Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. As a result, extensive time and effort must be spent on per-assay QC feature thresholding, and valid images and phenotypes may be discarded while image- and cell-level artifacts go undetected.ARcell) provides a single robust assessment of image quality regardless of the underlying causes of quality issues. Gating on this single intuitive metric, partially contaminated images can be salvaged and highly contaminated images can be excluded before image-level phenotype summary, enabling a more reliable characterization of cellular response dynamics.We present a novel cell-level QC workflow built on machine learning approaches for classifying artifacts from HT image data. First, a phenotype sampler based on unlabeled clustering collects a comprehensive subset of cellular phenotypes, requiring only the inspection of a handful of images per phenotype for validity. A set of one-class support vector machines are then trained on each biologically valid image phenotype, and used to classify individual objects in each image as valid cells or artifacts. We apply this workflow to two real-world large-scale HT image datasets and observe that the ratio of artifact to total object area (ARcell that summaries the ratio of artifacts contained in each image can be used to reliably rank images by quality and more accurately determine QC cutoff thresholds. Machine learning-based cellular phenotype clustering and sampling reduces the amount of manual work required for training example collection. Our QC workflow automatically handles assay-specific phenotypic variations and generalizes to different HT image assays.Our cell-level QC workflow enables identification of artificial cells created not only by staining or imaging artifacts but also by the limitations of image segmentation algorithms. The single readout  With the advance of high throughput (HT) imaging technology and related automated image analysis tools, scientists may conduct daily cell-level probes of biological systems under many treatment conditions –3. HowevA fundamental limitation of this approach is that existing QC metrics not only reflect fluctuations in image quality but also shift with natural phenotypic variations. For example, DNA accumulation in apoptotic cells can lead to more saturated pixels, high protein expression ratios may lead the image intensity into a wider range, and a shift to fewer but larger cells leads to fewer edge pixels and therefore less high frequency components in the image power spectrum. The dependency of such QC metrics on cellular context such as cell counts and morphology can cause images with valid phenotypic variations to be discarded, leading to low true positive rates. Likewise, these QC metrics may also be unaffected by true cell-level artifacts, such as segmentation failures from treated cells adhering together and forming blobs, leading to low true negative rates as well.The limitations of existing image-level QC metrics suggests the necessity of studying QC at the cell level. In addition to tackling the QC challenge in HT image analysis, cell-level artifact detection has also become increasingly vital due to the growing interest in studying and understanding the heterogeneous behavior embedded in cell subpopulations . For samARcell). To our knowledge, this represents the first QC method and scoring metric that operate at the cell level. Due to the lack of standardized ground truth image quality scores for HT image data to directly compare against, we demonstrate the benefits of our approach by applying it to two real-world assays, showing how it favorably compares to existing image QC methods in a variety of applications.To overcome the aforementioned limitations of current QC methods and improve the robustness of cellular heterogeneity studies, we developed a generalized  QC workflow to flag individual artificial cells in HT imaging, enabling both more reliable cellular profiling and straightforward assessment of image quality as evaluated by the ratio of artifact area to total detected cell area  to perform cell segmentation and quantitative measurements at both image and cell level using standard modules. As a result, every image is characterized by features including focus scores, intensity, correlation between pairs of staining markers (if more than one marker is collected), and signal-to-noise (SNR) ratio [We employ the open-source image analysis software CellProfiler  ratio . To miniR) ratio ) to idenR) ratio , to miniThe premise of our workflow is to build a set of classifiers trained by a comprehensive collection of biologically valid phenotypes, and then identify an object as an artifact if it does not resemble any of these phenotypes. This new approach requires the collection of artifact-free cell samples, in order to treat artifact identification as an anomaly detection machine learning problem. To ensure the effective sampling of all artifact-free cells, our phenotype sampler takes two steps: outlier-based removal of artifact-dominated images, and phenotype-preserving image sampling.First, we wish to identify and remove images that predominantly contain artifacts via image-level phenotype assortment Fig. a.i-ii, shi, for the ith feature dimension and treat this hi as a base sampling rate. The final sampling rate is determined by multiplying hi with an integer K, which controls the tradeoff between sample set size and computation time. We iteratively construct the sparse, full-feature space sampling grid by starting with any two feature dimensions and, before adding another dimension, removing any grid point whose distance towards its best image match (a data point obtained through nearest neighbor search) is beyond r = D is the dimensionality of the feature space in the current iteration. After iterating through all selected image features, the resulting multidimensional sampling grid is a low-resolution representation of the raw data set . To handle the variation in feature distribution with respect to different subgroups and choose a more suitable number of effective subgroups automatically, sampled images are then clustered by a variational Bayesian Gaussian Mixture model [nup. With the grouping determined, we output a set of representative images from each group for manual inspection to determine whether the group represents artifacts. Artifacts may be biologically valid but algorithmically problematic, for example, treated cells may adhere to each other and form blobs that cannot be segmented  for gamma. We choose a small constant for nu (1E-3) since we have high confidence in the sampled inlier training data output by our processing pipeline and want the SVM to positively identify a very high proportion of this data. The impact of different choices for parameters nu and gamma on the ARcell metric will be demonstrated later with a real-world case study.Each one-class SVM is defined by two parameters: ARcell values and corresponding image masks is given in Supplementary Fig. ARcell histogram to gain intuition about the image quality distribution. In the given example, due to the long tail of the distribution, a stringent, low ARcell threshold could be set without sacrificing a large percentage of images. Alternatively, an intuitive absolute threshold such as 50% could be used if the downstream processing pipeline is more robust against whatever anomalies are observed in the higher-scoring images. A QC process necessitates making a tradeoff between saving partially contaminated images and increasing statistical confidence, and this choice is intrinsic to the nature and goals of each study and assay. This approach helps quantify the decision-making process while still allowing room for domain-specific decision-making by the users.An example histogram of Our approach targets artifacts at the cell level, relying on a preprocessing stage (i.e. CellProfiler) for segmenting the cells. This segmentation in turn is usually limited by the number of images or phenotypes tested during the development of the preprocessing image segmentation solution. Thus, in our framework, we also apply the phenotype sampler ahead of designing the preprocessing cell segmentation solution  and PowerLogLogSlope (PLLS) Fig.  and founion Fig. b as wellnu and gamma when training the SVMs for this assay. Part A shows the ARcell values and corresponding image masks  for an artifact-containing image under different parameter values. Higher nu and gamma tighten the decision boundary of the SVM classifier around the cluster of training images corresponding to a particular phenotype, while lower nu and gamma expand the boundary to be more permissive. In Part B we find that varying our chosen value of nu = 1E-3 by a factor of 10 changes ARcell by over 1% in 0.08% of images (at 1E-4) and 6.3% of images (at 1E-2). Since the image clusters have higher density towards the center of each cluster, we expect this behavior whereby increasing nu has a larger effect on ARcell than decreasing it. While the parameter values we list provided accurate classification for both of our assays, users can replicate this parameter variation experiment if they find the SVM classifiers are too permissive or restrictive.In Supplementary Fig. ARcell > 50%), as the remaining non-artifact cell population may not be sufficient to characterize the well-level phenotype 4). Four nty Fig. . Also, acts Fig. .Fig. 4DARcell, the ratio of artifact to total object area in an image. The second insight is that high-quality cell-level artifact detection can be achieved through machine learning techniques: the properties of screening assays permit an unsupervised clustering approach to detect valid phenotypes with greatly lessened user intervention, and these phenotype clusters can then be sampled to train a set of classifiers to perform the aforementioned cell-level artifact detection.We have presented a QC workflow built on two novel insights that gives users more robust and fine-grained quality control capabilities, while avoiding the lengthy and difficult process of simultaneously thresholding multiple QC features for every assay. The first insight is that cell-level artifact detection enables a single powerful, phenotype-invariant, image-level QC measure in the form of Our results demonstrate the utility of model-based classification in filtering out artifacts while retaining valid phenotypes across the feature space of image quality metrics. Model-based classification approaches require manual sampling of training examples, making it extremely likely to overlook phenotypes of biological interest within the hundreds of thousands of well images in a production HT imaging assay. Our workflow avoids this issue and saves user effort by performing phenotype grouping using an unlabeled clustering methodology, built upon assumptions that are sound in the context of HT screening. It takes advantage of the guilt-by-association property embedded in HT screening whereby multiple images share biologically relevant phenotypes, and so implements a density-based outlier detector to remove images with prominent artifacts before sampling. To eliminate any remaining non-outlier artifacts from training examples, we group sampled images by their phenotypes for ease of inspection.ARcell provides an intuitive quality ranking more in line with human visual assessment than previous scoring systems. Gating on ARcell, images largely contaminated by artifacts can be excluded and less impacted ones can be salvaged before downstream analysis. In our experiments, we successfully remove artifact noise with our cell-level QC tool, enabling more accurate subpopulation identification and precluding the need for noise-reducing aggregation approaches that discard meaningful biological information.We demonstrated the capabilities of our proposed QC workflow by applying it to two large-scale HT screening assays, covering a range of cell lines, treatments, and image acquisition settings. Our QC metric Our QC workflow identifies artifacts at cell level, using an external cell segmentation solution. The phenotype grouping and manual inspection of phenotype sample images acts as a last line of defense against common artifacts, but on rare occasions erroneously segmented fragments from large artifacts may resemble valid phenotypes closely enough to be misclassified . This aAdditional file 1: Section 1. Sample preparation and imaging. Figure S1. Images from a HT assay form clusters in the multi-dimensional feature space, with two such feature dimensions plotted. Figure S2. Example application of ARcell for QC inspection, ranking, and thresholding. Figure S3. Representative images are collected to build a robust cell segmentation solution. Figure S4. A comparison between image-level QC measures and ARcell in Assay α. Figure S5. Effect of one-class SVM hyperparameter nu and gamma variation on well image ARcell scores. Figure S6. Cell-level QC improves the accuracy of population analysis. Figure S7. Dose response consistency across replicates before and after cell-level QC, assay α. Figure S8. A comparison between ARcell and defocused patch ratio under different defocus level cutoffs.


