Protein design plays an important role in recent medical advances from antibody therapy to vaccine design. Typically, exhaustive mutational screens or directed evolution experiments are used for the identification of the best design or for improvements to the wild‐type variant. Even with a high‐throughput screening on pooled libraries and Next‐Generation Sequencing to boost the scale of read‐outs, surveying all the variants with combinatorial mutations for their empirical fitness scores is still of magnitudes beyond the capacity of existing experimental settings. To tackle this challenge, in‐silico approaches using machine learning to predict the fitness of novel variants based on a subset of empirical measurements are now employed. These machine learning models turn out to be useful in many cases, with the premise that the experimentally determined fitness scores and the amino‐acid descriptors of the models are informative. The machine learning models can guide the search for the highest fitness variants, resolve complex epistatic relationships, and highlight bio‐physical rules for protein folding. Using machine learning‐guided approaches, researchers can build more focused libraries, thus relieving themselves from labor‐intensive screens and fast‐tracking the optimization process. Here, we describe the current advances in massive‐scale variant screens, and how machine learning and mutagenesis strategies can be integrated to accelerate protein engineering. More specifically, we examine strategies to make screens more economical, informative, and effective in discovery of useful variants. Integrating protein design and variant screening with machine learning facilitates the discovery of useful variants for applications including antibody therapy and vaccine generation. This perspective describes how the prioritization of variant testing maximizes the power of machine learning for protein engineering, strategies for designing a smart screening library, and considerations for integrating massively parallel assays with machine learning. Variations raise questions in evolution (studying how mutations arise and sustain), pathology (characterizing variant effects in diseases), and functionality . While studying naturally occurring variants provides valuable knowledge in diseases and biodiversity, rational design and directed evolution are employed to optimize or re‐purpose proteins for human use. Biocatalysts and protein‐based drugs are of great interest in industries; there is an ongoing pursuit for the improved efficiency, versatility, and specificity of these proteins.4–1012 variants in a pooled manner. The fundamental components of these screening systems are 1) library construction, 2) functional screen, and 3) selection of the “better” variants  to facilitate protein engineering by reducing the workload of variant profiling in cell lines, thus providing more realistic screening results compared to in‐vitro settings. Additional to optimization, ML‐assisted screens achieve great successes in protein diversifications, rivaling the data‐driven or Rosetta‐based de novo protein design methodologies. ML‐based generations of new antibody sequences can produce diverse binders to specific antigens; such generative framework can also create a new generation of antibody libraries with enhanced expression and developability. The novel sequences can be used for the next round of in‐silico screening or as training data to improve the model performance.Incorporation of computational methods into these screening platforms makes in‐silico screening possible, thus further accelerates the mining of useful variants.7 ReceML enables probing the extremely large space of variants with a smaller but informative subset of variants examined empirically. One of the main challenges in harnessing the power of ML is to generate a “useful dataset” that maximizes predictive accuracy. A strategy to accurately extrapolate the very few experimentally validated variants to infer fitness of the complete landscapes involves both a smart sampling strategy to capture some “positives” among these experimentally validated variants and an accurate ML model.Below, we provide our perspectives on machine learning‐guided massive parallel assays; we summarize the prioritization of variant testing under the workflow of ML‐assisted protein engineering and discuss how “smart” library design and massive parallel assays complement the ML model with useful training datasets.2 highlighting the importance of both amino‐acid representations and ML algorithms in generating high‐accuracy models.Numerous reviews and articles about ML on protein engineering already document its application and workflow,26 hig structural descriptors, and descriptors that capture physio‐biochemical properties of amino acids. Instead of complex metrics derived from protein structure prediction software, more intuitively explanatory descriptors such as the number of contacts to other amino acid residues are excellent predictors. Evolutionary descriptors including conservation score and PSSM weight are proven to be also extremely useful in predicting mutational effect and site‐wise tolerance to mutations. The use of multiple sequence alignments (MSA) to generate useful embeddings from transformer language models is under active research. Apart from producing accurate 3D structure models, language models built from MSA can perform zero‐shot fitness inference on proteins with abundant homologous sequence information. Among the many ML algorithms, neural networks, tree‐based models, and Gaussian processes have been commonly adopted for variant fitness inference and show high accuracy in selecting functional variants. Applications of generative models to extract useful features from amino acid sequences or 3D structural attributes and generate optimized protein sequences are gaining popularity, because of the models’ capability in generating synthetic sequences that can serve as future training data as well as novel designs.Amino acid representations contain additional features augmenting the sequence‐based embedding for ML. There are sequence‐based pretrained representations derived from NLP models,32 str3 substantial epistasis interactions in play, and the nature of the synthetic library .Evaluating the combined effects of beneficial mutations is the key to protein optimization. However, synthetic combinatorial libraries usually generated a large portion of non‐functional variants due to the shape of the fitness landscape,52 sub For example, only 2.4% of high fitness variants are present in the GB1 fully saturated mutagenesis library that is used as a benchmark of many ML packages of modeling fitness effects. Another test case is redirecting a zinc deaminase to catalyze organophosphate hydrolysis; mutants possessing the quadruple mutations  necessary for endowing such new reaction are extremely rare in a saturation mutagenesis library spanning 12 amino‐acid sites.Scarcity of functional variants in the library creates a class representation bias that makes ML training difficult.56 For Such educated guesses were carried out via selecting variants with high stability based on Rosetta energy scores. Alternatively, one can sample representative variants that maximize sequence diversity. Indeed, that is the concept behind the design of the experiment (DOE) method that systematically generates a small set of gene variant “infologs” to minimize co‐variations between amino acid substitutions and ensure uniform sampling  sequence‐function model to identify top‐performing variants. In each round, 5–12 variants designed based on the upper confidence bound criterion were experimentally validated and added to the model as training data. Finally, after 10 rounds of ML and experimental validation of a total of 96 variants, four top‐performing chimera fatty acyl reductases were identified. This study again demonstrates that iterating between ML and experimental validation is an extremely effective approach in protein optimization.Although active learning only outperforms randomized input for ML when the training data size exceeds 3,000, this sample size still only accounted for 17% of the entire dataset for chemical reactions.61 UsiWe next explore how these sampling strategies that facilitate ML predictions on protein functions can be implemented experimentally via smart library design and massive parallel assay.4Figure Increasing the number of replicates and NGS read depth is necessary to compensate for the massive library size. In addition, the experimental efforts in generating the variants should be factored in. Together, library construction becomes the first key element in creating accurate ML models to describe the variant's fitness.Careful design of the library is crucial for ML or statistical inference on the variant effect Figure2A. When4.1 and continuous evolution methods such as MAGE and PACE further increase the number of amino acid‐changes to 10–20 mutations in a variant by boosting the host genome mutation rate, accelerating the search for high‐performance variants.Random mutagenesis employed in deep mutational scans (DMS) is widely used in directed evolution and massive‐parallel assays. Hypermutation64 and Nonetheless, DMS does not generate informative variants for protein repurposing, especially when the new sought‐after function does not align with the natural function of the protein since the current active site no longer fits the new substrate. Instead, focused mutagenesis and rational design are better suited to redesigning the active site within the current protein scaffold once the protein structure is available or active site residues are identified.Mutation effect estimations from DMS experiments reveal structurally and functionally important sites.71 Non4.2N as the number of sites subjected to saturation mutagenesis increases. To limit the library size and reduce the screening effort, one may resort to structure‐guided design, restricting the number of substitutions screened per site and building a rationally designed library