulted in hotter climatic conditions . The larse gases . Fossil Wolbachia spp., A. paludum based on the current data: in the face of rampant male dispersal, the macropterous females from the western population who later arrived at the hybrid zone relative to females from the eastern population might suffer from reduced reproductive success due to the low fertility in the established hybrid populations. However, this explanation should be further tested based on more biological evidence, such as the dispersal abilities of males and females and their reproductive behaviors.Mito-nuclear discordance has been increasingly found in phylogeographic studies within and among conspecific populations and closely related species . This inCoelodonta antiquitatis, Mammuthus primigenius, and Megaloceros giganteus , which indicated that climate warming could promote population growth. Further local adaptation studies on transcontinental temperate taxa and cold-adapted taxa are needed to understand the functions of candidate genes that adapt to temperate and cold climate conditions under the impacts of climate warming.Most transcontinental cold-adapted species are adapted to cold climates at high latitudes in northern Eurasia near the Arctic, and the magnitude of recent climate warming in high latitude regions in northern Eurasia is larger than that in temperate Eurasian regions. The influence of past or future warming events on the demographic and genetic effects of extinct and extant cold-adapted Eurasian species has been extensively studied by using paleoclimate niche modeling and phylogeographic reconstruction. The ENM predictions indicated that the decreases in suitable habitat during the Holocene climate warming contributed to the demographic decline of cold-adapted Eurasian terrestrial mammals, which led to extirpation and in some cases the extinction of taxa, such as iganteus . For theiganteus . Furtheriganteus . In contA. paludum in Eurasia. The latitude and longitude of each collection site were recorded using a handheld global positioning system (GPS) unit. All samples were preserved in 70–95% ethanol and stored in a freezer at −20 °C in the College of Life Sciences at Nankai University . Genomic DNA of each specimen was extracted from the entire body, excluding the abdomen and genitalia, of each specimen using a Universal Genomic DNA Kit (CWBIO). Detailed information for molecular voucher specimens is provided in We sampled 52 populations, comprising a total of 409 individuals between 1999 and 2018 across the natural distributional range of A total of 390 out of 409 individuals were sequenced for COI and COII fragments  online. A ddRAD-seq library was prepared to obtain nuclear SNPs following Peterson’s protocol . A totalA. paludum, we chose one individual from each population and shotgun sequenced the genomes of 21 individuals    online. OH), expected heterozygosity (EH), and nucleotide diversity (Sπ), which were calculated in Arlequin 3.5 (K) was set from 1 to 10. For each value of K, we performed five independent runs with 1,200,000 iterations following a burn-in of 200,000 replications. The optimal K was then determined using the delta K method of K) using 50 replicates for the runs for each value of K and an alpha value of 250. In addition, the population structure based on the nuclear SNPs data was estimated by DAPC, which is a method that has no assumptions for evolutionary models, using the R package Adegenet  based on 5,000 permutations to quantify how genetic variation was partitioned across the different levels of sampling using Arlequin 3.5  to generate the observed SFS and chose to project the full unlinked SNPs dataset to reduce the possibility of linkage. We performed 50 replicate runs for each model with each replicate using 50,000 coalescent simulations and ran 40 optimization cycles. The monomorphic sites were ignored to avoid overfitting. To select the best-fit model, MaxObsLhood was compared with MaxEstLhood for the data in each model for each replicate run. The best-fit run was identified as the one that had the smallest difference between MaxObsLhood and MaxEstLhood for each of the 50 replicate runs for each model. To generate 95% CIs of the parameter estimates for our best-fit model, a combination of the initial model selection runs and parametric bootstrapping were used. We simulated 100 replicates SFS from the *_maxL.par file for the best-fit run. We then performed 50 replicate analyses as described above for each of the 100 newly simulated SFS files. Finally, we calculated the mean parameter estimates and 95% CIs from the 100 best-fit bootstrapping replicates.Based on the results of the population genetic structure estimated from the nuclear SNPs dataset, 24 populations were grouped into the two lineages  corresponding to geography (western and eastern regions). We simulated the demographic history of n models  online. n models . This menogaster  and assunogaster . We usedA. paludum. Details of historical demographic reconstruction can be found in We used three datasets to reconstruct the recent demographic trajectories for the western and eastern lineages of To quantify the niche comparisons between western and eastern lineages identified by the genetic structure (see Results), the same clipped layers of five climatic variables  and 1,643 occurrence records  were used. The NOT and NDT were performed using corrected e-space across the full distribution and shared e-space in Humboldt . FurtherA. paludum, we searched for outlier SNPs using R package pcadapt 4.0 (Gerris buenoi (GenBank accession no.: GCA_001010745.2) to identify nearby genes and identified enriched GO terms. This analysis was performed using the WGS_SNPs dataset including 21 individuals (13 from the western lineage and 8 from the eastern lineage). We chosed a cut-off for outlier detection based on three methods  from the less to the more conservative one. The SNPs that were identified as significant were located at the positions of the reference genome. To estimate the biological functions of the genes linked to outlier SNPs, gene sequences were extracted within 2000 bp, including the outlier SNPs, and then used for homologous search analysis. Loci under putative selection were functionally annotated by Blast2GO  (To test for signatures of selection between western and eastern lineages of dapt 4.0 . OutlierBlast2GO  against t (WEGO)  with defA total of 233 male specimens representing western  and eastern lineages  were analyzed based on size measurements of morphological diagnostic characteristics. We did not include individuals of potential hybrid populations (NMBT and HLQQ) in this analysis. Eight diagnostic characteristics were measured: body length, body width, head length, pronotum length, hind femur length, abdomen length, seventh abdominal sternum length, and connexival spine length. All measurements were obtained with a stereomicroscope and recorded in millimeters. PCA based on the eight morphological variables was used to visualize the morphological relationships between the western and eastern lineages using SPSS (IBM 2009). The eight morphological variables occupied by the two lineages were then compared one by one visually in boxplots using ggplot2 .A. paludum in the present study, we searched the Web of Science database for other phylogeographic studies on Eurasian species in order to conduct an integrated analysis. The published phylogeographic studies were filtered by the following two criteria: (1) species/subspecies or native populations of species were endemic in the regions of low and middle latitudes (1–50°N) of Eurasia, which had a similar transcontinental distribution pattern as A. paludum and (2) samples from the western and eastern regions in Eurasia were relatively balanced, and the divergence time between the lineages was estimated based on the molecular data. In total, five published studies met both criteria, including invertebrates , birds (P. pica) and mammals (M. meles). The phylogeographic patterns and divergence times for these five species were extracted and exhibited. For the ecological niche analyses, a total of 1,645 occurrence localities of A. paludum based on our collection, literature records, and online database from the Global Biodiversity Information Facility online database  were used for niche modeling. The coordinates of the other five Eurasian temperate species for niche model building were directly downloaded from the GBIF database and incorporated with sites from published studies . All 19 bioclimatic variables for the current and LGM scenarios under the Community Climate System Model (CCSM4) and the Model for Interdisciplinary Research on Climate (MIROC-ESM) were downloaded from the WorldClim website (http://www.worldclim.org/). We excluded those variables with the Pearson correlation coefficient r > 0.7 based on a pairwise comparison of raster files in SDMTOOLBOX .In addition to the newly obtained dataset of  studies , which de spthin , resultiMTOOLBOX . Five vaMTOOLBOX  to develMTOOLBOX . The aremsac089_Supplementary_DataClick here for additional data file.