While several multigene signatures are available for predicting breast cancer prognosis, particularly in early stage disease, effective molecular indicators are needed, especially for triple-negative carcinomas, to improve treatments and predict diagnostic outcomes. The objective of this study was to identify transcriptional regulatory networks to better understand mechanisms giving rise to breast cancer development and to incorporate this information into a model for predicting clinical outcomes.Gene expression profiles from 1097 breast cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Breast cancer-specific transcription regulatory information was identified by considering the binding site information from ENCODE and the top co-expressed targets in TCGA using a nonlinear approach. We then used this information to predict breast cancer patient survival outcome.We built a multiple regulator-based prediction model for breast cancer. This model was validated in more than 5000 breast cancer patients from the Gene Expression Omnibus (GEO) databases. We demonstrated our regulator model was significantly associated with clinical stage and that cell cycle and DNA replication related pathways were significantly enriched in high regulator risk patients.Our findings demonstrate that transcriptional regulator activities can predict patient survival. This finding provides additional biological insights into the mechanisms of breast cancer progression. Breast cancer is the most frequently diagnosed cancer and the second leading cause of cancer deaths in women worldwide . In the Currently, breast tumor classification is primarily based on histopathologic features and the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) . These sTranscriptional regulators, including the transcription factors, cofactors, chromatin remodelers, histone modification proteins and other DNA binding proteins, play fundamental roles in many cellular processes including response to extracellular and intracellular signals. By binding to promoter regions of genes, transcription regulators are able to up- or down-regulate specific target genes, thereby affecting many cellular activities. In recent years, several studies have implemented machine-learning methods in cancer prognostic biomarker development. For example, a support vector machine (SVM)-classifier has been used to construct a breast cancer prognostic signature consisting of 10 microRNAs that accurately predicted breast cancer stage . MoreoveWe hypothesized that transforming gene expression profiles into transcription regulator activity levels would reduce the dimensionality while retaining the most useful information from the gene expression profile during feature selection and the model training process. Herein, we built a transcription regulator-based model by mining gene expression profiles from 1097 breast cancer patients obtained from The Cancer Genome Atlas (TCGA). We implemented a rank-based score function to estimate the regulator activity levels to build the prediction model. In addition, the predictive power of this transcription regulator activity-based model was validated on over 5000 breast cancer patient profiles in the Gene Expression Omnibus (GEO) database. Gene set enrichment analysis demonstrated that the transcriptional regulator-based prognostic signature identified key pathways involved in breast cancer development and progression. Thus, transcription regulator activity models are expected to provide new information that could be used to develop new treatment strategies for breast cancer.http://xena.ucsc.edu, version 2017-10-13) [Breast cancer gene expression datasets and the corresponding clinical data were downloaded from TCGA and GEO databases. TCGA breast cancer RNA-sequencing data (log2(norm_count + 1) normalized) , includi7-10-13) . Validat7-10-13) . For michttp://rna.sysu.edu.cn/chipbase/) [The transcriptional regulators and their target genes list was retrieved from the ENCODE project via ChIPbase V2 (ipbase/) , 19. TheThe Cox proportional-hazards model was used to select the candidate transcription regulator that significantly associated with patients’ overall survival , 21. A mp-value less than 0.05 was considered as statistically significant. The survival analyses were conducted with the survival package in the statistical environment R (v3.5.1). Statistical computing and visualization were conducted with R.Patients were then divided into high-risk or low-risk groups using the median risk score as the cutoff . The Kaphttp://www.broadinstitute.org/gsea) [Gene set enrichment analysis was conducted with GSEA software (rg/gsea)  using thrg/gsea)  and a faRNA-seq profiles from 1097 primary breast cancer patients were used as the training set. Transcription regulator binding site information was extracted from ChIP-seq data contained within ENCODE. This data included 180 regulators and their gene targets. Additionally, 29 breast cancer studies in GEO were used as a validation set   Fig. b.Fig. 3In addition, we found that more hormone receptor negative (ER- and PR-negative) breast cancer patients were assigned to the high-risk group. Likewise, the basal-like and HER2-enriched breast cancer patients also were in the high-risk group, which is consistent with the clinical findings  that HERTo test whether the high performance of the risk-score model in the training dataset might have resulted from overfitting, we evaluated the performance of the transcription regulator activity model using independent breast cancer datasets from the GEO. Consistent with the training set, patients with high risk scores showed a clear trend of decreased survival rate in 23 of 29 GEO studies  on the TCGA breast cancer cohort. Compared with the patients assigned as low-risk, the high-risk patient group showed that the top two enriched pathways were cell cycle Fig. c and PLKIn this study, a transcriptional regulator-based model was established for predicting the prognosis of breast cancer. By transforming gene expression profiles to transcription regulator activity levels, we demonstrated that regulon activity can be used to explore breast cancer gene expression data. After identifying that regulons significantly associated with breast cancer overall survival, we further constructed a multi-regulator activity-based prediction model for breast cancer. The finding from the training set was validated in different independent GEO sets (> 3500 patients), which indicates that the regulator activity-based model is robust.Among the regulators that were used in the model, several are known to play a role in breast cancer. The chromatin remodeler CHD7 is one of the most commonly amplified CHD genes in breast cancer, and mRNA expression levels of CHD7 are significantly upregulated in basal-like breast cancer . The hisWe hypothesized that limiting a prognostic gene signature to transcription regulators would reduce the dimensionality of the expression profile during feature selection and model training process. Additionally, we predicted that such a transcription regulator activity profile would be less susceptible to variability in the expression of individual genes and may thereby improve the prognostic significance of tumor gene profiling. To the best of our knowledge, a breast cancer prognostic signature with high prediction power has not yet been constructed using only information provided by transcription regulatory factors. Compared with other published molecular signatures and panels for breast cancer, this transcriptional regulator-based signature was highly robust across different datasets and platforms with very large-scale breast cancer samples, as the TCGA data was from RNA-sequencing, while various microarray platforms were used in GEO. The robustness of this model arises primarily from two aspects. Firstly, each transcriptional regulator activity was estimated using hundreds of direct target genes by considering both co-expression and ENCODE results, which produced a highly stable model without perturbation due to the variability of single gene expression levels. Secondly, the transcription regulator activity estimation was based on the rank of the absolute expression value of its target genes, which enabled it to perform well across different gene expression platforms. In addition, the transcriptional regulator activities were estimated in a tissue-specific manner, which allows the regulator activity based prognostic risk score to deliver additional biological information from breast cancer mRNA expression profiles.Limitations of this study include the assumption that mRNA expression levels from the different platforms were measured appropriately and reflect the actual mRNA abundance of each gene. Secondly, the binding targets of the regulators obtained from ENCODE were not specific to breast cancer, so it is possible that a given regulator might not bind to all of the same targets in breast tumors. Thirdly, the cutoff for each dataset was determined separately based on the median risk score of the respective dataset; however, we did find that the value of the risk scores were in a similar range (− 12 to − 5).In the present study, we built a robust model for predicting overall survival based on biologically relevant transcriptional regulator information and further validated it using large cohorts of breast cancer patients. The transcription regulator model should enhance our understanding of breast cancer progression and guide personalized treatment selection.Additional file 1: Table S1. GEO datasets used in this study. Figure S1. Transcriptional regulator model predicts survival in breast cancer datasets. Figure S2. The regulators risk groups associated with average survival time. Figure S3. Comparison of the performance of the regulator model with MammaPrint.