with fewer combinations.To identify the best combinations of substitutions at a few key sites that dominate binding or enzymatic activities, saturation mutagenesis is an effective strategy to employ, generating all amino acid substitutions at a handful of amino acid sites via degenerate codons. However, the library size exponentially increases at a rate of 20 Another important feature of targeted library design is the linking of genotypes to a molecular barcodes to accommodate short‐read Illumina sequencing. For instance, DropSynth 2.0  allows the construction and barcoding of 1500 specific variants from hybridizing microarray‐derived oligos in droplets. Furthermore, Choi et al. demonstrated that CombiSEAL can survey multiple mutants possessing mutations hundreds of base pairs apart using a smart design of library cloning and barcoding scheme. Combinatorial mutagenesis generates and barcodes the full combination space of selected substitutions with certain extent of redundancy, since the information of lower‐order mutations (single and double mutants) is enclosed in higher‐order mutations (multi‐mutants). How to incorporate the DOE method in large‐scale combinatorial libraries to generate specific infologs remains to be addressed.Combinatorial libraries targeting multiple sites with a few shortlisted amino acid residues per site can be constructed through cloning with designer DNA oligos or trinucleotides Figure . With ge73 Ano Meanwhile, diverse antibody complementarity determining region 3 (CDRH3) was cloned via CRISPR mediated homology‐directed mutagenesis, achieving a library size of >105 in mammalian hybridoma cells. Introducing precise mutations across multiple sites is made possible by a combinatorial CRISPR screen that utilizes multiple‐sgRNAs cassettes. CRISPR‐based methods, albeit suffering from some off‐target effects, still possess higher precision compared to MAGE (350 mutations after 20–35 evolution cycles) and synthetic oligo assembly such as DropSynth (≈20% success assembly rate). Fine‐tuning the CRISPR editing consequences will be extremely beneficial for generating precise variants in future projects.Departing from the combinatorial construction methods, the CRISPR‐based method is the emerging theme as it generates variants with precisely defined mutations Figure . CRISPR‐79 MeaAs discussed in the previous section, a smart design library boosts ML predictive power by focusing surveying efforts on information‐rich variants. Oligo synthesis and CRISPR are useful tools in building massive, pooled libraries of variants with a set of focused substitutions. With the increase in the sequence length and scale of oligo synthesis and continuous improvement in CRISPR technology, we can foresee that more precise library construction is achievable in the future to generate computationally designed or selected variants en masse. Now, we turn to the screening system that is essential to generate functional fitness scores for ML.5 While massive parallel assays are fast and cost‐efficient in evaluating multi‐mutation effects, mitigation of experimental noise remains an important task to make phenotypic scoring accurate and reproducible. Gelman et al. proposed several useful recommendations on how to create reproducible multiplexed functional assays (MAVEs) useful for studying clinical variants; here we discuss some of them that are also applicable in ML‐assisted in‐silico screen.Screening systems perform the important role to tie the genotype to its phenotypic performance in a given environment. The most common approach is selection on cell survival or fluorescence‐activated cell sorting (FACS)‐based screens are followed by deep sequencing that identifies the “winning” variants by their enrichment in the filtered bin or selection endpoint. Thus far, massively parallel assays are almost built‐in with ML predictions on phenotypic values.88 Whi5.18 mutants, building a reporter system that can convert protein functionality to growth can be challenging. Besides, ML prediction quality improves when positive selections with lower stress were conducted, as a broader range of functional scores were collected and more variants were surveyed. Together, a reporter system independent of cell survival appears to be more beneficial for ML‐guided screens.First, the assay should distinguish meaningful differences. This is ideal to separate functional variants from their non‐functional counterparts. Hereinto, selection on cell survival would be an excellent reporter. Similarly, competitive growth assays are highly effective in identifying variants conferring growth advantages over a short time frame. Although survival/growth‐based selection can handle a massive library of up to 10 mutants,91 bui Although most of the current screens rely on FACS to isolate “winning” variants, future screens may shift to high‐throughput imaging combined with computer vision, especially in screens for protein‐based drugs that both drug potency and toxicity can be captured in real‐time through observing the morphology of treated‐cells under high‐resolution microscopes.Imaging‐based screen is a useful solution to assess functional phenotypes delineated from survivals, where the phenotypic readout in fluorescence level enables sorting, morphological and genotype profiling.92 Alt5.2Second, the assay design should provide the relevant environment for functionality assessment. The choice of screening/selection methods combined with the host determines the number of variants that can be assessed in one experiment as well as the usefulness of the screen. Balancing the trade‐off between the screening capacity and the selection environment should be factored into the choice of reporter assay.13 variants may be diminished for proteins that undergo extensive post‐translational modifications; yeast display (screening up to 109 variants) could be used as an alternative. Further, yeast complementation assays on human proteins are particularly useful to study variants of clinical importance.For example, the merits of using mRNA display to screen up to 10ernative.94 Fur However, CRISPR‐based modifications are greatly constrained by the multiplicity of infection (MOI) and FACS throughput; such setup ceilings at about 105 variants. Recent integration of single‐cell sequencing that can evaluate many different phenotypes greatly increases both throughput and resolution.As a better disease model, pooled multiplexed CRISPR screens are performed in human cell lines with specific disease characteristics to identify useful drug targets.97 How5.3 from multiple labs shares high correlations in functional scores (R2 = 0.4038), exhibiting high reliability. Experiments under similar conditions clustering together in a PCA plot is another reassuring sign for results reliability.The last but also one of the most important points is about quality control of the screen to ensure reproducibility. Using HSP90 as an example, DMS data deposited in MaveDB101 fr PACT, and MAVE‐NN. Alternatively, tagging multiple barcodes to a variant in a single experiment can facilitate reliable assessment. Although this method is adopted in MAVEs of enhancers, it is not common in protein variant assessments. An alternative approach is to introduce multiple synonymous variants for each protein phenotype.One obvious measure to boost reproducibility is producing multiple biological replicates for each screen that share a high correlation in the functional scores and having the scores denoised with Enrich2,103 PA and software like MAUDE is dedicated for correcting such errors. To reduce technical noise, controlling per cell plasmid numbers using the Equalizers plasmid can make expression‐based assays more robust.It is also important for an expression‐based enrichment assay to start with a balanced library with the same starting frequency for each mutant. Unevenly pooled library introduces high variance in enrichment levels after selection,62 andIn sum, the throughput of the screen, experimental setup, and accuracy of the reporter gene are key components for generating good input functional scores for ML.6An ML‐guided approach is necessary to transverse the sequence‐function space effectively in the search of useful protein variants. Incorporation of ML greatly reduces the experimental burden in an exhaustive screen; accurate approximation of all the variants' fitness is achieved using merely 15% or fewer empirical measurements. Nonetheless, the potential of ML is only fully realized when provided with informative variant fitness scores and descriptive features. Generating a set of useful training data for ML protein design requires strategic library design to capture “informative” variants possessing diverse mutation combinations followed by a massively parallel assay that measures the functional score reliably.However, library design and screening platforms are overlooked or only investigated retrospectively thus far in ML‐focused studies. An important next step is to factor in the ML component in both library construction and high‐throughput screen in future projects, so that we can maximize the utility of ML in the search of useful variants. Most importantly, the paradigm of using random mutagenesis for library construction can be replaced with methods that introduce substitution concisely such as combinatorial mutagenesis and CRISPR‐based methods to accommodate the downstream sequencing and ML analyses. Leveraging the power of ML to effectively summarize mutational effects, more experimental resources can be freed up for researchers to carry out more ambitious screens to explore more amino‐acid sites and include a higher number of substitutions per site. Such expansion in both the breadth and depth of the sampling space will accelerate the discovery of useful proteins and provide insights into the structure and functional mechanisms to guide further designs.A.S.L.W. would like to declare that patent applications have been filed based on their published work on the presented combinatorial genetics platforms CombiSEAL and CombiGEM‐CRISPR.