A reduced level or tension or the deprivation of oxygen is termed hypoxia. It is common for tumours to outgrow their natural source of nutrients, which causes hypoxia in some tumour regions. Hypoxia affects ovarian cancer (OC) in several ways.  In this study, the expression patterns of prognostic hypoxia-related genes were curated, and consensus clustering analyses were performed to determine hypoxia subtypes in OC included in The Cancer Genome Atlas cohort. Two hypoxia-related subtypes were observed and considered for further investigation. The analyses of differentially expressed genes (DEGs), gene ontology, mutation, and immune cell infraction were performed to explore the underlying molecular mechanisms. CLIP2 and SH3PXD2A were low in OC tissues. Immune cell infarction analysis revealed that the subtypes were associated with the tumour microenvironment (TME).  In total, 377 patients with OC were classified into two subgroups based on the subtype of hypoxia. The clinical outcome was considerably poor for patients with hypoxia subtype 2. DEG and protein-protein interaction analyses revealed that the expression levels of  Our findings established the existence of two distinctive, complex, and varied hypoxia subtypes in OC. Findings from the quantitative analysis of hypoxia subtypes in patients improved our understanding of the characteristics of the TME and may facilitate the development of more efficient treatment regimens. Ovarian cancer (OC) is by far the deadliest type of gynaecological cancer and the fifth leading cause of cancer-associated death among females . Early dHypoxia is reportedly associated with chemoresistance via several pathways. Altering the metabolism of cancer cells is one of the ways through which hypoxia may cause chemoresistance in patients with cancer. OC cells, when exposed to hypoxia, are subjected to metabolic reprogramming, which alters the glycolytic pathway and enhances resistance to carboplatin . HypoxiaImmunotherapy is considered a potentially viable treatment option since it has high degrees of specificity, long-term benefits, and minimal adverse effects. Owing to extensive variability, including clinical and pathological parameters, molecular characteristics, and the immune cell milieu, among other factors, the response rate to immune checkpoint blockade treatment in patients with OC remains as low as 15% –12. Givehttps://cancergenome.nih.gov/) project was used to collect and process the molecular data of 377 individuals who had been diagnosed with OC. The GDC data portal  was used to obtain the transcriptomic profiles (HTSeq-fragments per kilobase of exon per million mapped fragments (FPKM)) and clinical data for TCGA-OC dataset. The Ensembl IDs were translated to gene symbols, and the FPKM values were transformed into transcripts per million [The Cancer Genome Atlas , we created a network of DEGs and their coexpression genes [The significance analysis built into the empirical Bayes techniques used as a part of the limma package was used to detect DEGs. The cut-off values for selecting the relevant DEGs were a on genes ,17.The data were evaluated using functional enrichment analysis to confirm the fundamental function of putative targets. GO is a technique extensively used to annotate genes with functions, including cellular components (CC), biological pathways (BP), and molecular function (MF). ClusterProfiler version 3.18.0 in R was used to examine the GO function of putative targets and enrich the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to gain a deeper understanding of how mRNA contributes to the onset and advancement of cancer. The boxplot and heatmap were drawn using the ggplot2 and pheatmap functions of R software, respectively [https://portal.gdc.com), we retrieved the RNA-seq expression patterns, genetic mutation, and relevant clinical data of 376 patients. The maftools package of R software was used to retrieve data on mutations, which were further visualised by the program. Genes with a higher mutational frequency detected in 376 patients in the histogram are demonstrated.Using TCGA dataset  and BioGRID were considered. The resultant network included the subset of proteins that physically interacted with at least one other member in the list. The molecular complex detection (MCODE) algorithm 10 is used to determine the network components that are densely connected when the number of proteins in the network ranges between 3 and 500 .To further confirm the significant role of hub genes in the pathogenesis and prognosis of OC, we used the Gene Expression Profiling Interactive Analysis (GEPIA) database to retrieve information on the expression of these genes and their prognostic significance. The GEPIA database, an interactive online platform for analysing gene expression, contains data on 8,587 normal samples and 9,736 tumour samples .P-value, HR, and 95% confidence interval (CI) for each variable. We created a nomogram based on the findings of a multivariate Cox proportional hazard analysis to accurately predict the 1-year overall recurrence.To define the appropriate terms to generate the nomogram, both univariate and multivariate Cox regression analyses were used. Using the “forestplot” R package, we generated a forest plot to display the SIGLEC15, TIGIT, PDCD1LG2, HAVCR2, PDCD1, LAG3, CTLA4, and CD274 were determined to produce transcripts that are important for immune checkpoints, and the expression levels of these eight genes were measured. R foundation for statistical computing (version 4.0.3) was used for implementing the aforementioned analytical techniques. In addition, we used the ggplot2 and pheatmap functions of the R package [We used immuneeconv, an R software package that incorporates the two most recent algorithms, ssGSEA and CIBERSORT, to validate the outcomes of the immune score assessment. These algorithms are benchmarked and have distinct advantages.  package .μg) was subjected to qRT-PCR using the FastStart Universal SYBR ®Green Master  on an ABI QuantStudio5 Q5 real-time PCR system . Afterward, we used cDNA as a template in 20 μL reaction volume . We conducted PCR as follows: cycling began with an initial DNA denaturation step at 95°C for 30 s, followed by 45 cycles at 94°C for 15 s, 56°C for 30 s, and 72°C for 20 s. Each sample was assessed in triplicates. Using the 2−ΔΔCT method, readings from the threshold cycle (CT) were obtained and further standardised to the levels of glyceraldehyde 3-phosphate dehydrogenase in each sample. The mRNA expression levels were compared to those in paracancerous tissue controls. The primer pair sequences corresponding to the target genes are presented in Total RNA was extracted from paraneoplastic and tumour tissues of patients with OC using the TRIzol reagent . Furthermore, RNA from each sample  and immune subtype 2 (n = 242), were identified in patients with OC. Survival analysis revealed that patients with subtype 2 had a poorer outcome  cells (Figures Using the CIBERSORT algorithm, the landscape of tumour-infiltrating lymphocytes was obtained, and 21 types of immune cell profiles of patients with glioma were determined from TCGA. The proportion of cells such as naïve B cells and CD8subtypes . Checkpo SIGLEC15. Finally Figures .CLIP2 and SH3PXD2A genes in the tumour and nontumour adjacent tissues, the relative mRNA expression levels of CLIP2 and SH3PXD2A in both tumour and nontumour tissues were determined using qPCR. The average expression level of CLIP2 and SH3PXD2A in OC tissue was considerably less than that in normal tissues (To validate the expression of the  tissues .