KwaZulu-Natal, one of South Africa’s three malaria endemic provinces, is nearing malaria elimination, reporting fewer than 100 locally-acquired cases annually since 2010. Despite sustained implementation of essential interventions, including annual indoor residual spraying, prompt case detection using malaria rapid diagnostics tests and treatment with effective artemisinin-based combination therapy, low-level focal transmission persists in the province. This malaria prevalence and entomological survey was therefore undertaken to identify the drivers of this residual transmission.Malaria prevalence as well as malaria knowledge, attitudes and practices among community members and mobile migrant populations within uMkhanyakude district, KwaZulu-Natal were assessed during a community-based malaria prevalence survey. All consenting participants were tested for malaria by both conventional and highly-sensitive falciparum-specific rapid diagnostic tests. Finger-prick filter-paper blood spots were also collected from all participants for downstream parasite genotyping analysis. Entomological investigations were conducted around the surveyed households, with potential breeding sites geolocated and larvae collected for species identification and insecticide susceptibility testing. A random selection of households were assessed for indoor residual spray quality by cone bioassay.A low malaria prevalence was confirmed in the study area, with only 2% (67/2979) of the participants found to be malaria positive by both conventional and highly-sensitive falciparum-specific rapid diagnostic tests. Malaria prevalence however differed markedly between the border market and community (p < 0001), with the majority of the detected malaria carriers (65/67) identified as asymptomatic Mozambican nationals transiting through the informal border market from Mozambique to economic hubs within South Africa. Genomic analysis of the malaria isolates revealed a high degree of heterozygosity and limited genetic relatedness between the isolates supporting the hypothesis of limited local malaria transmission within the province. New potential vector breeding sites, potential vector populations with reduced insecticide susceptibility and areas with sub-optimal vector intervention coverage were identified during the entomological investigations.If KwaZulu-Natal is to successfully halt local malaria transmission and prevent the re-introduction of malaria, greater efforts need to be placed on detecting and treating malaria carriers at both formal and informal border crossings with transmission blocking anti-malarials, while ensuring optimal coverage of vector control interventions is achieved. With a sustained national malaria incidence of less than 1 malaria case per 1000 population at risk, South Africa began implementing its 5-year malaria elimination strategy . Using tPlasmodium falciparum parasites [Anopheles funestus vector populations [KwaZulu-Natal bore the brunt of the 1999/2000 malaria epidemic, reporting in excess of 40 000 cases and over 300 deaths in 2000 . Driversarasites , 7 and tulations . Responsulations  in the pulations , have suSince 2005, KZN has accounted for less than one percent of South Africa’s malaria burden, with its three endemic districts, uMkhanyakude, King Cetshwayo and Zululand Fig. b, achievIt is against this backdrop that this study was undertaken to identify, describe and define the factors driving and sustaining low-level malaria transmission in KZN. It is envisaged that the data generated will contribute to a holistic understanding of the various elements contributing to residual transmission in northern KZN, and inform elimination intervention selection and implementation towards sub-national malaria elimination by 2021.P. falciparum and Anopheles arabiensis the dominant parasite [® falciparum-specific rapid diagnositic test  or blood smears and treatment with the artemisinin-based combination therapy, artemether–lumefantrine .This study was conducted in areas identified as hotspots of local transmission, namely the municipalities of Jozini (a peri-urban area) and uMhlabuyalingana  in uMkhanyakude district, KZN, over a 6-week period in February and March 2018. Malaria transmission in this region occurs mainly during the hot, wet summer months from September to May, with parasite  and vectparasite  species,A mixed-methods approach was employed to facilitate an in-depth examination of potential malaria risk factors. The first approach was a community-based, household-level malaria prevalence survey in which participants were tested for malaria using standard and highly-sensitive falciparum-specific RDTs, and were assessed on their malaria-related knowledge, attitudes and practices (KAP). To understand the movements of the migrant and mobile populations (MMPs) and their potential contribution to sustained transmission, the second component comprised an assessment of individuals visiting the KwaPhuza border market Fig. c, situatThe community-based, household-level survey was restricted to two types of localities: those reporting at least one locally-acquired case  during the previous two malaria seasons, and those where MMPs are thought to frequent. As the edges of these communities are somewhat diffuse, the official administrative boundaries from Statistics South Africa (StatsSA) may potentially exclude portions of communities, so each StatsSA boundary was extended outwards by 1 km. These extended borders defined the sample frame and study localities. To obtain the study sample, study localities were subdivided into a grid comprised of 500 × 500-m blocks  were invited to participate. All those who consented were surveyed.Entomological investigations were conducted in and within a 2 km radius of any surveyed household where a malaria case was detected during the survey. In study localities where no cases were detected, at least two randomly selected surveyed households were subjected to entomological investigations.All individuals over 2 years of age present at the selected household or visiting KwaPhuza border market were invited to participate in the study. Prior to blood sampling and survey administration, written consent was obtained from individuals ≥ 18 years of age, with written consent from a guardian/caregiver/parent of individuals between 2 and 18 years. Assent was also obtained from children aged between 6 and 18 years.P. falciparum HRP2 Detection Rapid Card Test, Premier Medical Corporation Ltd, India) and highly sensitive RDT . This was done to compare the performance of the highly sensitive RDT to the standard RDT in a low-transmission rural South African setting. Filter-paper finger-prick blood samples were collected on Munktell TFN cards  and labelled with unique patient identifiers to ensure linkage of molecular and demographic data. Air-dried blood samples were packaged individually in zip-lock packets containing desiccant and transported to the National Institute for Communicable Diseases (NICD) in Johannesburg for further analysis. Malaria RDT-positive individuals were treated on-site with artemether–lumefantrine in accordance with South African malaria treatment guidelines [All consenting participants from the community and border market were tested for malaria by standard RDT , RStudio  and Stata 15.0 . Odds ratios (OR) associated with malaria risk were generated using univariate analysis and variable logistic regression models which took into account correlations at the locality level. Confidence limits were set at 95% with p < 0.05 considered significant. Geospatial mapping and analysis was conducted using ArcGIS .Initially 2096 community members from 1214 households consented to malaria testing and KAP survey administration. However, two participants withdrew their consent after receiving their test results, resulting a final study sample of 2094 participants from 1214 households. Willingness to participate in the survey was extremely high, with only one household head denying the survey team permission to administer the survey in their household. Data on the number of households where no one was present at the time of the survey were unfortunately not collected. Anecdotal evidence however suggests that this number was low. At the border-market, all 885 individuals approached, consented to malaria testing, and provided both travel histories and a filter-paper blood sample. The targeted sample size of 4000 was not achieved due to fewer participants than expected present at the selected households at the time of survey administration together with a week-long disruption of survey activities due to localized flooding.Malaria prevalence was low, with only 67 2%) of the 2979 sampled individuals testing positive for malaria by the standard falciparum-specific malaria RDT. The malaria burden however differed significantly between the community (2/2094) and border market , with the border market accounting for 97% (65/67) of all cases detected. Based on travel history data, all cases detected during this study, both at the border market and in the community, were classified as imported from neighbouring Mozambique. Good quality genotyping data were available for 46 parasite isolates (68.7%) and revealed that the parasite isolates were genetically diverse and complex with limited genetic relatedness . The unemployment rate was incredibly high  in spite of just over 50% (1031/2001) of the sampled population having secondary education or higher . Males tested at the border market were more likely to be infected with malaria compared to the females . The majority of the individuals who provided travel information indicated that they were planning on travelling to major South African cities Fig. .Fig. 3a Anopheles breeding sites identified during the survey were geolocated, classified according to breeding status and mapped  malaria transmission in the province. However, vigilance in malaria surveillance and response must remain a priority, as the attainment of malaria elimination is under threat from numerous factors, including malaria importation from neighbouring high-prevalence countries and insecticide resistance.The study parasite isolates were found to be highly diverse and complex with limited levels of genetic relatedness, distinctive features of parasites from a high transmission area. These findings supported the classification of all detected cases  as imported based on travel history data. The rapid development of both formal and informal global transport networks has increased human mobility and the speed infectious diseases spread . Within All the identified malaria carriers in this study were asymptomatic, a likely consequence of acquired immunity due to Mozambique’s higher transmission intensity. The majority of these malaria carriers would have most likely evaded passive case detection, potentially seeding and sustaining secondary (introduced) transmission in receptive areas of KZN, had they not been detected and treated at the informal border crossing. In addition, many