The Social Responsiveness Scale (SRS) is a 65‐item measure yielding a continuous score capturing autism‐related traits. Scores based on SRS item subsets have been analytically examined but administration of shortened versions has not been evaluated prospectively.The goal of this study was to compare psychometric properties of two shortened versions of the SRS to the full 65‐item SRS, in young children from both a clinical and general population setting.n = 154) and Kaiser Permanente Northern California (n = 201) and block randomized to receive either the 16‐item short SRS, a newly developed computer adaptive testing‐SRS, or the published full‐length SRS. Total scores across the three SRS administration methods were scaled to facilitate comparisons. Scores were plotted to assess distributional properties, while Receiver Operating Characteristic analysis was used to estimate Area Under the Curve (AUC) and address predictive ability.Study participants (aged 3–5 years) were drawn from the AJ Drexel Autism Institute clinic  and comparable across the administration methods, though there was evidence of difference in predictive ability across measures for females (AUC for full SRS = 0.99 vs. 0.84 for short). Within individual comparisons of short versus full scores  suggested underestimation of actual full SRS scores with the CAT‐SRS.Our findings broadly support the construct validity and performance of shortened SRS versions examined here, though the full measure may be needed to more accurately assess traits consistent with ASD diagnosis in females. This work suggests opportunities for collection of ASD‐related phenotype in settings where participant burden or feasibility considerations may have otherwise prohibited such measurement. Quantitative measurement of social communication traits is important for characterizing functioning across the population.Prior work proposed a shortened version of the Social Responsiveness Scale (SRS). Properties of this shortened version have not been examined in a field validation study, nor has prior work considered computer adaptive testing (CAT) administrations.We conducted a validation study across a clinical and general population site, including approximately 200 participants each, randomized to one of the 3 versions .Predictive ability and distributional properties were highly comparable across the 3 versions, supporting comparability of shortened and full SRS scores.Findings suggest opportunities to increase efficiency and broaden collection on quantitative social communication traits.Autism spectrum disorder (ASD) is a neurodevelopmental condition that spans a range of functioning. Evidence suggests that core features of ASD, including social communication traits, extend into the general population and can be measured along a continuum  analyses using existing 65‐item SRS data from several autism databases   version of the SRS .Recruitment took place at a clinical site at Drexel University in Philadelphia, Pennsylvania, and a general population site consisting of two San Francisco Bay‐Area clinics of Kaiser Permanente Northern California. Participants at the clinical site were 3–5 years of age and referred to the AJ Drexel Autism Institute for clinical evaluation from a community partner organization due to suggestion of developmental delays and suspicion of ASD. Families who indicated willingness to participate in research were offered enrollment by clinicians performing the evaluation. Participants at the general population site were recruited from the San Jose and Oakland Kaiser Permanente clinics as part of an ongoing research project participating in the Environmental Influences on Child Health Outcomes (ECHO) Program  of Drexel University.Following approval from the publisher to modify administrations, short SRS questionnaires were created including only the 16 items  and are also summarized in Appendix EAP scores were estimated with a diffuse prior and from the administered items on the CAT‐SRS. Then, each EAP score was converted to an expected test score  .Additional information collected included basic demographic characteristics and diagnostic status. Participants at the clinical site were asked to complete a brief demographic questionnaire assessing primary caregiver/respondent information on race/ethnicity and education. Information on these characteristics was obtained from medical records at the Kaiser general population site. Autism spectrum disorder diagnosis at both sites was determined based on clinical evaluations by trained professionals, information collected during evaluations including the Autism Diagnostic Observation Schedule (Second Edition) , primary analyses used equipercentile equating to scale short scores  were also conducted for the clinical site only . In addition, plots were created for within‐individual comparisons of short and CAT versus full scores from the relevant groups at the Kaiser site. Bland‐Altman plots were also used to visualize agreement between the full SRS and shortened (16‐item and CAT) administrations within this group.To assess predictive ability to address our second primary aim, SRS scores from each version were compared to ASD diagnoses using Receiver Operating Characteristic (ROC) analyses for the entire sample. ROC analyses were also conducted stratified by sex. Given the small number of ASD cases from the general population site, and small number of non‐cases at the clinical site, these analyses were not conducted stratified by site. Chi‐squared tests were used to statistically compare areas under the ROC curves across groups. As a secondary comparison and exploratory analysis to assess specificity to ASD diagnosis, we also conducted ROC analyses assessing SRS scores ability to predict other, non‐ASD diagnosis as indicated in medical and clinical assessments . Children from the clinical site were more likely than those from the general population site (as expected) to have an ASD diagnosis (84% vs. 2%), and to be male (72% vs. 34%) Table  and S1B.The most commonly administered items within the CAT‐SRS by site are shown in Table Mean scores were substantially higher in the clinical group overall (the majority of whom received an ASD diagnosis) as compared to the general population group for all three versions of the SRS Table . DiffereOverall, SRS score distributions from the three versions were highly overlapping at each site Figure . Short sComparing short and CAT scores to corresponding full scores obtained from the same individuals at the general population site, agreement was generally high, though both short and CAT scores tended to be slightly lower than full scores (by a mean difference of approximately two points for short and 9 points for the CAT) Table . Bland‐AROC analyses for the total study population suggested nearly identical performance of full, short, and CAT SRS scores in predicting ASD diagnoses  , in contrast to AUC values falling in the excellent to outstanding range for ASD prediction. This comparison adds to prior work supporting that scores capture traits that are consistent with ASD diagnosis. These analyses also suggested better prediction of other non‐ASD diagnoses with the shortened versions than the full SRS , implying that shortened versions may compromise the separation from other diagnoses. Although our work was not optimally designed to address information on co‐occurring conditions given several limitations, including the age range of our sample, this suggestion is consistent with early SRS development  for females only. Though we had relatively small numbers in our study to address sex differences, and cannot rule out potential chance variation in our findings, these potential sex differences are worth consideration. While sex differences of ∼4:1 male:female ratio are consistently reported, there is also evidence that at least some proportion of diagnostic differences by sex could be due to under‐recognition and misdiagnosis in females  in later ages. Given variability across racial and ethnic groups  even with the approximate 12% reduction in overall target sample size. We also cannot rule out potential impacts of the COVID‐19 pandemic, which may have caused CAT‐SRS scores to differ from the other administrations due to our block randomization scheme. However, scores collected pre‐COVID and during COVID were similar. Furthermore, findings from within individual comparisons at the general population site were consistent with those seen across‐groups, suggesting any potential impacts of COVID on our study did drive differences observed across administration modes. Choice of scaling for shortened scores may impact comparative performance, though we tested two scaling methods that yielded comparable findings. In addition, prior work comparing scaling methods, though noting greater sensitivity of equipercentile equating for sparse and variable data, has suggested similarity in calibration methods . The availability of shortened SRS versions presents opportunities for collection of ASD‐related phenotype data in settings where participant burden or feasibility considerations may have otherwise prohibited such measurement. Wider‐scale collection of data on these traits, in turn, can complement ASD diagnostic data to ultimately enrich our understanding of the phenotype.Study conceptualization and funding ; primary data collection and coordination , primary manuscript drafting , statistical analyses , manuscript editing , study guidance , final manuscript approval .John N. Constantino receives royalties from Western Psychological Services for the commercial distribution of the Social Responsiveness Scale‐2. The remaining authors have declared that they have no competing or potential conflicts of interest.All participants consented to participation and utilization of information for research purposes. This study was reviewed and approved by the Institutional Review Board (IRB) of Drexel University.Supporting Information S1Click here for additional data file.


Strongylocentrotus purpuratus), which occurred in 2014 in southern Monterey Bay, California, USA, was primarily driven by a behavioural shift, not by a demographic response . We then tracked the foraging behaviour of sea urchins for 3 years following the 2014 outbreak and found that behaviour is strongly associated with patch state (forest or barren) transition dynamics. Finally, in 2019, we observed a remarkable recovery of kelp forests at a deep rocky reef. We show that this recovery was associated with sea urchin movement from the deep reef to shallow water. These results demonstrate how changes in grazer behaviour can facilitate patch dynamics and dramatically restructure communities and ecosystems.Understanding the role of animal behaviour in linking individuals to ecosystems is central to advancing knowledge surrounding community structure, stability and transition dynamics. Using 22 years of long‐term subtidal monitoring, we show that an abrupt outbreak of purple sea urchins  in southern Monterey Bay, California, USA, was primarily driven by a behavioral shift, not by a demographic response . We then tracked the behavior (passive or active grazing) of sea urchins for three years following the 2014 outbreak and found that behavior is strongly associated with patch state (forest or barren) transition dynamics. Image credit: Patrick Webster. The importance of behaviour in linking individuals to ecosystems is widely recognised in the ecological literature  algae are deposited in crevices, urchins mainly employ a cryptic passive‐grazing modality , followed by an episodic marine heatwave event that occurred from mid‐2014 into 2016  is the dominant structure‐forming foundation species, over a 95% reduction in historical kelp biomass was documented  near Bahía Asunción, Mexico  or by a demographic response . We then tracked the behaviour of sea urchins in the years following the 2014 outbreak to determine how grazer behaviour shapes alternations between kelp‐dominated  and urchin‐dominated  states. This study was motivated by the following hypotheses: (1) sea urchins emerged from refuge following the regional collapse of Pycnopodia, the 2014–2016 marine heatwave, and a decline in kelp production, (2) sea urchin behaviour (passive or active) explains patch state (forested or barren) transition dynamics and (3) sea urchin migration in search of alternative food sources is associated with macroalgae recovery.In this study, we explore whether an outbreak of purple sea urchins that occurred in 2014 along the Monterey Peninsula, CA, USA was driven by a behavioural shift , is the principal benthic herbivore, although bull kelp (Nereocystis luetkeana) and red sea