α, for instance, has been reported to play an integral role in various processes, including the promotion of OC immunosuppression, tumour metastasis, and chemoresistance.OC is a severe epithelial cancer that predominantly contributes to cancer-associated death among females –24. The TP53, have been observed to be correlated with the success of immunotherapies and exhibit a predictive potential [TP53 gene was the first to undergo mutation. In hypoxia subtype 1, the TP53 gene was reported to have a greater incidence of mutations than that in hypoxia subtype 2. Our results suggest a difference among the hypoxia subtypes in terms of genetic changes and mutations.In this study, two subtypes of hypoxia were identified using consensus clustering analysis, each of which was based on the prognostic immune-relevant genes. Particularly, hypoxia subtype 2 displayed a more unfavourable clinical outcome than hypoxia subtype 1. Cancer is a malignant neoplasm that may be caused by genetic mutations and variations . Hypoxiaotential . In the CLIP2 and SH3PXD2A, were selected and used for further investigation. Recently, the expression of SH3PXD2A-AS1 was observed to be related to OC; however, the underlying molecular mechanism remains unknown. Simultaneously, the absence of SH3PXD2A has been reported in the OV area. Nonetheless, further investigation is warranted. The results of ssGSEA demonstrated that the decrease in CLIP2 and SH3PXD2A expression may influence the infiltration levels of immune cells, such as NK cells. Finally, PCR results confirmed these patterns in OC tissues. While this work is a bioinformatics and pcr analysis, more investigation should be performed in clinic for future application.In this study, we identified 374 genes generated from the hypoxia subtypes, which had the potential to influence pathways such as the PI3K-Akt signalling pathway. Hub genes, such as CLIP2 and SH3PXD2A should be further investigated and could be novel biomarkers for patients with OC.A well-defined hypoxia score has significant benefits over standard prognostic markers used for OC. Therefore, the hypoxia score may be used to compare various hypoxia-modulating components and aid the exploration of how tumour cells interact with the immunological milieu. In addition, it helps stratify patients with OC into various groups based on their potential response to chemotherapy or other immune checkpoint blockades. Thus,


Ebolavirus contains multiple species of viruses that are highly contagious and lethal, often causing severe hemorrhagic fever. To minimize the global threat from Ebola virus disease (EVD), sustainable, field-appropriate tools are needed to quickly screen and triage symptomatic patients and conduct rapid screening of cadavers to ensure proper handling of human remains. The OraQuick® Ebola Rapid Antigen Test is an in vitro diagnostic single-use immunoassay for the qualitative detection of Ebola virus antigens that detects all known species within the genus Ebolavirus. Here, we report the performance of the OraQuick® Ebola Rapid Antigen Test and provide a comparison of its performance with other rapid diagnostic tests (RDTs) for EVD. OraQuick® Ebola demonstrated clinical sensitivity of 84.0% in archived EVD patient venous whole-blood (WB) samples, 90.9% in Ebola virus-infected monkey fingerstick samples, and 97.1% in EVD patient cadaver buccal swabs, as well as clinical specificity of 98.0–100% in venous WB samples and 99.1–100% in contrived saliva samples. It is the only 510(k)-cleared Ebola rapid test, has analytical sensitivity as good as or better than all RDT comparators for EVD, and can detect the Sudan virus. Our data demonstrate that the OraQuick® Ebola Rapid Antigen Test is a sensitive and specific assay that can be used for rapid detection of EBOV in humans and could support efforts for EVD-specific interventions and control over outbreaks.The genus  Ebolavirus belongs to the viral family Filoviridae, which includes six (6) species: Zaire ebolavirus (EBOV), Sudan ebolavirus (SUDV), Bundibugyo ebolavirus (BDBV), Tai Forest ebolavirus (TAFV), Reston ebolavirus (RESTV), and Bombali ebolavirus (BOMV). EBOV, SUDV, BDBV, and TAFV often cause severe and/or fatal diseases in humans, whereas RESTV and BOMV are apparently not pathogenic to humans. Since the first reports of human diseases caused by SUDV and EBOV in 1976, there have been more than 30 outbreaks of EVD in the subsequent 40 years . The device was read at 30 (−2) minutes after it was placed in the developer solution. Device testing was performed as per the OraQuickt Insert . A totalProcurement of clinical samples and test protocols are discussed in the 3-ethylenediaminetetraacetic acid (EDTA) . Samples were mixed by gentle inversion, checked for clots with wooden applicator sticks, and any excessive clotting was noted. Peripheral blood was collected via fingerstick blood collection. Briefly, NHPs were sedated and placed in a supine position on a downdraft procedure table. The animal’s body temperature was maintained using a Bair Hugger system (3M). To improve peripheral circulation, the animal’s hand was placed in a warm water bath for 2 min and dried, and two fingers were selected and disinfected using 70% isopropyl alcohol wipes. A 4 mm Goldenrod lancet was used to puncture the tip of both the index and middle fingers, just off the center of the pad. The first drop of blood was wiped off with sterile gauze, and the next drop was collected with a disposable micropipette provided in the OraQuick® Ebola Rapid Antigen Test kit. The micropipette was held horizontally, and the blood flowed automatically into the pipette to the volume indicator line (approx. 