of MMPs interviewed indicated that they were transiting to major non-endemic cities within South Africa, potentially placing an increased burden on the health systems in these cities, if they became symptomatic and required treatment. As this survey was conducted outside of the peak transmission periods, it is very likely that levels of both HPM and asymptomatic carriage have been significantly underestimated, notwithstanding the likelihood that population movements differs across the year. This, together with the study findings strongly emphasize the critical role the mobile border malaria surveillance units play in reducing the opportunities for these asymptomatic carriers to seed secondary transmission in KZN, particularly as entomological investigations confirmed the presence of numerous receptive areas within the province. The ability of the mobile surveillance units to prevent onward transmission has been further enhanced with the recent addition of the gametocidal drug, primaquine, to their case management toolkit.Acknowledging the threat asymptomatic malaria importation poses to malaria elimination in southern Africa, the MOSASWA  cross-border collaboration has implemented two strategies to address asymptomatic malaria importation both in source and sink areas . The firThe current point of care diagnostic in South Africa, a conventional falciparum-specific RDT, appeared to be sensitive enough to detect asymptomatic carriage as no additional malaria cases were detected by standard PCR or ultra-sensitive falciparum-specific RDTs. While similar results have been observed in low-transmission areas of the Gambia , they diThe severe under-sampling of school-going children and employed adults, a consequence of the community-based survey being conducted on weekdays between the hours of 09h00 and 15h00 when these populations groups were not present, was a major limitation of this study. As these groups are often highly mobile, their exclusion could have biased the finding of very limited indigenous transmission within the community. However, given the limited number of indigenous cases reported from the communities within the study area during the previous two malaria seasons, this seems highly unlikely.Encouragingly, despite KZN’s current extremely low malaria prevalence, awareness of malaria and its associated dangers was high within the community. It was however surprising to note that while most participants knew the common signs and symptoms of malaria, headaches and feeling cold were more frequently listed than fever. A similar trend was observed in an earlier study , highligSustained effective coverage of efficacious vector control inventions is fundamental to the success of any elimination strategy . The subThis study identified the continual introduction of malaria parasites into receptive areas through asymptomatic importation from neighbouring Mozambique as a potential significant driver of residual indigenous transmission in northern KZN. Routine active detection at known informal border crossings, the introduction of the transmission-blocking drug primaquine and the strengthening of cross-borders initiatives are a few of the elimination strategies implemented to address imported and introduced malaria. It should be noted that current research suggests that if these interventions are to have any impact on suppressing indigenous transmission, a coverage of at least 80% must be achieved and maintained for a significant length of time. In addition, the receptivity and vulnerability of many localities within KZN was confirmed, highlighting the need for sustained, optimum coverage of a package of targeted effective vector control interventions.Additional file 1: Table S1. Additional risk factors associated with Plasmodium falciparum malaria from the community-based KAP survey by study municipality in uMkhanyakude district, KwaZulu-Natal.


Thalassarche melanophris, grey-headed T. chrysostoma, and light-mantled albatrosses Phoebetria palpebrata), we quantified the influence of invasive European rabbits Oryctolagus cuniculus and extreme weather patterns on breeding probability and success. Temporal variability in rabbit density explained 33–76% of the variability in breeding probability for all three species, with severe decreases in breeding probability observed after a lag period following highest rabbit numbers. For black-browed albatrosses, the combination of extreme rainfall and high rabbit density explained 33% of total trait variability and dramatically reduced breeding success. We showed that invasive rabbits and extreme weather events reduce reproductive output in albatrosses and that eliminating rabbits had a positive effect on albatross reproduction. This illustrates how active animal management at a local breeding site can result in positive population outcomes even for wide ranging animals like albatrosses where influencing vital rates during their at-sea migrations is more challenging.Invasive species present a major conservation threat globally and nowhere are their affects more pronounced than in island ecosystems. Determining how native island populations respond demographically to invasive species can provide information to mitigate the negative effects of invasive species. Using 20 years of mark-recapture data from three sympatric species of albatrosses (black-browed  Island ecosystems, which generally show high levels of endemism, are particularly vulnerable to the direct and indirect stressors of invasive species, including competition, predation and habitat degradation3. The influence of invasive species on island ecosystems already experiencing pressure from global climate change may result in trophic imbalances amongst native communities, and reduced species resilience in the face of multiple threats4. Further, given the frequency and severity of extreme weather events, such as heat waves and precipitation, are predicted to increase as a result of global climate change5, their effects on island ecosystem structure and function are likely to be exacerbated6. In severe cases, the combination of invasive species and extreme weather events could have particularly serious demographic consequences for threatened native species.Invasive species are a major threat to global biodiversity, with the capacity to cause rapid ecosystem change7. The defining characteristics of the most successful mammalian invaders are high adaptability and rapid reproduction, both of which increase the likelihood of population establishment and the rate of dispersal8. The European rabbit Oryctolagus cuniculus epitomises these life history traits, contributing to its historical dominance as a successful invasive species worldwide9. Acting as an ecosystem engineer, rabbits can reduce landscape heterogeneity through over-grazing that can lead to catastrophic habitat degradation10. Rabbit populations typically grow quickly before decreasing rapidly once local carrying capacity is reached in a boom and bust scenario12. The transitional period between the boom and the bust can swiftly transform a landscape through high vegetation suppression and increased erosion rates and consequently, drastically degrade native species communities14.Amongst invasive species, introduced mammals are particularly problematic16, with vegetation providing fodder for rabbits and nesting habitat for seabirds. There are profound demographic responses of burrowing seabirds to high rabbit density, including reduced reproductive output through increased competition for burrow-nesting habitat18. However, for surface-nesting seabirds such as albatrosses, the consequences of high rabbit density and associated habitat change remain unquantified. As rabbit population densities grow, elevated grazing pressure can lead to a reduction in vegetation cover and plant diversity19, with the potential to deplete available nesting material, increase predation risk, or increase variability in nest microclimate. For surface-nesting seabirds greater exposure to weather conditions may increase the energetic costs of incubation or brooding, and for unattended chicks increase thermoregulatory costs21. With greater exposure, the risk of predation is also likely to increase, as adults, eggs and chicks become more visible22. At high rabbit densities, increased soil erosion rates driven by severe vegetation suppression may also influence nest site selection, as some areas become less suitable13.Invasive rabbits on islands often co-exist with native seabird colonies23, reproductive rates can have greater influence on population growth rate and should be assessed.Typically, in long-lived species such as albatrosses, that inhabit variable environments, fluctuations in breeding probability and success often have limited influence on regulating population growth rates. However, for albatrosses which have low immigration rates and in most cases severely diminished populations due to widescale declines in adult survival driven by interactions with commercial fishing operationsth century; cat Felis catus, weka Gallirallus australis scotti, rabbit, rat Rattus rattus and mouse Mus musculus populations have fluctuated due to variability in resource availability and more recently through management interventions25. With coordinated releases of the rabbit control virus Myxoma, rabbit numbers were reduced from around 300,000 in 1977 to 30,000 or less by the late 1980s, remaining low until the late 1990s12. A subsequent rapid rise in rabbit numbers between 2001 and 2005 has been attributed to multiple factors including the eradication of feral cats, reduced distribution and effectiveness of the Myxoma virus, warmer climatic conditions and increased availability of food resources as a result of twenty years of vegetation recovery following a myxo-induced reduction of the rabbit population27. Before they were eradicated in 2000, feral cats preyed heavily on rabbits and burrowing petrels, using both as a main prey source28. However, there are no reliable indications that cats regularly preyed upon nesting albatrosses, possibly due a surplus of preferred prey and an effective defence mechanism: the regurgitation of stomach oil28. Furthermore, intensive control of cats began in 1996, two years after the initiation of this study, with cage trapping being the most effective method for considerably reducing the population29. While rats have contributed to breeding failure of several burrowing petrel species on Macquarie Island, there is no clear evidence that they depredate albatross eggs or chicks in active nests31. In 2014, one of the largest and most ambitious invasive species eradications to date was deemed successful in removing rabbits, rats and mice from Macquarie Island32. While present on the island, rabbits caused widespread and severe vegetation suppression, enhanced slope erosion and more frequent landslide events  occurred approximately four and a half years prior to the peak in the middle and south of the island (2009)12. Furthermore, rabbit impacts have