urchins (Mesocentrotus franciscanus) also inhabit the region. In 2013, a coastwide sea star wasting syndrome occurred that extirpated the urchin predator Pycnopodia helianthoides throughout California by 2014  of sea urchins surveyed in sequential years.To determine the temporal point when the sea urchin outbreak occurred, we conducted change‐point analyses on a 22‐year time series of published subtidal data collected by the Partnership for Interdisciplinary Studies of Coastal Oceans  of sea urchins recorded on PISCO surveys from 2011 to 2016 (sea urchin sizing began in 2011). Support for the behavioural shift hypothesis would be evidenced by increases in counts of all size classes of urchins as they emerged from crevices and became more visible on surveys. Alternatively, support for the recruitment hypothesis (i.e. that the urchin outbreak was associated with a recruitment event) would be evidenced by a dramatic and disproportionate increase in the frequency of sea urchins less than 3 cm. We used a Kolmogorov–Smirnov test to evaluate equality in urchin size distribution across the critical 2013–2014 breakpoint (identified from the segmented regression). Finally, we tested for a relationship between urchin counts and terrain ruggedness (i.e. reef complexity), reef slope and relief among which we found no effect (Supplementary Information Methods).To further explore whether the marked increase in counts of adult (>3 cm) sea urchins after 2013 is explicable by a sea urchin behavioural shift, we used a Bayesian hierarchical framework to fit a size‐structured population dynamics model to survey data. We used this model to evaluate sea urchin recruitment, survival (growth) and detection probability as plausible drivers of observed population dynamics. The structure of the process model is described in detail in Supplementary Information Methods. Briefly, the key demographic processes were the annual rates of growth, survival and recruitment, while a fourth observer process (detection probability) related the latent dynamics of the true population to the observed survey data. We assume that detection probability is reflective of sea urchin behaviour (i.e. whether sea urchins are cryptic and not counted on surveys or actively grazing and visible to divers). The model tracked the dynamics of 10 size classes (corresponding to 1 cm size increments), with growth transition probabilities between size classes parameterised from literature‐reported values Ebert, . Model‐eWe used standard Markov Chain Monte Carlo methods (MCMC) to fit the process model to survey data on average size‐structured urchin counts per transect (details of fitting methods and diagnostics are described in detail in Supplementary Methods). We summarised the estimated proportional changes in recruitment, survival and detection probability after 2013, and we used a simulation‐based sensitivity analysis to calculate the relative contribution of variation in each of these processes to the observed increase in urchin counts over the study period.n = 90), 2018 (n = 111) and 2019 (n = 72) to determine patch transition dynamics and attributes of each patch state such as sea urchin behaviour, density and the cover of key groups of algae , and each transect radiated from a fixed central location  using additional field surveys to further examine how sea urchin behaviour underpins switching among patch states . Survey sites were initially randomly selected and sampled annually from May to September in 2017  based on initial impressions of the site at the start of each dive. In the lab, photoquadrats were analysed to determine the number of actively foraging (i.e. exposed) sea urchins and to estimate the cover of key algal groups that are characteristic of forests and barrens. Each photoquadrat was assigned 16 universal points using a digital grid in ImageJ. Because many algae are difficult to visually quantify to the species level in imagery, we used four taxonomic categories that are known indicators of patch state  in subsequent analyses.The state  of each site was characterised by constructing a linear discriminant analysis (LDA) using urchin behaviour , density and the percent cover of algae as classifiers. In the field, each site was surveyed using 16 randomly placed 1 mTo test the hypothesis that shifts in patch state are associated with alternations in sea urchin foraging behaviour, we explored transition dynamics across two time steps (2017–2018 and 2018–2019). For this analysis, only sites surveyed at the same spatial location in sequential years were used to determine whether each site (1) persisted in the same state across the time step, (2) forward‐shifted from a forest to barren or (3) reverse‐shifted from a barren to a forest. Logistic regression was used to determine the transition probability based on the natural log‐transformed mean density of exposed (i.e. actively foraging) sea urchins, the mean number of cryptic urchins and starting state . We defined the logistic target level based on a positive state shift, where a transition to a different state in the following year was classified as ‘1’ and state persistence as ‘0’. Therefore, each of the variables in the model represents starting‐year values used to predict the following‐year state. We used AIC model selection to identify the best‐fit relationship between patch transition and the density of sea urchins exposed or concealed. Finally, to determine the strength of discontinuity in state‐shift thresholds, we examined the logged odds of state transition probabilities as a function of exposed sea urchin density.n = 18) near the recovery area to one of three depth zones: shallow (0–6 m), mid (7–13 m) and deep (14–20 m). Sea urchins were categorically assigned to one of three size classes based on test diameter: small (<30 mm), medium (30–38 mm) and large (>38 mm). These size classes were selected based on the first, second and third quantiles of the entire population size distribution across all three survey years .In 2019, we observed a dramatic reduction in counts of sea urchins and an extraordinary recolonisation of a kelp forest to an area that was an expansive sea urchin barren just 2 years prior (2017). Surveys farther inshore  during the recovery year (2019) revealed an abundance of large (>6 cm) sea urchins and a reef devoid of macroalgae. We hypothesised that the observed recovery of kelp to the deep reef area was associated with sea urchin movement to shallow water because the shallow reef was previously dominated by red foliose algae, an alternative sea urchin food source. To test this hypothesis, we examined urchin size structure and density across three depth zones and three survey years. We categorically assigned all the survey sites (We used a mixed model with a Restricted Maximum Likelihood (REML) to test for differences in mean sea urchin density across three survey years (2017–2019), three depth zones  and three size classes of sea urchins . The model was constructed as a full factorial with year, depth zone and size class as fixed effects and site and transect as random effects. We then used a contrast test to examine the hypothesis that the density of large‐ and medium‐sized urchins declined over the period 2018–2019 in the deep zone and simultaneously increased in the shallow zone. The output of the mixed model revealed that the density of sea urchins did not significantly change across the 2017–2018 time period. Therefore, we restricted the subsequent contrast test to the 2018–2019 period.Finally, we explored changes in the algal assemblage across each depth zone in relation to sea urchin movement. To test for changes in the mean percent cover of foliose red algae, brown algae and encrusting algae, we used an analysis of variance (ANOVA) test on photoquadrats at each survey site with depth zone and algae type as predictors of percent cover. We then used PISCO data on kelp density from an adjacent site located near the recovery area (Pt. Pinos) to examine whether the spatial and temporal kelp recovery at deep reefs was associated with sea urchin movement to shallow water.2 transect). However, the density of visually detectible sea urchins in the 2015–2020 period markedly increased to over 700 individuals (per 60 m2 transect). Despite the dramatic increase in the density of visually detectible sea urchins, the size frequency distribution of urchins between 2013 and 2014 was similar, although larger urchins (>4 cm) were more frequently detected in 2014  and provided excellent goodness of fit to the survey data, satisfying all posterior predictive checks Figure . The resR2 = 0.18, p < 0.0001, AICc = 108, 𝚫AIC = 10).We found support for the hypothesis that patch state transitions are explained by shifts in sea urchin behaviour. The linear discriminant analysis revealed that 113 sites persisted as the same starting and ending state across time steps, 11 forward‐shifted from a forest state to barren and 12 reverse‐shifted from a barren state to the forest Figure . Model s2. However, the 50% probability transition threshold for a reverse shift from a barren to a forest was 0.03 exposed urchins/m2.An analysis of the logged odds from the logistic regression revealed evidence of a strong discontinuous state shift Figure . The 50%Nereocystis luetkeana) recolonised the deep depth zone, with a sharp uptick in 2019  rocky reef that was an expansive sea urchin barren just 2 years prior (2017). Long‐term subtidal monitoring data revealed a rapid decline in kelp density across all depth zones in 2014 that coincided with the onset of sea urchin barrens. Starting in 2018, bull kelp  to 14.17 urchins/m2 (±1.19 SE). All size classes of urchins significantly increased in the shallow zone in the 2019 survey year (p < 0.0001). The contrast test revealed that the density of large‐ and medium‐sized urchins significantly declined within the deep zone between 2018 and 2019  and increased within the shallow zone during this same time period . Finally, a comparison of slopes between the deep and shallow zones across the 2018 and 2019 periods revealed that they were significantly different .We found that the total density of sea urchins significantly decreased in the deep zone between the 2018 and 2019 sampling seasons, with the most pronounced effects occurring in the large and medium urchin size classes .Results from analyses on the percent cover of foliose red algae, brown algae and encrusting algae further support the hypothesis of mass sea urchin movement to shallow water. Starting in 2019, the cover of encrusting coralline algae significantly decreased in the deep zone, along with a simultaneous increase in the cover of foliose red and brown algae . Behaviorally driven alternations among patch states across the mosaic further demonstrate the role of grazer behaviour in facilitating transition dynamics.This study demonstrates the important role of grazer behaviour in facilitating patch‐state transition dynamics. The kelp forest‐urchin barrens mosaic that developed following the extirpation of In many systems, behaviour is a primary mechanism for the organisation of ecological communities , lobsters (Panulirus interruptus), sheephead (Semicossyphus pulcher), sunflower sea stars (Pycnopodia helianthoides), rock crab (Cancer spp.) and wolf eels  sea urchins. Behavioural switching within the mosaic across such a short temporal duration may be driven by spatial variability in drift kelp. High levels of drift kelp have been shown to facilitate reef‐scale behavioural feedback in California, Chile and New Zealand , not the giant kelp . Prior to the 2014 sea urchin outbreak, kelp forests along the Monterey Peninsula were dominated by the giant kelp (Foster & Schiel, Nereocystis from light limitation, thereby enabling the rapid recolonisation and growth of Nereocystis following sea urchin movement inshore to shallow water.At the locations where kelp recovery was observed, it is important to note the kelp species that repatriated the once barren grounds was the bull kelp (The results presented in this study highlight the role of behaviorally mediated effects in structuring ecological communities. One of the most unusual aspects of this system is the ability of sea urchins to persist in low‐resource environments for extended periods of time Ebert, , which mJGS conceived the study, performed the analyses and wrote the first draft of the manuscript. MTT developed the population state dynamics model, contributed to manuscript and model revisions and provided substantial conceptual input.https://publons.com/publon/10.1111/ele.14064.The peer review history for this article is available at Appendix S1Click here for additional data file.