20 µL). Blood was collected from both fingers to reach the required volume. The fingerstick blood in the filled pipette was immediately deposited into the sample port on the OraQuick® test device.Seronegative rhesus monkeys (referred to as nonhuman primates (NHPs) in this report) were inoculated intramuscularly with 1000 plaque-forming units (PFU) of Makona strain EBOV. Blood specimens were collected from all animals twice prior to inoculation (baseline), on days 3, 5, and 7 post-exposure, and when animals reached pre-determined clinical endpoints. Animals were anesthetized for all procedures. Venous blood was collected in BD vacutainer plastic blood collection tubes containing K® test devices and/or the Ebola Zaire (EZ1) rRT-PCR (TaqMan®)  assay [The blood samples were tested with the OraQuickA) assay  at multiA) assay . Probit analysis was used to determine the limit of detection (LoD) as per Clinical and Laboratory Standards Institute (CLSI) Guidelines EP17-A2. The LoD concentration selected by the probit analysis was confirmed by testing a blinded panel of 20 LoD samples, 20 negative samples, and 20 moderately reactive samples. Inclusivity was evaluated for known strains of the EBOV, which were provided by both the CDC and DoD as inactivated viruses. Testing of inactivated viruses provided by the CDC was completed at the CDC in Atlanta, GA. Further, analytical specificity was evaluated by testing viral, bacterial, and parasitic pathogens. Testing was performed with mostly live organisms unless otherwise indicated, and they were cultured as necessary. Each of the pathogens was spiked into venous WB or OF at the concentrations required. Study testing was completed at Microbac Laboratories, MicroBioTest Division, in Sterling, VA. Potentially interfering substances were tested in WB or OF spiked with recombinant VP40 antigen (rVP40 Ag) to assess their potential effect on the assay performance as per CLSI guidelines EP7-A2. rVP40 Ag (comprising the Zaire strain VP40 gene) is a recombinant protein expressed and purified from a bacterial expression system. A panel of recombinant Ebola VP40 from different variants has been designed and expressed to enable species variation testing with our devices. Reproducibility was assessed for WB, directly collected OF , and OF in viral transport media (VTM). Three sample types  were tested for each of the three device lots; tests were carried out twice daily over five days by three operators per site at each of three sites . ®) version 9.2 or later. The percentages for specificity/sensitivity were calculated as follows: % specificity = 100% × TN/(FP + TN) and % sensitivity = 100% × TP/(TP + FN), where TP is true positive, TN is true negative, FP is false positive, and FN is false negative. The confidence interval (CI) was calculated using the Clopper–Pearson (exact) method. Sensitivity and specificity are sometimes referred to as positive percentage agreement and negative percentage agreement, respectively. Probit analyses utilized the cumulative distribution frequency to determine the LoD, which was defined as the concentration calculated to yield reactive results 95% of the time. The numbers of LoD results included in the analysis varied depending upon the number of sample concentrations selected and prepared for testing. All analyses were performed using Microsoft Minitab® Statistical Software version 17.1.0.All analyses were performed using Statistical Analysis Software ), and WHO . DetailsMultiple clinical sensitivity (percent positive agreement) studies were completed with either retrospective or contrived samples obtained as venous WB, fingerstick WB, directly collected contrived saliva samples, or contrived saliva samples collected in VTM from either the USA or western Africa.  Details Details of the Sierra Leone study, the WHO reference panel study, and the nonhuman primate study are discussed below.® Ebola Rapid Antigen Test. These samples were tested in western Africa using a US FDA EUA Ebola Virus VP40 rRT-PCR Assay [® Ebola Rapid Antigen Test. Testing was performed in a randomized, blinded manner. The performance of the OraQuick® Ebola Rapid Antigen Test in comparison to the results generated by the EUA CDC Ebola Virus VP40 rRT-PCR Assay [A total of 75 retrospective, remnant WB samples collected from patients in western Africa (Sierra Leone) during the 2013–2016 EVD outbreak were tested using the OraQuickCR Assay  on the BCR Assay   [® Ebola Rapid Antigen Test demonstrated a sensitivity of 81.8% (9 of 11 samples) and 90.9% (10 of 11 samples) and a specificity (negative percent agreement) of 100% (15 of 15 samples) and 93.3% (14 of 15 samples) for venous and fingerstick WB, respectively, in this animal model testing. The positive predictive values were 100% (9 of 9 samples) and 90.9% (10 of 11 samples), and the negative predictive values were 88.2% (15 of 17 samples) and 100% (14 of 14 samples) for venous and fingerstick whole blood, respectively. Due to challenges in obtaining Ebola patient fingerstick WB samples, an NHP model was used in the development of the OraQuick studies , this wae course . All basay (DoD) , the Ora® Ebola Rapid Antigen Tests. The results of the OraQuick® Ebola Rapid Antigen Tests were in 100% agreement with all nine de-identified samples in the WHO proficiency panel (not shown).The 1st Reference Reagent 2016, lyophilized Ebola VP40 Antigen (Item #2016.2302) was obtained from the National Institute for Biological Standards and Control (NIBSC). A total of nine samples were prepared by reconstituting the reference material as per the manufacturer’s instructions. Testing was repeated daily for three days using the same lot of OraQuick® Ebola Rapid Antigen Test was determined using rAg VP40, Mayinga EBOV strain ZZXDK901/812094/VSP (provided by the CDC); and gamma-irradiated EBOV . The LoD of the OraQuick® Ebola Rapid Antigen Test was determined in different sample matrices  for rAg VP40 and inactivated EBOV.The LoD for the OraQuick® collection system, respectively. The LoD for the rAg VP40 analyte in WB was 0.053 µg/mL. After correction for the sample volume per test in each matrix, the OraQuick® device LoD in OF was 7 times more sensitive than the LoD in WB, 61 times more sensitive than the OF in the BD collection system, and 422 times more sensitive than the OF in the Σ-Virocult® collection system. The LoD in WB for the inactivated Ebola virus preparation was determined to be 1.64 × 106 TCID50/mL. Several collection transport media and collection containers were compared for the effects on LoD. When using rAg VP40, the LoDs were 0.106 µg/mL, 6.51 µg/mL, and 31.98 µg/mL in directly collected OF, OF in the BD collection system, and OF in the Ʃ-Virocult® Ebola Rapid Antigen Test was next tested for the ability to recognize antigenically distinct strains of Ebola. The inclusivity of the OraQuick® Ebola Rapid Antigen Test was determined only for known species of the genus Ebolavirus responsible for human infection in the past. The test was reactive for EBOV, SUDV, and BDBV  and non-reactive for TAFV and RESTV (The OraQuicknd RESTV . Ebolavirus) was tested for inclusivity and cross-reactivity in WB. The results confirmed the inclusivity results , and cholesterol at the select concentrations tested  and in VTM. The test was highly reproducible over three sites, five days (two runs/day), multiple operators, and three sample