coincided with changes in climate: increases in surface air temperatures by 0.62 °C (1948–2007), total annual precipitation by 35%, and higher wind speeds35. Rising annual rainfall has the capacity to increase the frequency and extent of landslips, and the rate of erosion on Macquarie Island’s coastal slopes, which has been made worse in recent years by the widespread loss of vegetation from rabbit grazing13.Subantarctic Macquarie Island has experienced drastic change over the last ~140 years due to the establishment and proliferation of invasive species. First introduced by pre-industrial commercial sealers in the late 19Thalassarche melanophris, grey-headed T. chrysostoma and light-mantled albatrosses Phoebetria palpebratra nest on high, exposed escarpments. These steep nest sites have experienced major degradation driven by over-grazing and landslips from resulting soil instability, geological activity and heavy rainfall events  of both black-browed and grey-headed albatrosses decreased over the study period (1995–2014) Fig. . AlthougHigh island-wide rabbit density was associated with decreases in breeding probability of all three species, explaining 75.8%, 39.2% and 33.1% of the variance for black-browed, grey-headed and light-mantled albatrosses, respectively Fig. . Light-mA negative correlation between rabbit density and breeding success was detected for black-browed albatrosses, explaining 22.8% of the variance of modelled breeding success, and showing the lowest estimate (0.17 ± 0.06) 3 years after peak rabbit density Fig. . This coThe relationship between extreme rainfall on breeding success was only obvious for black-browed albatrosses, explaining 28.3% of the variation Fig. . The tot°), and grey-headed albatrosses for high elevations , whereas light-mantled albatrosses showed more variability in elevation    and southerly  Fig. . These s01) Fig. . Colonie295 = 154.15, p < 0.001), grey-headed  and light-mantled albatrosses . For grey-headed and light-mantled albatrosses trap-shyness was detected , i.e., where individuals encountered on the first occasion tend to be encountered less at the second occasion. The biennial breeding frequency and associated lower breeding probability of grey-headed and light-mantled albatrosses may explain the trap-shyness detected for these species. Biennial breeding is accounted for in the mark-recapture modelling by possible transitions into unobservable states. In contrast, trap-happiness was detected for black-browed albatrosses . Trap-happiness, in this case, appears to be an artefact of reduced accessibility for investigators to portions of the study site during periods of high rabbit grazing; consequently, individuals in accessible locations were reencountered more frequently. Black-browed albatross encounter histories showed evidence of underdispersion. The biological explanation for underdispersion is unclear and may simply reflect greater uncertainty in the estimation process due to a smaller sample size. To correct for lack of fit to the underlying capture-mark-recapture assumptions, over dispersion factors (ĉ) of 1.00, 1.00 and 1.46 were included in the models for black-browed, grey-headed and light-mantled albatrosses, respectively36.Poor model fit was found for all three species; black-browed  vegetation degradation directly affecting nest site habitat and microclimate over short temporal scales, and ii) vegetation degradation and rabbit burrowing activity reducing soil stability and amplifying natural erosion rates, leading to large-scale degradation of overall slope integrity reduced habitat suitability at breeding colonies.Thalassarche cauta colony). Regardless, albatrosses on Macquarie Island bred in areas of dense tussock grass (Poa foliosa and P. cookii) during periods of effective rabbit control, until management intervention ceased and heavy rabbit grazing transformed the landscape. Founding nest site selection is likely to be strongly influenced by habitat quality, which for Macquarie Island albatross was drastically and rapidly reduced to dead tussock pedestals, bare ground and mud following increased rabbit numbers. Habitats that provide protection from adverse weather conditions, reduced variability in nest microclimate and cover from predators would, in theory, be selected preferentially37. As adults return to colony, and encounter poor nesting conditions, they may either invest in breeding in substandard conditions or skip breeding, consequently maintaining body condition until nest site conditions improve. For Macquarie Island albatrosses, reduced breeding probability during periods of severe rabbit damage potentially reflects their adaptive capacity, whereby the energetic costs of breeding are avoided when the likelihood of reproductive success is low. As long-lived species, albatrosses have many opportunities to breed and can skip some breeding opportunities with little consequence for individual fitness and lifetime fecundity38. However, continuous skipping and low breeding probability can have delayed population level consequences by reducing breeding population recruitment rates.The greater affect of habitat degradation on breeding probability rather than breeding success is unexpected given that albatrosses are found breeding in exposed colonies with very little vegetation cover  and mega-herb species (Stilbocarpa polaris) to bare ground and secondary succession of Leptinella plumosa has led to an overall reduction in the availability and quality of nesting material  and Northern Royal (Diomedea sanfordi) albatrosses has resulted in a considerable decline in hatching success42. Furthermore, the loss of vegetation cover might also have led to increased predation on chicks from brown skuas (Catharacta antarcticus) and giant petrels (Macronectes sp.)25. Rabbit densities, vegetation suppression and predation risk impact chick survival through a complex interplay of processes. The eradication of rabbits and subsequent drop in grazing pressure has allowed the vegetation to recover, but also potentially increased predation risk, as skuas lose rabbits as a principal prey resource25. The significant correlation between rabbit density and breeding success in black-browed, but not in grey-headed or light-mantled albatrosses, may also be related to the annual breeding strategy of black-browed albatrosses. As annual breeders, black-browed albatrosses face a greater number of years of poor slope condition over the study period. Alternatively, black-browed nests on Macquarie Island predominantly face south-east into the prevailing winds, which may increase the vulnerability of chicks to storm events when vegetation cover is poor.The negative relationship between rabbit density and breeding success of black-browed albatrosses may reflect an increase in the metabolic cost of incubation and brood for adults due to reduced vegetative cover and the increased exposure to wind and rain preventing efficient thermoregulation by chicks12. Following the introduction of myxomatosis in 1978, rabbit numbers plummeted and remained low until the mid-1990s43. Rabbit numbers increased dramatically from 1998 to 200612 and this boom in rabbit numbers coincided with our study period. This increase reflects growth of localised rabbit populations, particularly in the south of the island, which represents black-browed and grey-headed nesting sites, and at the time showed greater vegetation diversity and abundance14. By 2008, heavy grazing had resulted in severe vegetation suppression and slope degradation of the escarpment at both breeding colonies13. The four and a half-year lag between high rabbit density on the island and low breeding probability for the two albatross species reflects the delayed vegetation suppression at these southern colonies. For light-mantled albatrosses, which are widely distributed on the island, the half-year lag in the response of breeding probability is more likely to be a direct effect of the high, island-wide rabbit density.The asynchronous relationship between breeding probability and rabbit density reflects spatio-temporal heterogeneity in grazing pressure. Prior to this study, the rabbit population on the island was estimated to be several hundred thousand individuals in the mid-1970s, before it was reduced to just tens of thousands around 199744. Subantarctic latitudes have recently experienced changes in predominant weather patterns as a result of global climate change47. For Macquarie Island, a prolonged positive phase of the Southern Annular Mode has resulted in increases in the frequency and magnitude of heavy rainfall events and increases in average daily wind speeds33. Extreme rainfall events in January, i.e. during the early stages of albatross chick growth, can result in high mortality, as we have shown by the lower breeding success for black-browed albatrosses. Chicks are more vulnerable at young ages because waterproof feathers have not yet developed and they have less ability to thermoregulate48. Consequently, protection from extreme events by vegetation, which provides a more stable microclimate, is likely to enhance chick survival. The interaction of high rabbit grazing pressure in an already perturbed system appears an important driver of the low breeding success observed between 2008 and 2010. For black-browed albatrosses the effect of extreme weather may have had a greater influence on breeding success than in other species, because of the lower variation in nest aspect (predominantly south-easterly), low elevation and sparse vegetation in the colony. Breeding birds and their chicks were therefore more exposed to extreme storm events driven by circumpolar deep low-pressure systems, and at greater risk of nest-site waterlogging by escarpment runoff.Understanding the effects of invasive species together with that of a changing climate are crucial to mitigating impacts on native species49. Additionally, increasing wind speeds may accelerate drying of soils between heavy rainfall events, leading to further slope erosion and potentially influence land slipping. However, the stochastic nature and spatial distribution of these events confound efforts to link them statistically to demographic trends.The combination of an anomalously high number of extreme rainfall events and high rabbit grazing also presents the possibility of secondary effects on albatross reproductive output, with greater soil instability increasing the frequency of landslips12 and severe habitat degradation13.In this study, the likelihood of incorrectly assigning a breeder as a nonbreeder, or assigning a breeder as not detected is marginally increased during the periods of poor slope condition as the number of accessible nests decreases. To negate the effects of incorrect state assignment detection probability was incorporated in the modelling. Furthermore, the results of the modelling generally show a more significant relationship between rabbit density and albatross breeding probability compared to breeding success. This may in part be