All but one personal comfort model had a median prediction accuracy of 0.78 (F1‐score). Skin, indoor, near body temperatures, and heart rate were the most valuable variables for accurate prediction. We found that ≈250–300 data points per participant were needed for accurate prediction. We, however, identified strategies to significantly reduce this number. Our study provides quantitative evidence on how to improve the accuracy of personal comfort models, prove the benefits of using wearable devices to predict thermal preference, and validate results from previous studies.Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right‐Here‐Right‐Now surveys using a smartwatch for 180 days. We collected more than 1080 field‐based surveys per participant. Surveys were matched with environmental and physiological measured variables collected indoors in their homes and offices. We then trained and tested seven machine learning models per participant to predict their thermal preferences. Participants indicated 58% of the time to want  In addition to demonstrating the advantages of employing wearable technology to gather subjective feedback from people, our study validates the findings from earlier research and offers quantitative evidence on how to increase the precision of personal comfort models. Our methodology and results can be used in buildings to develop and implement occupants centric controls. This enables building operators to enhance thermal comfort conditions indoors while possibly reducing the overall energy consumption of the building. We made the decision to openly publish our data so that others might use it to test various assumptions or create personal comfort models utilizing various methodologies.1ti]), and personal  parameters. For example, the Predicted Mean Vote (PMV) predicts the average thermal sensation of a group of people sharing the same environment, as an outcome of the heat transfer balance model between the human body and its surrounding environment. The PMV was developed through laboratory experiments by Fanger,Occupant thermal comfort significantly affects how people perceive their indoor environment, and thermal dissatisfaction is an ongoing challenge. Evidence shows that approximately 40% of the 90 000 surveyed occupants in North America were dissatisfied with their thermal environment.1.1Required inputs—In real buildings, it is extremely challenging to accurately measure some input variables needed to calculate PMV, such as metabolic rate, clothing, airspeed, and mean radiant temperature.Prediction accuracy—Even when all input variables are accurately measured, these models have poor accuracy both in predicting group and individual thermal comfort.Training—Aggregate models do not adapt or re‐learn.Limited inputs—Aggregate models only use a small set of input variables. They do not use variables, such as skin temperature (tsk), heart rate (HR), age, or health status, that may affect the thermal perceptions of people.Both the PMV and the adaptive models have several limitations when used to control the temperature in buildings,erature tsk, hea1.2one‐size‐fits‐all approach of aggregate models. Instead of an average response from a group of people, a single model is trained and tested for each participant. Personal comfort models are, however, not limited to predicting one person's thermal preference. Their aggregated outputs can be used to predict the thermal preference of a large group of people sharing the same environment.Personal comfort models challenge the physiological sensors to improve model accuracy. For example, skin temperature (tsk) reflects the vasomotor tonetsk as an independent variable can improve the prediction accuracy of thermal comfort models.tsk may be even determined using non‐contact sensors like infrared.tsk from body areas that are in the line of sight to the camera and are expensive to install.tsk are common and iButtons sensors are often used. They can accurately measure and log tsk.HR with sufficient accuracy for thermal comfort research; however, none incorporate sufficiently accurate skin temperature sensors.Wearable devices have increased the viability of personal model development due to the use of 1.3Despite the momentum of personal comfort models, there are still several unknowns and limitations as outlined in a recent review.One of the biggest challenges that researchers currently face is recording how people perceive their thermal environment over a long period of time while minimizing the fatigue of completing a Right‐Here‐Right‐Now (RHRN) thermal comfort survey. To partially solve this issue, Kim et al.1.4https://cozie.app and https://cozie‐apple.com. Cozie allows people to conveniently complete an RHRN survey via their smartwatches. Subjects' perceptions, preferences, and behaviors collected via Cozie can then be coupled with environmental data collected from wireless sensing devices and physiological data collected by the smartwatch.To address the limitations mentioned above, an emerging methodology focuses on the use of wearable devices to collect physiological data and act as the subjective feedback collection interface. This method builds upon research in the area of Ecological Momentary Assessments (EMA), a form of collecting subjective information in diverse field‐based settings.1.5How many data points per user must be collected to develop a reliable and robust personal comfort model? We collected data for 180 days resulting in more feedback responses per person (up to 1080) than in any previous study.Are environmental and physiological data sufficient to train personal thermal comfort models while minimizing the impact on users? The methodology of this paper utilizes a novel framework of simple‐to‐use non‐intrusive techniques to collect physiological, environmental, and geospatial data using smartwatch‐based micro‐EMA.Can increasing the diversity of space types and conditions improve the accuracy of personal comfort models? How can different variables contribute to the overall model accuracy? This study is designed to collect data from diverse spaces, including the participants' homes, where there is a lack of data in previous studies. In addition, this paper is novel in accurately monitoring whether the RHRN was completed during transitory conditions.Our research aims to resolve gaps in personal thermal comfort models by collecting field‐based thermal preference data. Our methodology is designed to enable us to address the following questions with resulting novel insights:In addition, we decided to publicly share our data so other people can use it to test different hypotheses or develop personal comfort models using a different methodology.2We collected subjective responses and physiological data from human subjects using wearable devices, personal data using surveys, and environmental data using data loggers. We then applied supervised machine learning algorithms to train personal thermal comfort models for each study participant. Thermal preference votes from the RHRN survey  about participants was collected using a web‐based survey at the beginning of the study.2.2Each participant received a Fitbit Versa (v1 or v2) and was asked to wear it daily for the whole duration of the study.tsk,w) and wrist near body temperature  we installed two iButtons, model DS1925, on the Fitbit wristband. One iButton was installed on the inner side of the wristband and measured tsk,w in the front part of the wrist. The other was installed above the watch display and was used to measure tnb,w. Figure To measure and log wrist skin temperature  while exercising, (ii) when not in the proximity of either of the environmental sensors provided (answered “No” to Q.3), (iii) during a transitory situation (answered “Yes” to Q.7), (iv) when outdoors, and (v) while not wearing the smartwatch correctly. The rationale behind our decisions was that personal comfort models could mainly be used indoors to improve thermal comfort conditions where environmental conditions can be controlled. We provide a detailed description of how we implemented the above‐mentioned selection criteria in Section 2 of the 2.6.2H‐test to test the null hypothesis that the population median of all the groups is equal. The Kruskal–Wallis H‐test was used since the ANOVA assumptions were not satisfied, and it is a non‐parametric version of ANOVA. The rejections of the null hypothesis do not indicate which of the groups differs. Comparisons between groups are required to determine which groups are different.We used seven supervised machine learning classifiers to predict thermal preferences: Logistic Regression (LR), Random Forest (RDF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), K‐Nearest Neighbors (KN), Gaussian Naive Bayes (GNB), and Multi‐Layer Perceptron (MLP). We used the Kruskal–Wallis 2.6.3One of our objectives was to determine how the number of training data points would affect the model accuracy. This has practical applications since it would inform us of the minimum required number of RHRN to be collected from each participant. The hypothesis is that a higher number of data for each participant would lead to more accurate results. To test this, we randomly selected 100 data points for testing and then trained the models using the first 42 RHRN surveys (approximately 1 week of data) each participant completed. We then iteratively trained a new model for each increment which comprised additional 84 training data points.2.6.4ti), and humidity ratio indoors (Wi); clo–met—self‐reported clothing and activity as explained in Section HR), wrist skin temperature , and wrist near body temperature ; time—hour of the day, weekday or weekend, and day of the week.The independent variables we used to train our