types  3 and 4 organisms  [® Ebola Rapid Antigen Test is equivalent to the other commercial tests. The OraQuick® Antigen Ebola Rapid Antigen Test and DPP Ebola Antigen System  are the only two rapid tests with FDA EUA authorization; however, the EUA approvals were phased out with the OraQuick® 510(k) clearance. The FDA EUA was withdrawn for the ReEBOV Rapid Antigen test in May 2018 at the request of the manufacturer Corgenix. In addition, SD Biosensor’s SD Q Line Ebola Zaire Ag test, a rapid antigen test for whole blood, plasma, and serum from live patients, has WHO EUAL approval. As of May 2018, six EBOV RDTs were listed for the detection of EBOV by stringent regulatory authorities  . Table SA 510(k) . Overall® Ebola Rapid Antigen Test is a reproducible, sensitive, and specific assay for the detection of EBOV in humans. This test was used in 2015 and 2018 in the western Africa and the COD EVD outbreaks, respectively. A target product profile (TPP) for EBOV diagnostics was developed by the Foundation for Innovative New Diagnostics (FIND), Doctors without Borders, the WHO, and partners in 2014 [Ebolavirus meet the “acceptable” TPP criteria and one meets the “desired” TPP criteria [The OraQuickcriteria ,14 for scriteria . Automat® Ebola Rapid Antigen Test, which has become the first FDA 510(k) RDT for the detection of viruses from the genus Ebolavirus. It is the first IVD test that has used animal data as part of validation for diagnostic use. Our data showed that the OraQuick® Ebola Rapid Antigen Test has clinical sensitivity of 84.0% in archived EVD patient venous WB samples and 97.1% in EVD patient cadaver buccal swabs, and it has clinical specificity of 98.0–100% in venous WB samples and 99.1–100% in contrived saliva samples, suggesting the diagnostic utility of this rapid test for timely detection of EBOV infections. One caveat to this study is that the logistical issues surrounding working with humans infected by this risk group 4 pathogen during an outbreak limited the ability to test prospective samples. Other RDT tests, such as the QuickNavi-Ebola, show promise and have overlapping CT sensitivity; however, this test targets the NP protein rather than the VP40 [The roles of NAAT and RDT in diagnosis are viewed as complimentary. Rapid point-of-care tests have great utility when new cases of suspected EBOV occur in remote locations and sample transport to a PCR testing lab would delay an immediate answer on whether the outbreak has spread. Employment of a POC RDT would be timelier under these circumstances. Presumptive positive samples can be confirmed later by PCR; meanwhile, those positive results should trigger immediate patient isolation and contact tracing to prevent disease spreading at the location. The outbreak in the eastern COD emerged in a complex political and security environment . Factorsthe VP40 . A more the VP40 .® Ebola Rapid Antigen Test is fast (30 min) and portable, with no need for electricity. This test can used for both WB (venous and fingerstick) and contrived saliva samples. Moreover, it has broad detection abilities for multiple species from the genus Ebolavirus . Due to the finite limitations affecting the sustainability of polyclonal products and normal lot variation, an ideal next iteration we are working on would be to have a monoclonal antibody. The testing results suggest that the analytical sensitivity performance of the OraQuick® Ebola Rapid Antigen Test is equivalent to or better than other commercial RDTs tested in this evaluation (The utility of the Ebola rapid detection assay makes the platform well-suited for detecting acute infections in a self-test format that can be deployed anywhere and can respect quarantine and social distancing. The OraQuickaluation .® Ebola Rapid Antigen Test is a reproducible, sensitive, and specific assay that can be used for the detection of EBOV in humans in both oral fluid and whole blood and could support efforts to control the spread of EBOV infections and outbreaks.EVD outbreaks remain a great public health threat, so it is important to quickly detect EBOV infection and take prompt EVD-specific interventions for isolation, treatment, and containment. The data demonstrate that the OraQuick


We discuss various land management options through other effective area-based conservation measures (OECMs) and the costs to deliver this strategy.As more ambitious protected area (PA) targets for the post-2020 global biodiversity framework are set beyond Aichi Target 11, renew thinking into spatial prioritisation is required to enable PA expansion that maximises environmental values. Our study focuses on the biodiverse and forest-rich Indonesian island of Sulawesi, which has a terrestrial PA network that covers 10% of the island. We used Marxan to investigate trade-offs in the design of an expanded PA network that prioritised different conservation features  under varying island-wide coverage targets . Our first scenario, which required existing PAs to be selected, required larger areas to meet these coverage targets, in contrast to our second scenario, which allowed for any part of the island to be chosen, irrespective of PA status. The vast Mekongga and Bangkiriang Landscapes, and Gorontalo corridor were consistently identified as a high priority for protection under all scenarios. To meet our conservation targets through expanding current PAs, creating new PAs, and creating corridors that connect existing PAs, we used a spatially explicit three-phase approach. Our findings identified 26,508 km While they may not be a perfect solution4, PAs often represent the last strongholds for threatened species and intact habitats, and provide a buffer against environmentally damaging development8. Terrestrial PA targets are typically set as a percentage of total land area, despite the fact that focusing on quantitative aspects while ignoring qualitative aspects and robust indicators may fail to fully recognise differences in biodiversity richness and habitat types10. Nevertheless, over the past 10 years, most countries have been working towards the Convention on Biodiversity (CBD) Aichi targets, in particular Target 11, which calls for the protection of at least 17% of terrestrial and inland water areas11. A forthcoming post-2020 Global Biodiversity Framework (GBF) will likely include a revised PA target of 30% of land conserved by 203014 and an ambitious target of 50% by 205016. This raises the question of how and where this can be achieved and how it can be done in a way that complements the current PA network in each country.Protected areas (PAs) are a mainstay nature conservation strategy18. The Government of Indonesia is currently formulating a new set of national PA targets as part of its efforts to update its National Biodiversity Strategy and Action Plan 2015–2020 in response to the post-2020 GBF. Setting meaningful targets for 2030 