explained by the inherent natural variability of each demographic parameter. Breeding probability is generally less variable than breeding success, and in this case only shows a response to rabbit density over the period that corresponds with the greatest rabbit damage. Therefore, the differences in findings may reflect our capacity to detect a relationship for each demographic parameter. The disadvantage of using a correlative approach to estimating drivers of demographic rates is that spurious relationships may be inferred without clear mechanisms of underlying causation. Here we have shown a statistically significant correlation between rabbit densities and reduced breeding probability and success, supported by quantified evidence of high rabbit numbers51. Furthermore, research on the underlying processes that determine breeding probability of seabirds largely focuses on factors such as breeding experience, adult quality and at-sea conditions53, with little work on the effects of nesting habitat change.The effects of non-predatory invasive species on the demography of native species are complex and difficult to quantify. Current research on demographic responses of seabirds to invasive species primarily addresses predation, particularly by cats, rats or miceProlonged low breeding probability and success, as found on Macquarie Island, is likely to have ramifications for juvenile recruitment in the future, leading to a temporary decrease in the breeding population and a skewed population age structure. Due to a delayed onset of maturity and high recruitment age of albatrosses, the greatest population-level impacts on Macquarie Island may not be observed for 8–14 years after the vegetation recovers.54. There are early signs of increases in albatross breeding probability and breeding success, and the continued recovery of slope vegetation should provide greater protection and stability in nest microclimate. However, for black-browed albatrosses, where a large proportion of the population (36.7 ± 1.2%) nest in an area where the dominant tussock species, P. cookii exhibits slower growth compared to the widespread P. foliosa, recovery of vegetation may take longer. Alternatively, cessation of heavy grazing provides an opportunity for the secondary succession of species with more vigorous recruitment, such as P. foliosa or S. polaris.The success of the Macquarie Island Pest Eradication Project in removing rabbits from the island has resulted in rapid vegetation recovery55. Positive changes in vital rates, as we show, can translate into real outcomes at the population level, such that when invasive species are removed, populations can increase given more individuals are recruited into the potential breeding population by increases in chick fledging rates. By quantifying the indirect and complex effects that invasive species have on the demographics of a community of native seabird species and their subsequent population structure, we obtain greater insight into vulnerability or resilience of each species to changes in ecosystem state and ultimately reduce the likelihood of negative and unintended consequences of future applied conservation management.Here we show that breeding site conditions are linked to breeding probability and success. This is important in a conservation context as for wide-ranging species, like albatrosses, apparently indirect onshore management interventions such as removing invasive species, can have a positive effect on vital rates. Affecting change in their at-sea habitats is much more challenging and achieving a conservation outcome correspondingly more difficult and complexWe found that high rabbit density was linked to low breeding probability of subantarctic albatrosses through severe habitat degradation. For one species , the additive impacts of extreme weather events and high rabbit density explained decreases in breeding success. The future for the albatross populations following the rabbit eradication on Macquarie Island looks positive, given that reproductive ability has already improved. Our findings illustrate the importance of breeding site characteristics on vital rates in albatrosses and show that integrating terrestrial threatening processes is important when assessing population viability and the development of management policy for predominantly marine species.56 from three albatross species breeding on Macquarie Island  to quantify the relationship between invasive rabbits and weather patterns, and albatross breeding probability and breeding success.We used 20 years (1995–2014) of capture-mark-recapture data in a multi-event modelling approachMacquarie Island is a narrow, elongated, uplifted island oriented approximately north-south. The coastal perimeter of the island rises steeply from sea level to approximately 300 m and provides habitat for escarpment-nesting albatrosses. Albatross construct their nest using nearby material, such as mud, vegetation and other natural debris shaped into a pillar with a rounded hollow on the top that forms the nesting bowl. Adults continue to maintain the nest throughout incubation by adding more organic material.Between 1995 and 2014 (seasons 1994/95 and 2013/14), all accessible albatross chicks at one black-browed, one grey-headed (encompassing the entire island breeding populations) and seven light-mantled albatross study sites  were banded with stainless steel bands. The majority of breeding adults had existing bands from previous long-term studies. Over the study period, annual nest visits during incubation allowed the bands of breeding adults and the presence of an egg to be recorded. Banded nonbreeding adults observed within the study site were also recorded. Nest checks at the end of the breeding season determined whether the chick fledged successfully as a measure of breeding success. All methods were performed in accordance with the relevant guidelines and regulations. Nest monitoring protocols approved by the Tasmanian Department of Primary Industries, Parks, Water and Environment’s Animal Ethics Committee in accordance with the standard operating procedures for seabird research on Macquarie Island.A geographic survey was undertaken in the 2013/14 season to establish physical differences in elevation, slope angle and aspect of nests of the three species . Elevation was measured using a calibrated Garmin GPSMAP 64 s with a built-in barometric altimeter. A Suunto PM-5/360 PC Clinometer was used to determine the slope angle of the escarpment at the nest site. A bearing measurement taken approximately 90° perpendicular to the slope at the nest site using a handheld compass was used to determine nest aspect. Differences in nest site characteristics  among species were tested using one-way ANOVA and Tukey’s HSD test.36. All chick, juvenile and nonbreeding states before the first breeding attempt were suppressed to focus on reproductive parameters.An encounter history was produced for each individual  which included several observable adult states, successful breeder (coded 1), failed breeder (coded 2), nonbreeder (coded 3); and unobservable states (coded 0) corresponding to post-successful breeder, post-failed breeder and post nonbreeder  was incorporated in an event matrix59. Tests for trap-dependence (Test 2: 2.CT + 2.Cl) were excluded since the model structure accounted for differences in breeding probability based on previous breeding states, characteristic of skip-breeding36. A correction for lack of fit within the populations was applied using an over-dispersion factor (ĉ)60.To test for goodness-of-fit, encounter histories were transformed to single-state and assessed using U-Care 2.3.2 software, including tests for transienceP) were therefore quantified and incorporated in the demographic models , breeding probability (β), breeding success (γ), and detection probability (P) were estimated using E-Surge 1.9.0 software61. See online Supplementary Fig. γ) estimates were derived using a time-dependent (t) model for all adult observable states due to their annual breeding behaviour. Given the difference in likelihood of carry-over effects on the current state from the previous state of biennial breeders, the probability of return and breeding for successful breeders was different to failed and nonbreeders for the original time-dependent models, except when modelling the demographic parameter of interest, where time-dependent parameters were retained. This constituted our general model for grey-headed and light-mantled albatrosses. Furthermore, because long-term temporal trends in the covariate and demographic rate can obscure their relationship, detrended models that provide a measure of short-term variation were also implemented, which described the total variation explained by the covariates when temporal trends were removed.Adult survival (Φ), return rates , whereby the model with the lowest AIC (ΔQAIC > 2) was chosen as the best modeli.e. one value for the entire island for each month from 1994 to 2014) were taken from Terauds, et al.12, who used a smoothed regression model to generate island-wide estimates from rabbit counts in two-hectare study plots. The region-specific rabbit density data represents individual count data shows large inter-annual and intra-annual fluctuations and may obscure any important relationship between albatross breeding. Therefore, modelled island-wide estimates from Terauds, et al.12 were implemented in the demographic modelling because of the extensive effort this study takes to ensure data accuracy and completeness across the time-series that aligns with the albatross demographic dataset used in this study.Environmental covariates were included in the demographic modelling to quantify the influence of rabbit density and extreme weather events on breeding probability and breeding success. Monthly estimates of island-wide rabbit density  values obtained from the Bureau of Meteorology weather station on Macquarie Island were summarised into the number of days above the 80th percentile of the study period average and included as a covariate to capture the frequency of extreme rainfall events. This equated to the number of January days with total rainfall above 4.40 mm.To investigate the influence of extreme events on breeding success during the early chick-rearing period, when chicks are most vulnerable and thermoregulatory costs are high2) and the total variation explained by the covariates when temporal trends have been removed from the variable and the demographic rate (detrended R2) following Grosbois, et al.63. The detrended models provided a short-term measure of variation compared to the standard models, which consider long-term variability and can be sensitive to temporal trends in the environmental covariate and demographic rate.The significance of each covariate on modelled breeding probability and breeding success was assessed using ANODEV, and the magnitude of its effect determined according to both the total variation explained by the covariates (RSupplementary Information.