models are shown in Table hist) to take into account how thermal history may have influenced how participants perceived their environment at the time of completing the RHRN survey. For each of the time‐series data included in either the environmental or the wearable variable sets, we calculated the following additional variables: exponentially weighted moving average and gradient over a 20 and 60 min period preceding the survey. The average and gradient for the weather data were calculated using timeframes of 1 and 8 h.We also computed some variables (We used the SHapley Additive exPlainations (SHAP) method to determine how much each variable influences the output of the model. The primary idea behind Shapley's value‐based explanations of machine learning models is to divide the credit for a model's output among its input variables using fair allocation outcomes from cooperative game theory.env, time, and wearable in all models since previous research has demonstrated that the inclusion of these variables into personal comfort models significantly increases their prediction accuracy.We included We have shared the data we collected publicly so other researchers may test different hypotheses or use a different approach from the one described in this paper.ti), wrist skin temperature , and wrist near body temperature  in all models may introduce multicollinearity. The environment to which a person is exposed, the clothing they wear, and the actions they perform, together which several other factors that affect how indoor air temperature (ti), wrist skin temperature , and wrist near body temperature  are correlated. We, therefore, decided to keep them all in the models since they allowed us to potentially capture all the above‐mentioned interactions that cannot be measured but still play a significant role in how people perceive their thermal environment. For example, the near‐body temperature may approximate the air temperature when a person is exposed to elevated air speeds. On the other hand, it will be more influenced by the skin temperature when the person is resting and the air in the room is still. It is worth mentioning that Apple in their latest smartwatch, the Apple Watch 8 released in October 2022, also included two temperature sensors, one that measures the skin temperature and one below the screen to isolate the body temperature from the outside environment. Apple claims that this allows them to get a more accurate estimate of the variables that they want to predict.Including indoor air temperature  and recall (true positives divided by the number of samples that should have been identified as positives). In multilabel classification,  the F1‐micro is calculated globally across all classes.2.7.2Hyper‐parameters optimization is done using a random search and 5‐fold cross‐validation. We tested 10 random combinations of hyper‐parameters in each of the 5‐fold, and the best performing model, in terms of objective metric as specified in Section 3The longitudinal study commenced in April 2020 and ended in December 2020 in Singapore. A total of 20 participants  took part in our study. Key information about each participant is presented in Table 3.1Participants completed a total of 22212 RHRN. Of the total surveys collected, participants completed 2% of them while exercising, 6% while outdoors, and 12% while in transitory conditions. These surveys were not included in the data analysis as previously explained in Section tsk,w and tnb,w data we measured while the participants completed the RHRN are depicted in Figure tsk,w was higher than tnb,w. This result was expected since the maximum value of ti that participants experienced throughout the study never exceeded 34°C. For example, the delta between tsk,w and tnb,w in participant 10 was as low as −0.7°C, while the average value across all participants was −3.2°C. We consequently remove the data using the methodology detailed in Section 2 of the The This sub‐set of the original dataset, which included 13 073 survey responses, was used in the data analysis. The filtered number of surveys for each participant is shown in Figure 3.2The 13 073 survey responses are summarized in Figure Most of the participants reported being involved in sedentary activities in 77% of the cases. Participants perceived air movement only less than 30% of the time, and 69% of them wore “Light” clothes.ti recorded when a participant completed the survey are shown in Figure To better depict how participants perceive their thermal environment, in Figure The thermal preference votes grouped by the self‐reported clothing and metabolic rates are shown in Figure 3.3The prediction accuracy of the personal comfort model we developed is depicted in Figure p < 0.01) and substantially (excluding XGB) higher (≈37%) than the results obtained from the PMV model. In our study, we only qualitatively logged clothing levels and metabolic rates, and we did not measure airspeed as detailed in Section The prediction accuracy of all the personal comfort models developed with the supervised machine learning algorithms was significantly , wrist near body temperature , heart rate (HR), wrist skin temperature , and humidity ratio indoors (Wi) contributed the most to the models' final predictions, we observed a significant difference of SHAP values between different participants and across different models. In The absolute mean SHAP values across all six best‐performing supervised machine learning models are shown in Figure 4The results of our study enabled us to draw several connections to the existing literature, discuss the usefulness and limitations of the methodology and results, and motivate future work.4.1One novel aspect of our study was the duration of the data collection, which enabled us to gather the longest longitudinal data set so far among studies that aimed to develop personal thermal comfort models.We also observed that, for some participants, the F1‐micro curves did not vary much as a function of the data size . Some possible causes of this are that participants were constantly exposed to warm temperatures and that some did not maintain compliance with experimental guidelines. The latter point is discussed in Section 4.2ti), wrist near body temperature , heart rate (HR), wrist skin temperature , and humidity ratio indoors (Wi) contributed the most to the models' final predictions. This insight is in line with the existing body of knowledge since ti is the primary driver of sensible heat loss or gain from the environment to the human body. Our results reinforce previous work.HR is a proxy for the level of activity of the person, and it is positively correlated with the metabolic rate. The value of tsk,w reflects the vasomotor tone. The human body uses vasoconstriction and vasodilation for thermoregulation.Wi influences the latent heat loss toward the environment. On the other hand, the outdoor air temperature, occupant location, and outdoor humidity ratio only had a marginal contribution to the final prediction, which can be explained by the fact that these variables do not directly influence people's thermal sensation or preference, in particular during steady‐state conditions. The value of the outdoor air temperature only indirectly affects occupants' thermal preferences since they may influence the type of clothing that participants decide to wear before leaving their homes. This result may, however, only be applicable to climates similar to the one in Singapore that are characterized by limited variability.We used SHAP values to quantify of the impact that each independent variable had on the accuracy of the personal models. While the average magnitude for each variable varied in different models indoor air temperature  occupants’ thermal preferences. With the limitations in data collection posed by the study methodology, they could outperform the PMV model.ti), wrist near body temperature , heart rate (HR), wrist skin temperature , and humidity ratio indoors (Wi), listed in decreasing order of importance, had the highest average marginal contribution to the overall model prediction.Indoor air temperature  plateaued at around 300 data points across all participants. Individual personal models are sensitive to dataset size to varying degrees. The amount of data required to characterize thermal comfort could potentially be reduced with the development of targeted sampling, which strategically requests feedback only when it is necessary.We made available publicly the data we collected and open‐sourced the Python code we used to analyze them to enable other researchers to test different hypotheses utilizing our data.We conducted a longitudinal thermal comfort study that aimed to develop personal thermal comfort models. Twenty participants took part in it, and they completed on average at least six RHRN surveys per day for a period of 6 months. We developed an effective methodology that simplified the life of the participants, and none of them dropped from the study. We measured and logged environmental parameters, physiological signals, outdoor weather data, and participants' location outdoors and indoors. We used these data to train and test a personal thermal comfort model for each participant. We were able to determine that:HRheart rate, beats per minutePMVPredicted Mean VoteRHRNRight‐Here‐Right‐NowSVMSupport Vector Machinetiindoor air temperature, °Ctnb,wwrist near body temperature, °Ctskskin temperature, °Ctsk,wwrist skin temperature, °CWiwater vapor/kgdry airhumidity ratio indoors, kgThis research has been supported by the Republic of Singapore's National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore‐Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Appendix S1Click here for additional data file.