requires increasing the representativeness of PAs in Indonesia and their management effectiveness, rather than simply expanding the network by only using an area-based approach. Here, we provide an Indonesian case study, using systematic conservation planning to explore how best to achieve the post-2020 GBF target by increasing the coverage and representativeness of important conservation features inside Sulawesi’s PA network.As a signatory to the CBD, the Government of Indonesia is working to achieve the various Aichi targets and may further commit to the new post-2020 GBF 30% target. However, with 21.3% of its land area already under some form of protection, Indonesia has surpassed the Aichi Target 11 for terrestrial protection19. It is part of the Wallacea biodiversity hotspot21 and one of the 200 global ecoregions for conservation22. Despite its proximity to the islands of Borneo and Java, Sulawesi's biodiversity is unique27, with 98% of its non-flying mammal species, 37% of its bird species, and 26% of its reptile species being endemic30. Its extensive forests and mangroves store and sequester globally significant volumes of carbon32. The terrestrial area of Sulawesi spans 186,404 km2, of which 18,822 km2 (10.1%) is assigned within the island’s PA network. Of the 79 PAs in Sulawesi, Lore Lindu National Park (NP) was the first to be designated (in 1982) and Gandang Dewata NP the most recently created (2016), which indicates the government’s willingness, commitment, and ambition to expand its PA network. Sulawesi also has the world's largest nickel deposit33, which is currently the most sought-after element for the development of electric vehicle batteries34.Sulawesi is the eleventh largest island in the world. It is renowned for its biogeographical importance ever since Alfred Russel Wallace first explored it in the eighteenth century35 identified synergies between carbon and biodiversity conservation planning at a broader scale across Asia, including Sulawesi. Cannon et al. (2007) carried out a similar study in Sulawesi, which was restricted to forest condition  and a data set from more than 20 years ago. This foundational study showed that the priority conservation areas for that period were consistent with the areas chosen by government and non-governmental organisations, with several additional priority areas identified outside of the PA system. Our analysis builds on these two studies by incorporating more recent and detailed data sets and using a spatial approach that, in addition to forest condition, considers other critical ecosystem types, biodiversity, and carbon stocks, with detailed data layers at a finer resolution, that enables site-specific management recommendations to be made. In contrast to the Key Biodiversity Areas (KBA), which only uses biodiversity data in its prioritization, spatial conservation planning can combine biodiversity and other conservation features that are relevant for species and habitat management36. Our study aimed to identify priority areas by setting targets under different conservation planning scenarios by: (1) performing a PA gap analysis; (2) selecting high-priority areas in addition to the current PA network; and, (3) identifying implementation opportunities and strategies for forest conservation across Sulawesi.In this study, we conduct an island-wide spatial priority setting analysis for Sulawesi to inform PA planning and, more broadly, land-use management decision-making. A recent study38, to enable systematic conservation planning for Sulawesi. Our goal was to determine how to prioritise potential new conservation areas when faced with multiple candidate sites . We used a hexagonal grid of 1.3 km2 that served as the basic PUs within the landscape.We used Marxan version 2.43 simulated annealing optimisation, with an iterative improvement method39. Two provinces in Sulawesi rank fifth  and ninth  highest for poverty amongst Indonesia’s 34 provinces40. In Indonesia and Sulawesi, there are 13 different types of conservation areas  forest types defined from the government’s official Land Use Land Planning 2018 dataset (MoEF 2019); (iii) karst (MoEF 2019); (iv) distribution of important animal species (IUCN Red List website); and, (v) ultramafic outcrops (MoEF 2019).We used eleven conservation features and set targets based on the proportion of each feature under the three management scenarios Table , Fig. S1We combined primary dryland and secondary forest into a single forest cover layer and reclassified this based on their elevation range to include lowland (0–150 m asl), low elevation hill (150–500 m asl), medium elevation hill (500–900 m asl), sub-montane (900–1400 m asl), lower montane (1400–1900 m asl), montane (1900–2500 m asl), and tropical upper montane/sub-alpine (> 2500 m asl) .https://www.iucnredlist.org/search, accessed on March 16, 2022) using the following advance search category: Animalia is the Kingdom; Amphibia, Aves, Mammalia, and Reptilia are the Classes; CR and EN are the Red List Categories; and Sulawesi is the Land Region. We eliminated four species because they are not found in Sulawesi, possibly due to an error in the IUCN database (Vanellus gregarious and Pterodroma baraui), or because their distributions cover the marine area rather than the terrestrial (Chelonia mydas and Eretmochelys imbricate). We excluded VU species because several species, such as the Sulawesi giant squirrel, lacked fine-scale data and had distribution maps that were simply allocated to any forest patch on the island, rather than species occurrence. The final search yielded 12 mammals, 4 birds, 14 reptiles, and 3 amphibians . The Forest Landscape Integrity Index combines observed and inferred forest pressure and fragmentation data to produce a continuous index ranging from 1 to 10, indicating the degree of anthropogenic modification56. We inverted the value of intact forests so that the most integrated had the lowest cost. We created a single cost layer from multiple factors57 by averaging the 15 cost factors . We defined a total of 140,906 PUs on mainland Sulawesi and its outer islands, with each PU being a 1.3 km2 hexagon. Multi-edged PUs, such as hexagons, are more efficient in creating priority areas because they have a lower edge:area ratio than square grid cells. We ran the analysis in QMarxan for each scenario, starting with a Species Penalty Factor (SPF) of 1 and a constant boundary cost (1). The connectivity cost, also known as the boundary cost, is a value that represents connectivity; the higher the PU value, the more likely that it will be connected. By starting the analysis with a constant value, the algorithm ensures that all PUs are treated equally. We then calibrated the SPF and the Boundary Length Modifier (BLM). The SPF is calibrated to ensure that the penalties for missing conservation features are appropriately scaled and