Mangroves are salt-tolerant woody species occurring in tropical/subtropical coastal habitats. Plantation of fast-growing non-native mangrove species has been used as a tool for mangrove restoration/reforestation in several countries. However, the fast-growth ability can make recently introduced species invasive as they can possibly replace co-occurring native mangroves through expressing higher growth performance and phenotypic plasticity. Therefore, quantifying growth differences between native versus non-native mangrove species is important for forest ecology and management. In this meta-analysis, we compared the growth performance of non-native and native mangrove species pairs by analysing all available results in the literature (33 studies). We found that non-native mangrove species performed better than co-occurring native mangrove species in their introduced regions (Log response ratio = 0.51 ± 0.05) and they also expressed higher trait plasticity. Therefore, these species can be potentially invasive owing to their greater competitive advantage. However, the growth difference was diminished at higher latitudes where native mangrove species seem to perform as well as non-native mangrove species do. This is the first meta-analysis on the growth response of mangroves and it has consequential management implications. We suggest that planting of non-native mangrove species should be avoided and their spread should be monitored. Despite their invaluable ecosystem goods and services, mangroves and the species depending on them are at an elevated extinction risk owing to coastal development, pollution, aquaculture, logging and biological invasion3. Globally, 20% of the total area of mangroves has been lost between 1980 and 20054. Similarly, global climate change  also threaten mangrove ecosystems by creating novel environments for mangroves where new abiotic factors  or biotic interactions may occur . Therefore, the ecological consequences of global climate change on mangrove ecosystems are not straightforward.Mangroves are stress-tolerant species occupying coastal intertidal zones  in the tropics and subtropics worldwide6. Different components of climate change  trigger different responses in native and non-native species and therefore build more uncertainty regarding plant invasion status and appropriate management practices7. For example, increased CO2 can favour non-native species more than native species and facilitate biological invasions9. Therefore, the effect of biological invasion and global climate change interaction remains elusive but has critical importance, especially in mangrove forests.Biological invasion is also a major threat to mangrove ecosystems. Climate change can be a driving factor regarding invasive species performance. Abrupt range shifts in invasive species are expected under global climate change across biomeset al. (2000) an alien plant is an introduced species due to human activities in a certain area, whereas a naturalized plant is an alien plant that can constantly reproduce. Invasive plants are naturalized plants producing an excessive amount of offspring and potentially can spread across different areas10. Invasive species in mangrove forests are a serious problem and the main reason for introduced species is accidental introductions11. However, some mangrove species were intentionally introduced to restore mangrove habitats, and later these species became invasive such as Bruguiera and Rhizophora species in the USA12 and Sonneratia species in China13.Several terms have been used to describe invasive species in the literature . According to the definition by Richardson 16 19.Many non-native woody species have also been introduced into mangrove forests in North America, Australia and Africa20. However, trait-based coexistence theory contradicts the niche theory21. According to the coexistence theory, species abundance can determine its population growth rate and competitive interactions are the main factors shaping communities22. Species with high abundance and distribution  may have high intraspecific trait variation because of high habitat heterogeneity. However, high trait variation can be negatively correlated with species abundance in tropical tree seedlings23. In other words, occupying optimal trait values (low trait variation) can contribute to a higher spread of a particular species.How do introduced species express better performance in growth and competition than native species and become invasive? According to the niche theory, non-native and native species those that have a similar niche cannot coexist but that have similar niche cannot coexistRhizophora mangle in Hawaii)24. Moreover, natural enemies may not co-migrate with introduced non-native species when these plants expand their ranges25. Therefore, an invasive species may invest more in its growth and competitive ability rather than defence due to lack of enemies in the introduced regions26 . Therefore, invasive woody species can express better performance than their congeneric or con-familial relatives27.High establishment of non-native mangrove species in their introduced range can be linked with reduced propagule predation  in woody species is positively correlated with the niche breadth of a species37.Functional traits are biochemical, morphological, physiological, phenological traits which can affect the performance and fitness of an individual38. Therefore, intraspecific trait plasticity can be a key factor to understand species fundamental niche and distribution35 as well as the invasive potential of a species39. For example, several studies indicated that higher values in some traits can be used as indicators of invasive tree species  and evolutionary relationships es Table . As a re40. Trait variation and plasticity comparisons between native versus non-native plants have been mostly studied in invasive herbaceous plants using meta-analyses43. However, woody invasive species were rarely examined  and, particularly no mangrove species were included in these meta-analyses. Although there are meta-analyses on mangrove productivity44, economic value45 and carbon budget of mangroves46, there are no meta-analyses on the growth performance of mangrove species in the existing literature.Meta-analysis is one of the valuable methods to compare the growth performance of non-native and native mangroves52. Quantifying growth differences between native and non-native species pairs under climate change is a knowledge gap in the literature. To address this knowledge gap, we compared the growth response of co-occurring native and non-native mangrove species across different regions by examining several key performance traits in a meta-analysis. We specifically asked: Do introduced non-native mangrove species express better growth performance than co-occurring native mangrove species in their introduced regions? Is there a latitudinal gradient in growth difference between non-native and native mangrove species? Moreover, we examined the current global status of introduced species in mangrove habitats and possible mangrove responses to climate change.There are only a few number of experimental studies simultaneously compared the trait responses of both native and non-native mangrove species although biological invasion in mangroves had gained a lot of attention. Some studies focus on field surveys of non-native mangrove species, whereas others measured the response of either native or non-native mangrove speciesSonneratia caseolaris is native to Hainan, China but introduced to other regions in China53. Therefore, it was regarded as a native species in Hainan but non-native in Shenzhen, China.Quantitative and qualitative data were extracted from the literature using ISI Web of Science and China Knowledge Resource Integrated Database (CNKI) in 2019. Several keywords were used to find the most related papers for our meta-analysis including mangrove*, native*, naturalized, indig*, non-native*, nonnative*, alien*, nonindig*, non-indig*, exotic*, invasive* and combinations of these keywords. Following criteria were selected to be included in the analysis: (i) Comparative empirical studies must have at least two co-occurring mangrove species . (ii) Each study must measure at least one trait . (iii) Each study must be at the species-level (community-level studies were excluded). “Native” or “non-native” groups were determined based on the species information presented in each publication. For example, 43 as each study has a unique response ratio including biological and environmental variation54. The use of co-occurring species pairs, instead of different species from separate studies, provides more realistic comparisons between non-native and native mangrove species because the data will be from the same experimental conditions .In each study, a non-native mangrove species was paired with a co-occurring native mangrove species which was presented in the same study. In the case of several native and non-native species in a single study, several pairs were included in our analysis. These species pairs were regarded as independent data points–RGR, tree height, specific leaf area, net photosynthetic rate) from tables, figures and Supplementary Files/appendices (if there are any) as these traits can be predictors of invasions57 , that is widely used in meta-analysis studies, was preferred as an effect size to quantify the overall differences between growth differences between species pairsThe use of LRR allows growth performance comparison between species pairs to assess which species (non-native or native) performs well in particular environmental conditions . If the overall value of LRR is greater than zero , growth performance in non-native woody species is greater than that in native species and vice versa. If LRR overlaps zero, there is no overall growth difference between non-native and native species.59 to quantify phenotypic plasticity values in growth performance traits (the same traits used for the LRR) of native and non-native mangrove species pairs in China:c is the trait mean under control treatment; Xt is the trait mean under a treatment . PIV is a common index that allows plasticity comparisons across species by illustrating the overall absolute change in trait means61. PIv values are always between 0 and 1 .In a subset of our dataset, we also used a plasticity index (PIv)P-values were calculated at 0.05 level. Regression analyses were used to determine the relationship between the mean log response ratio (LRR) values and latitudes. All statistical