Recent work has demonstrated that changes in resource availability can alter a consumer's thermal performance curve (TPC). When resources decline, the optimal temperature and breadth of thermal performance also decline, leading to a greater risk of warming than predicted by static TPCs. We investigate the effect of temperature on coupled consumer‐resource dynamics, focusing on the potential for changes in the consumer TPC to alter extinction risk. Coupling consumer and resource dynamics generally reduces the potential for resource decline to exacerbate the effects of warming via changes to the TPC due to a reduction in top‐down control when consumers near the limits of their thermal performance curve. However, if resources are more sensitive to warming, consumer TPCs can be reshaped by declining resources, leading to increased extinction risk. Our work elucidates the role of top‐down and bottom‐up regulation in determining the extent to which changes in resource density alter consumer TPCs. We investigate the relationship between resource density and temperature (warming) on the persistence of a consumer population. Our work elucidates the importance of jointly considering temperature and resource limitation when assessing the thermal performance of species. We demonstrate how knowledge of the thermal performance of a resource population can be used to generate realized consumer thermal performance curves. Topt), skewed toward a warmer temperature. The lower and upper thermal limits of performance are defined as CTmin and CTmax for physiological traits such as movement rate or response to stimuli, and as Tmin and Tmax for population demographic traits such as growth rate. In the latter case, the difference Tmax−Tmin  is consistent with Hutchinson's definition of the niche provided that resources are non‐limiting  towards cooler temperatures. This shift in the optimum temperature for growth could have additional detrimental consequences for populations if the mismatch between optimal and ambient temperatures leads to behavioural responses. Huey and Kingsolver  is the assumption that food resources or nutrients will decline in concert with warming. There is growing evidence that warming is reducing the availability of food resources for herbivores, particularly when warming is coupled with changes in precipitation  and h (handling time) of the type II functional response have been well established empirically  as temperature (or any other driver) is varied (see below for more detail). In this paper, we utilize this constraint to advance our understanding of the impact of temperature on trophic communities.The unimodal temperature‐dependence of resource production, and the parameters Lemoine, . As we eLemoine,  recentlyBOX 1Brett et al.  and BretC) is proportional to the rate at which it assimilates consumed biomass, less the amount of biomass that is respired in order to maintain vital functions  and most interpretations of consumer growth assume that only the functional response is influenced by resource density (R) , and fR,T is an increasing function of R, then the temperature that maximizes dC/Cdtwill increase with resource density.The rate of accumulation of biomass by a heterotrophic population  and handling rate (1/h) have been shown to exhibit a hump‐shaped relationship with temperature  and β is a parameter determining the breadth of the response. Although the temperature dependence of ingestion may be best described by an asymmetric unimodal function ; however, as resources decline, the optimal temperature for growth shifts to cooler temperatures. The temperatures at which the consumption and respiration rates intersect represent the lower and upper critical temperatures for population growth (Tmin and Tmax); these limits are shown in Box Figure Tmin (on the colder side) and Tmax (on the warmer side) and at the point where they connect (the limit where the TPC collapses to a single point), positive per‐unit growth of the consumer can occur only at the temperature that is midway between the Tmin and Tmax. If we use the formalism of R*theory  corresponding to a resource‐only equilibrium  and a mixed consumer‐resource equilibrium . These equilibrium solutions and their stability can be solved analytically; we provide these in Appendix A1. For this model, one of the two possible equilibria is always stable .We begin our analysis of temperature‐dependent consumer‐resource dynamics using a chemostat model of resource supply, where resources are assumed to be non‐living nutrients. Here, resources are provided at an inflow density Equation , the equS) and temperature (T) falls outside the range allowing positive consumer growth . Turnover is lowest at temperatures near Tmin and then increases approximately exponentially with warming. With increases in turnover, more biomass is lost to conversion inefficiency. Coupled with higher rates of metabolism at warmer temperatures, this leads to a peak in the equilibrium consumer biomass peaks at temperatures below TI and gradual declines in equilibrium consumer biomass with warming  to cope with the issue of negative population growth. In this model, the effects of r are separated from the density‐dependent parameter α, allowing a sign change in r to propagate properly into population dynamics. However, ; however, Vasseur (KT peaks at a lower temperature than rT (similar to the patterns shown for the consumer in the chemostat model).Here, we use the same optimum, Equation ; however Vasseur  showed tξ0,0), a resource‐only equilibrium , and a mixed equilibrium . Analytical solutions for these equilibria and their stability are provided in Appendix A1. In contrast to the chemostat model, this model can also produce a mixed equilibrium point which is unstable, resulting in a stable limit cycle.This model has three possible equilibrium states: a trivial equilibrium . Despite the fact that the resource‐only equilibrium also declines as the consumer approaches its thermal limits, the increase in resource densities caused by a relaxation of top‐down control is superseding factor. Even when consumers are extremely prudent . This parameter (∆T) shifts both the rT and KT functions in accordance with Equations ∆T), the consumer's Tmin increases to reflect the effect of resource limitation at low temperatures due to the resource's temperature dependence. Similarly, if the resource's thermal performance shifts to colder temperatures, the consumer's Tmax becomes limited by the resource's thermal performance and shifts to lower temperatures  determine the consumer's Tmin or Tmax, we find scenarios where temperature‐driven declines in resource density are not superseded by a relaxation in top‐down control. Specifically, when the contours of the resource‐only equilibrium and the consumer's zero‐net‐growth isocline (ZNGI) intersect so that Tmax is located on a negative‐sloping region of the ZNGI, resource densities will decline with warming as consumer's approach Tmax—consistent with the conditions necessary for a ‘metabolic meltdown’  thermal performance. The observation that both the optimum and breadth of the consumer realized performance curves shift to cooler temperatures as resource densities decline suggests that warming, when coupled with a decline in resources could exacerbate extinction risk and generate the necessary conditions for a metabolic meltdown . Dell et al. (CTmin) than their predators and suggested that prey is under selection for broader thermal performance curves to avoid being eaten (life‐dinner principle). Similarly, others have shown that prey has a higher CTmax than their predators  than hosts, so hosts become more at risk when temperatures shift outside of their limits . Temperature change has been shown to reduce vertical niche segregation in deep cold lakes of species including whitefish and vendace  where the effects of parameter changes are well‐known  can achieve population growth. A better understanding of this phenomenon may help identify the causes of nuisance or toxic algal blooms and identify opportunities for mitigation. Overall, this generalizable framework where we develop a mechanistic link between the role of temperature change, species interactions and persistence both identifies gaps in our understanding of thermal responses, and takes a vital step toward improving forecasts of community response to climate change.This study builds upon the work of Thomas et al.  and HueyACV and DAV designed the study and conducted data analysis. ACV wrote the manuscript with substantial input from DAV.ACV acknowledges the support of an NSF PRFB #2010783, Yale University, and the University of Oxford. DAV acknowledges the support of NSF DEB #1754012 and Yale University. NSF DEB, Grant/Award Number: 1856279.https://publons.com/publon/10.1111/ele.14086.The peer review history for this article is available at


The plant microbiome is essential for plant fitness and health. Antibiotics produced by plant‐associated bacteria have been shown to play an important role in protecting plant hosts against phytopathogens. Here, we highlight the strong biotechnological potential of (i) antibiotic producing plant‐associated bacteria as biocontrol agents and (ii) the heterologous expression of antibiotic biosynthetic gene clusters in non‐pathogenic plant‐associated bacteria. We also provide the complete list of the active substances based on bacteria, fungi, and viruses currently approved or pending approval in the European Union, as an indication of the significant emergence and biotechnological applicability of biopesticides. Further progress in this field of research will enable the development of novel biopesticides for the biocontrol of agricultural pests. The potential of plant microbiome for the development of novel biopesticides for the integrated pest management of agricultural crops. Microbial Biotechnology where the authors found important differences in the microbial composition and diversity of healthy and diseased plants, providing evidences that specific native microbial members may assist the pythopathogens during the infection process  of Fusarium graminearum  Marrone, , the maira Table .Microbial Biotechnology, the possibility of heterologously expressing AGCs from Burkholderia species in Paraburkolderia strains was assessed ; project administration ; writing – original draft ; writing – review and editing . Miguel A. Matilla: Conceptualization ; project administration ; writing – original draft ; writing – review and editing .Spanish Ministry for Science and Innovation/Agencia Estatal de Investigación .The authors declare that there is no conflict of interest.


Existing empirical work has focused on assessing the effectiveness of nonpharmaceutical interventions on human mobility to contain the spread of COVID‐19. Less is known about the ways in which the COVID‐19 pandemic has reshaped the spatial patterns of population movement within countries. Anecdotal evidence of an urban exodus from large cities to rural areas emerged during early phases of the pandemic across western societies. Yet, these claims have not been empirically assessed. Traditional data sources, such as censuses offer coarse temporal frequency to analyse population movement over infrequent time intervals. Drawing on a data set of 21 million observations from Meta‐Facebook users, we aim to analyse the extent and evolution of changes in the spatial patterns of population movement across the rural–urban continuum in Britain over an 18‐month period from March 2020 to August 2021. Our findings show an overall and sustained decline in population movement during periods of high stringency measures, with the most densely populated areas reporting the largest reductions. During these periods, we also find evidence of higher‐than‐average mobility from high‐density population areas to low‐density areas, lending some support to claims of large‐scale population movements from large cities. Yet, we show that these trends were temporary. Overall mobility levels trended back to precoronavirus levels after the easing of nonpharmaceutical interventions. Following these interventions, we found a reduction in movement to low‐density areas and a rise in mobility to high‐density agglomerations. Overall, these findings reveal that while COVID‐19 generated shock waves leading to temporary changes in the patterns of population movement in Britain, the resulting vibrations have not significantly reshaped the prevalent structures in the national pattern of population movement. As of 2021, internal population movements sit at an intermediate level between those observed pre‐ and early phases of the pandemic. In addition to stringent international travel restrictions, nonpharmaceutical interventions to contain the spread of COVID‐19 have transformed daily life behaviours resulting in reduced overall levels of mobility  relatively high local mobility; (2) high intensity of mobility from high‐density city areas to less dense areas ; (3) high intensity