relative to one another. While the BLM is calibrated to find the best value that strikes a balance between area compactness and cost . To compare the spatial arrangement within and between scenarios, we used the kappa statistic58.To meet the conservation features targets, ensure connectivity between areas, and reduce the total cost of priority area management Fig. , we used57. We included 79 PAs  statistics, where 0 denoted no agreement, 0.01–0.20 low agreement, 0.21–0.40 fair agreement, 0.41–0.6 moderate agreement, 0.61–0.80 substantial agreement, and 0.81–1.00 the highest agreement60.We ran each of the three protection scenarios in two ways: with a requirement that all existing PAs be included (scenario ‘a’); and, without such a requirement (scenario ‘b’). PAs were included to identify new priority areas outside of those already protected. In scenario ‘b’, PAs were excluded to allow for spatial assessments based on representativeness, allowing for comparisons with existing PAs to assess their efficacy in meeting the conservation targetsAs Table  from theMarxan works by continually repeating the analysis to identify PUs that best achieve objectives. Due to the nature of the algorithm often each run can be slightly different. Selection frequency is the sum of how many times a PU was selected across all the runs. Areas designated as a priority have a high degree of selection frequency of more than 75 out of every 100 iterations. We merged the priority areas from the three percentage area scenarios and devised a plan for sequential implementation to make meeting this goal more realistic. We began by identifying high priority areas outside of the current PA network, as determined by the 17% scenario, and then expanded on this for the 30% and then 50% scenarios.Based on provincial delineation and governance structures, we divided the study area into six geographical regions Fig. : (1) Nor61 to evaluate them in the context of landscape conservation in Sulawesi. We overlaid the identified priority areas with current active concessions to assess the potential threats of establishing the priority areas as conservation areas. Priority areas that fall within the scope of a concession are regarded as being under threat of clearance. Using MoEF data, we identified plantation concessions: (1) Natural Forest Management Permit (Hak Pengusahaan Hutan); (2) Palm Oil Concession; and, (3) Industrial Plantation Forest (Hutan Tanaman Industri). The mining concessions are public data made available by the Directorate General of Mineral and Coal, Ministry of Energy and Mineral Resources.The identified priority areas were overlaid on the KBAsGiven the same iterations, all percentage coverage targets under scenario 'b' (PAs are not required to be included) had a lower mean cost and were more compact with less edge. With the exception of the 17% target, they also had lower mean boundary lengths than those under scenario 'a'  Table . Under sFigure Within the same target, the weighted Kappa statistic between scenarios 'a' and 'b' describes the spatial congruence of the two scenarios. The weighted Kappa statistic was the lowest for the 17% target (κ = 0.30), moderate for the 30% target (κ = 0.58), and significant for the 50% target (κ = 0.75). The lower the percentage target would result in the algorithm prioritizing noticeably different areas to meet the target, since the current PAs did not adequately represent the five conservation features . As the percentage targets increased, there was increasingly closer alignment between scenarios ‘a’ and ‘b’ for the size of the priority areas required to be selected to meet those targets.2), 9% of its karst , 15% of the distribution of important species , and 12% of its ultramafic soil . Only 18%  of the island's top quantile for soil carbon organic is located in its PA network. Including these priority areas in the current PA network increases the representation of conservation features  being outside of the PA, followed by the 580 km2 Bangkiriang landscape (Region 4), and 892.6 km2 Gorontalo corridor (Region 2)  spans 4729.6 km2, with the first order area serving as the core (892.6 km2). The Tokalekaju Mountain landscape (1145.4 km2) is a large single second order area (with 63 km2 of the first order), and the central Sulawesi corridor (2751.1 km2) that connects Gandang Dewata NP, Lore Lindu NP, and Pamona NR  , is located in the southern extent of this area. Another potential threat is the priority area along the northern boundary of Bogani Nani Wartabone NP, which overlaps with the PT Huma Sulut Natural Forest Management Permit area, suggesting that it could be logged in the future. This region’s karst is located within the Karakelang landscape, outside a PA.Aside from the PAs, the first order scenario 'a' adds 1,121.4 kmm2) Fig. . In compm2) Fig. . The PT 2 that overlaps with KBA Popayato-Paguat (first order priority area of 461.4 km2) and KBA Buol-Tolitoli (first order priority area of 317.4 km2) which we term as the Gorontalo corridor, dominated (68%) by low and medium elevation hill forest. The first order priority area in this landscape extends the border of Nantu NR, Gunung Dako NR, and Tinombala NR . This region includes PT Sentral Pitulempa and PT Taman Hutan Asri’s logging concession areas (HPH), as well as PT Inti Global Laksana and PT Cipta Cakra Murdaya’s oil palm concessions. Furthermore, the mining concession of PT Gorontalo Sejahtera overlaps with the southern part of Panua NR  is mostly an extended border of Lore Lindu NP, Gandang Dewata NP (PA ID 37), and a small polygon west of Pamona NP (PA ID 28). The Tokalekaju landscape, KBA Pambuang, and the Central Sulawesi corridor are connected as one large landscape in scenario ‘b’ with a significant amount of first order priority areas outside the current PAs  belonging to three companies with National Forest Management Permits. One of them, PT Satya Sena, has a concession that overlaps with 15.6 km2 of a first order priority area. Furthermore, 560.4 km2 of the Central Sulawesi corridor is within a concession, primarily the Natural Forest Management Permits of PT Rante Mario and PT Inhutani I Mamuju.The overlap of Pegunungan Tokalekaju KBA with the Natural Forest Management Permit area of PT Satya Sena Indratama, Pambuang KBA with the Natural Forest Management Permit area of PT Inhutani I Mamuju, and Sulawesi Tengah Grand Forest Park with the mining concession PT Citra Palu Mineral indicate the potential development impacts that may occur in this region. Some 40% of the Tokalekaju Landscape is under concessions (456.4 km2) , contains a significant amount of karst , and because it is part of the East Sulawesi crescent geology area, it has a significant amount of ultramafic soil . Scenario ‘b’ shows the importance of priority areas outside of PAs by selecting the majority of first and second priority areas outside of the