analyses were performed in JMP software v.13.2 . Figures were prepared using SigmaPlot v.12 .A two-way ANOVA was used to determine the effect of trait type, experiment type and their interaction on trait responses of mangrove species. In a Tukey’s honestly significant difference (HSD) test, significant differences between trait, experiment type and their interaction were detected. In a paired sample t-test, non-native mangrove species were paired with a native species in each study and Kandelia candel in China is recognized as Kandelia obovata62) (Appendix We extracted data from four non-native mangrove species belonging to two families (33 studies) which met our selection criteria to compare the growth performance of co-occurring native and non-native mangrove species. In our dataset, there were 220 data points including nine traits, seven treatment types and 74 different experimental conditions  across all studies . We found that, on average, recently introduced non-native mangrove species expressed higher trait plasticity than native mangrove species in China  compared to co-occurring native mangrove species  and specific leaf area (SLA) are features of invasive tree species in the Mediterranean climate and these traits can be predictors of biological invasions63. The competitive ability of non-native species can determine their invasion success39. As taller trees can intercept more light, tree height affects the competitive ability. If non-native species grow faster and perform better than co-occurring native species do by expressing higher phenotypic plasticity43, they can outcompete and even replace existing native mangrove species as global environmental changes can favor the spread of invasive plants9. In the case of non-native S. apetala invasion, forest structure can be significantly affected after the invasion and mangrove ecosystem functioning  may be changed65.Invasion of new habitats  depends on how fast populations can adapt to changing environmental conditionsSonneratia apetala and Laguncularia racemosa) in China. L. racemosa was introduced from La Paz, Mexico where is in an arid climatic zone and freshwater availability can increase its growth and productivity66. In Gulf of Mexico, low rainfall and high salinity prevent the movement of mangroves to higher latitudes67. However, at its site of introduction , the mean annual rainfall is almost 9 times higher and salinity is 3 times lower than in La Paz, Mexico70. L. racemosa can outcompete native mangrove species (Rhizophora apiculata) in Hainan and it is reported to be spreading outside afforestation sites16. Moreover, L. racemosa can express high trait plasticity under environmental stresses including salinity and shade71. Therefore, L. racemosa can shift its range towards higher latitudes in China where salinity is lower and become potentially invasive owing to fast-growing characteristics, high germination rate, buoyant propagules, cold resistance, the ability to form dense monospecific stands72. These characteristics make L. racemosa a model species for afforestation but invoke questions regarding its invasive potential as it has frequently been used in mangrove afforestation since 2002 in China.In this meta-analysis, most of the data points were from two recently introduced mangrove species  that can replace native species through competitive exclusion as it can grow better than native species73. In recently reforested areas in China, introduced S. apetala occupies approximately 50% of the replanted mangrove areas13. Moreover, the efficiency of S. apetala in using photosynthetic energy is higher than that in native mangrove species which can make the species invasive in South China74. Two mangrove species from the Indo-Pacific region planted in the US in 1940 have been invading tropical Atlantic forests12 and the same situation might be the case for L. racemosa in China whose origin is Atlantic East Pacific biogeographic zone.75 because such biochemicals produced by a mangrove species can prevent the germination, growth and survival of other mangrove species76 and therefore, determine mangrove forest succession . For example, allelopathic properties of introduced mangrove species such as L. racemosa and S. apetala may reduce the germination rate of seeds belonging to native mangrove species78 and therefore, affect the mangrove ecosystem functioning. As our meta-analysis indicated introduced mangrove species can outperform native mangrove species in their introduced regions, the invasive potential of non-native mangroves should be considered during mangrove afforestation and conservation management.When an invasive species is introduced to a new habitat, existing native plants may be more vulnerable to allelopathic chemicals originating from invasive species79. At high latitudes, naturally occurring native mangroves are expected to perform better than recently introduced species because biochemical adaptations to colder temperatures would be necessary80, and these adaptations can be costly for non-native species in terms of competitive ability81. However, as species at high latitudes experience a reduced number of freezing events as a result of climate change, cold adaptation in native species may be maladaptive.We found that there might be a negative correlation between growth performance differences in mangroves and the latitudinal gradient in China Fig. . In high82. We found that introduced mangrove species in China  express higher plasticity than co-occurring native species do  in afforestation in the past 30 years, the invasiveness of such species has been detected using either surveys or experiments53. The findings of our meta-analysis suggest the invasive potential of non-native mangrove species based on their superior growth performance compared to native species.This study is the first meta-analysis in the literature on the growth responses of mangrove species and it has far-reaching implications for mangrove management . Interestingly, only a very limited number of studies carried out experiments using multispecies including non-native and native mangrove species pairs although several woody species were introduced to different mangrove ecosystems across the world Table . Most st86. Most data in the present meta-analysis were from experimental comparisons on the growth and stress-tolerance of native and non-native species pairs, reflecting the early establishment of plants. According to our effect size (LRR), these non-native species performed better during the early establishment stages. Once they survive and dominate new habitats, they can be potentially invasive.The relationship between growth performance at early life stages and invasiveness can be evaluated by the probability of dominance and dispersal which are two important aspects in predicting non-native species invasion in forests87. Shading, inundation or harvesting is the major measurements to control the invasiveness of a species89. Understanding how the introduced invasive and native species respond to changing environments will be a necessity for humankind to preserve robust ecosystem functioning. Therefore, in the future, more empirical studies are needed to better examine mangrove responses to biological invasions, particularly under global climate change.The early life stage is also an appropriate period to reduce the biological invasions by manipulating the degree of disturbance or productivity in forest management90. Our meta-analysis also examined stress tolerance to several environmental factors, such as inundation, salinity, light and temperature  under changing environmental conditions, they might go extinct or move to different habitats to alleviate the detrimental effects of global climate change.The findings of this meta-analysis can be useful to predict the distribution of mangrove species and possible range shifts in mangrove species under climate change. On the global scale, climate change is a major challenge for woody plant invasion management which might accelerate the dispersal ability and growth performance of non-native speciesperature ppendix AOur study also draws attention to the use of non-native mangrove species in afforestation practices and mangrove conservation management across the globe because introduced non-native species can outperform native mangrove species in their introduced ranges. We suggest that the invasion potential of non-native species should be considered and the use of introduced non-native mangrove species should be avoided in mangrove afforestation practices. Although ecological repercussions of non-native mangrove species invasion in mangrove forests are uncertain, such introduced non-native species should be monitored for their potential spread. Restoration and management of mangroves are crucial for socio-economic and scientific aspects which can be achieved through a better understanding of how mangrove species respond under global climate change and biological invasion. More empirical studies in the future can enhance our understanding of mechanisms behind invasions and possible methods to control the invasion in mangrove forests.Appendix AAppendix BAppendix CAppendix DAppendix EAppendix F


Using polymerase chain reaction-based suppression subtractive hybridization, we recently identified 27 different formaldehyde-inducible genes including platelet-derived growth factor receptor alpha gene (PDGFRA) and mouse double minute 2 (MDM2) gene which were increased significantly in both formaldehyde-exposed human trachea cells, 680. Tr, and rat tracheas. To establish a possible relationship between induction of these formaldehyde-inducible genes and symptoms of SBS, we examined expression levels of these genes in peripheral lymphocytes of residents of new apartments. Here, we report that the expression of PDGFRA and MDM2 transcripts was significantly higher in peripheral blood lymphocytes obtained from 15 residents in new buildings than in seven control individuals. Our results suggest that the elevated levels of PDGFRA and MDM2 may be associated with the formaldehyde-induced pathophysiology that is closely related with SBS, and that they deserve evaluation as potential biomarkers for formaldehyde intoxication.Formaldehyde has been identified as the most prevalent cause of sick building syndrome (SBS), which has become a major social problem, especially in developing urban areas. However, studies on the molecular mechanisms associated with formaldehyde toxicity have been limited, probably because it is difficult to relate the experimental results obtained from