PA network  Fig. . It streore Fig. . The Banork Fig. , with a 2), including PT. Bina Balantak Raya, PT. Dahatama Adi Karya, PT. Palopo Timber Company, and PT. Satyaguna Sulajaya, may have forest impacts. The first company is the largest, with 292 km2 of its concession overlapping with the KBA Bangkiriang. Another potential impact is from the PT Sinar Makmur Cemerlang’s mining concession that is located within the KBA Bangkiriang.Concessions with Natural Forest Management Permits . This patch contains karst (854.7 km2) and Mekongga’s only patch of ultramafic soil (301.7 km2). Scenario ‘b’ depicts these three patches as a single continuous landscape with a large first order and a second order in the buffer zone  and Rawa Aopa Watumohai NP (PA ID 65). Based on our scenarios ‘a’ and ‘b’, this gap in area is prioritized. Scenario ‘a’ identified three large standalone patches of first and second priority areas that overlap the KBA Mekongga and cover 3,346.9 km2 Fig. . The larone Fig. .2 of forest that overlaps with the KBA Routa  and contains both karst (165.3 km2) and ultramafic soil (385.5 km2). The final significant standalone area is an unnamed landscape located east of Danau Matano (PA ID 43) and Danau Mahalona (PA ID 44)  and a significant proportion of ultramafic soil . The Routa landscape and the unnamed landscape to the north are depicted as one continuous landscape in scenario ‘b’  Fig. . This is‘b’ Fig. . There a2), and contains 83% of montane and upper montane forest  evaluated PA effectiveness in the Indo-Malayan Realm in the 1980s. Except for Dumoga-Bone NP (now Bogani Nani Wartabone NP), Tangkoko Dua Saudara NR, and Lore Lindu NP, they concluded that Sulawesi's PAs were generally inadequately protected. Our work reveals a shifting baseline with regards to the essential areas to protect. It also benefits from detailed data layers and analytical techniques that have recently become available. There are considerable gaps in the areas identified as priorities in the PAs exclusion scenario (‘b’) compared to those considered when Sulawesi’s PAs were established nearly 40 years ago. Three of the six PAs identified as essential by MacKinnon and MacKinnon, 1986 are not identified as priorities under scenario 'b' . Nonetheless, both our research and that of MacKinnon and MacKinnon (1986) identify Lore Lindu NP and Bogani Nani Wartabone NP as conservation priorities.Macaca nigra) population62. The Macaca nigra distribution was included in our analysis . Many PAs in region 2  would benefit from increased protection status beyond their current designations.Hutan Lindung). They typically have a lower protection status than national parks because there is no technical implementation unit, unlike national parks, that is directly responsible for them and, as a result, no direct funding for their protection. An ‘Essential Ecosystem Area’ is another plausible option, although their size is generally much smaller than that of a PA. For example, in 2017 the 0.15 km2 Taman Keanekaragaman Hayati (Biodiversity Park) Oheo Essential Ecosystem Area in North Konawe Regency67, was established near the Mekongga landscape. Given their size and importance, the Mekongga and Bangkiriang landscapes may even merit national park status and the increased resources that this would bring. This study supports Mekongga's previous effort to promote the area's status as a national park69.For a continuous large landscape that is not connected to any PA, notably Mekongga and Bangkiriang, another type of protection is required. Parts of the two landscapes are also managed by the local government as part of Indonesia's watershed forest, also known as Protection Forest  schemes, offer an option for expanding the conservation area network73, as do privately managed restoration concessions, which could be managed under an Ecosystem Restoration Concession licence. Another framework such as indigenous lead conservation area could also be considered. Currently there 207,000 km2 of indigenous land in Indonesia, however only 15% of it are certified 74. In Sulawesi there are 16,000 km2 of indigenous land with the largest in the South Sulawesi province with 8,188.4 km275. The second largest is in the Central Sulawesi province with 6,298.2 km2, this include five indigenous lands overlapping the Bangkiriang landscape: Taa Mpoa (168.5 km2), Taa Salaki (236.7 km2), Taa Vananga Bulang (220.1 km2), Lipu Wana Posangke (305.8 km2), and Wana Kayupoli (179 km2)76.One option would be through OECMs with long-term conservation effectiveness outside of the designated PA networkwww.naturepositive.org). Following the first ‘Retain’ step, the Mitigation and Conservation Hierarchy’s subsequent steps are to ‘Reduce’ the impact (e.g. through an agri-environment scheme), ‘Restore’ (e.g. ecosystem restoration concession), and ‘Renew’  that is endemic to Mount Sahendaruman, Sangihe Island. This species is highly vulnerable to habitat change because its entire range is within the PT Tambang Mas Sangihe gold mining concession, which encompasses half of Sangihe island. The Sangihe Island’s significance was recognized as early as the early 80 s by20, who prioritised it for reserve establishment.Increasing PA coverage is a key first step towards meeting the target of being nature positive by 2030 set by the Global Goal for Nature (2) of degraded land on Sulawesi being assigned for protection. The final mitigation hierarchy step of renewal, or rewilding, is still a foreign concept in Indonesia, but one that is gaining popularity78.The mitigation hierarchy’s third step of restoring degraded areas aligns with the post-2020 target of restoring 20% of degraded terrestrial land and ensuring landscape connectivity. Indonesia may still fall short of this goal, with 7.6% (1,917 km79. Although the focus of this study is on potential governance models, such as strictly managed PAs, to protect these vital but currently unprotected areas, it should be noted that protection does not always imply a fenced park. Other more participatory options, such as local community and indigenous people participation in conservation area management, are important and could be accommodate under future OECM designation, should this become an option. In the meantime, advancing Indonesia’s FOLU Net Sink 2030 strategy remains vital to safeguarding Sulawesi's rich natural capital while providing critical benefits to its people.There is no set number for protection percentage that will guarantee that the PA network ensures the survival of ecosystem processes and biodiversity. However, in the case of relatively small, safeguarded areas, it appears safe to say that, in general, the higher the percentage target, the better. Decisions must be science-based while also considering equity for rural communities that rely on natural resources and benefit from healthy ecosystem servicesSupplementary Information.