of mobility between high‐density city areas to low‐density areas  during early stages of the pandemic and (4) a decrease in the intensity of mobility from highly dense city areas as COVID‐19 stringency eased.2.2To determine the extent of change in mobility patterns during COVID‐19, we review preexisting predominant trends in the British mobility system. Globally, the United Kingdom occupies an intermediate rank in terms of overall levels of mobility. Estimates based on 2011 census data, indicate that 6.8 million individuals changed their usual residential address in the last 12 months . The data sets are built from information from users who shared their location history. Before sharing the data, Meta applies three techniques to ensure privacy and anonymisation: Random noise, spatial smoothing and dropping small counts. First, a small undisclosed amount of random noise is added to ensure that precise location cannot be identified for small population counts in sparsely populated areas. While removing small counts may underrepresent the population in these places, it also reflects the actual geographic pattern of population distribution. Second, spatial smoothing is applied to produce a smooth population count surface using inverse distance‐weighted averaging. Third, any remaining population counts of less than 10 are removed from the final data set—see Maas et al. . Comparing the location of individuals between two temporal windows provides data on the number of people moving between locations. Both data sets include a baseline population count indicating the number of Facebook users moving between locations, or a total number of Facebook users in a given location during a fixed baseline period. The baseline period is defined as an average of the population counts covering 45 days; that is, the 45 days before 10 March 2020. The data sets also include a ‘quality’ score indicating the number of standard deviations by which the observed population count at a given time point differs from the baseline population count, highlighting statistically significant changes in population counts. We note that the aggregate nature of the data does not allow distinguishing between various forms of population movement, for example, permanent and daily population moves. However, we distinguished between movements between areas and movements within an area, to capture differences in the impact of the pandemic on mobility. As described above, while COVID‐19 restrictions are expected to have discouraged long‐distance movements , they are argued to have produced increased levels of local mobility .2.Facebook used the Bing Maps Tile System developed by Microsoft as a spatial reference framework to organise the data. It is a geospatial indexing system that partitions the world into tile cells in a hierarchical way, comprising 23 different levels of detail . A description of the timeline of the COVID‐19 pandemic in the United Kingdom is provided in Section 4.1x~ corresponds to the median count for a specific type of population movement ; b is the baseline median mobility count; t relates to the two periods described above; c refers to each population density class as defined in Section I score indicates an increase in the extent of population movement relative to the baseline prepandemic period. A negative I score represents a decrease, while a zero score denotes no changes.We measured changes in mobility inflows, outflows and intraflows across the urban hierarchy between two distinctive periods during the course of the COVID‐19 pandemic: (1) after the announcement of the first lockdown between 23 March 2020 and 19 April 2020, and (2) after the implementation of the government's reopening plan out of lockdown between 19 July 2021 and 16 August 2021. We measured changes in mobility during these periods, relative to precoronavirus levels during the baseline period as defined by Meta‐Facebook . Specifically, we computed the percentage change in mobility inflows, outflows and intraflows by population density class as follows:4.2In the second stage, we used statistical modelling to understand differences in the intensity of mobility between tiles of different population density classes , the origin (Tilei) and destination (Tilej) tiles and the temporal nature of the flow (Tw). Crucially, we included indicator variables that capture the pair of population classes  and destination tile j (J), resulting in 99 pairs (10 classes × 10 classes minus one so it is not collinear with ⍺); dij is the geographic distance between tiles i and j; qijw is a measure of the quality of the flow estimate provided by Meta‐Facebook and related to the uncertainty behind the user count of the flow as described in Section Popi,j represents the population at the origin (i) and destination (j) tiles; β1,2,3,4 are parameters to estimate in the model linking their respective covariates to µijw; D is a trend tracking the day to which the flow relates to during the period in analysis; while Wk and H are indicator variables capturing day of the week  and hour window .We adopted a spatial interaction framework  is systematically higher or lower than if it occurred between a baseline origin‐destination population density class pair of tiles . Additionally, we standardised continuous variables , α so that they can be interpreted as the expected flow on the first day (D = 0), during the first temporal window (H = 00:00–08:00), on a weekday (Wk = 0), for the baseline origin‐population population density class pair, when all the other variables are at their mean value. In this context, each γIJ can also be seen as the ‘modulation factor’ around that expectation associated with each pair of origin‐destination classes. The baseline origin‐destination population density class pair is the lowest population density class as origin and destination.Our focus in Equation  is centrµijw) linked to the flow count (Fijw) through a log link:We used a count data regression model. Specifically, we fitted a generalised linear model (GLM) where the error term is assumed to be distributed following a Poisson distribution, with a flow expectation of (The Poisson regression model (PRM) assumes that equidispersion, that is, equality of mean and variance in the response variable  for individual origin‐destination population density classes over time (x‐axis). These coefficients capture changes in the intensity of mobility between origins and destinations of specific population density levels over the course of the pandemic. Coefficients for origin‐destination pairs were derived from the regression model described in Section y‐axis scale for the regression intercept.Figure Recalling the national levels of mobility during the period in the analysis is important to contextualise our estimates. The evidence so far  across different population density classes. High‐density destination class areas tend to display relatively stable coefficients between March 2020 and August 2021. This suggests limited variations in the size of population movements in high‐density agglomerations during the pandemic, compared to population exchanges involving low‐density areas.A third key feature involves population movements between population density areas of similar classes reported in the diagonal of Figure 66.1During the early stages of the COVID‐19 pandemic, anecdotal reports of an ‘urban exodus’ from big cities in various western societies emerged. Using smartphone application data from Facebook users, we sought to analyse the extent and durability of changes in human mobility patterns across the rural–urban hierarchy in Britain during the COVID‐19 pandemic from March 2020 to August 2021. We found evidence of an overall and sustained decline in human mobility between areas during the enactment of nonpharmaceutical interventions between March 2020 and February 2021, bouncing back to precoronavirus levels following the rollout of the government lockdown exit strategy in March 2021. Declines in mobility between areas during high levels of stringency co‐occurred with increases in mobility within areas, probably reflecting rises in active travel and e‐forms of transport (Li et al., We presented evidence of higher‐than‐average patterns of mobility from highly dense population areas to low densely populated areas as stringent nonpharmaceutical interventions were enacted and overall levels of mobility declined across Britain. This pattern is consistent with arguments of migration away from dense agglomerations as they became key early epicentres of COVID‐19 infections and lost their urban vibrancy because of business, school closures, social distancing and lockdowns (Florida et al., We showed evidence of a systematic decline in mobility to low‐density areas, sustained mobility between high‐density areas and a rise in mobility intensity between areas of similarly high population density levels as COVID‐19 restrictions were eased. These findings suggest that while COVID‐19 generated shock waves leading to temporary changes in the patterns of population movement in Britain, the resulting vibrations have not significantly reshaped the prevalent existing structures in the national internal migration system. Large and dense urban areas are likely to remain key centres of population movement. Hybrid forms of working may become widely adopted and predominant ways of interaction. Sparsely rural locations lack the infrastructure and services needed to support hybrid working arrangements OECD, . Poor br6.2Assessing the wider generalisability of our findings is challenging. Digital footprint data are known to suffer from biases and issues of representation reflecting differences in digital technology penetration, usage and accessibility Rowe, . In the We analysed the spatial patterns of mobility across the rural–urban continuum. Future work is needed to establish the causes of the observed changes in the spatial direction of mobility during COVID‐19. Understanding these causes can help anticipating long‐term structural changes in mobility intensity extending beyond the pandemic. A combination of factors, including school shutdowns, business closures, social distancing, telework, employment density, housing space and affordability have been cited as key forces altering the preexisting patterns of population movement during the pandemic and triggering moves away from cities. While some of these factors have already dissipated as COVID‐19 restrictions have been lifted, factors such as telework will most likely to endure the pandemic and become the main form of engaging with work (Florida et al., The authors declare that there is no conflict of interest.The research meets all ethical guidelines, including adherence to the legal requirements of the study country and institutional ethical requirements of the University of Liverpool.Supporting information.Click here for additional data file.


We hereby present a case of thrombus formation in the noncoronary sinus of Valsalva following primary graft dysfunction. The case highlights that stagnant and nonpulsatile flow can form thrombi in the noncoronary sinus since this sinus does not have a natural distal runoff. Primary graft dysfunction following heart transplantation is a calamity. The low cardiac output caused by mono‐ or biventricular dysfunction requires inotropes, pressors, and occasionally temporary mechanical support.A 66‐year‐old woman with a mutation negative arrythmogenic right ventricular cardiomyopathy and previous multiple implantable cardioverter defibrillators underwent an ABO‐compatible primary heart transplant. The orthotropic transplantation was done through a median sternotomy with standard bicaval and aortic ascendens cannulation. Cold crystalloid cardioplegia was administrated after suturing the left atrium and the pulmonary artery. The transplantation was technically uneventful with a total ischemia time of 92 min . The donor heart was preserved using cold static storage. The donor heart  started in sinus rhythm, but did not generate forward flow and intraoperative transesophageal echocardiography showed severe biventricular dysfunction. After prolonged reperfusion peripheral veno‐arterial extracorporeal membrane oxygenation (V‐A ECMO) as well as an intraaortic balloon pump were placed and the patient transferred to the intensive care unit (ICU). The bed‐side echocardiogram in the ICU showed severe biventricular dysfunction. There was no flow in the left main coronary artery, and the aortic valve did not open. On the evening of postoperative day (POD) 1 a coronary arteriography was done that showed sluggish flow in the right and left coronary artery with a thrombus in the noncoronary sinus (Figure Biventricular dysfunction after a heart transplant needing mechanical support can be seen in the setting of primary graft dysfunction.All authors have contributed significantly to the manuscript and read and approved the final version of the manuscript.The authors declare no conflicts of interest.Written informed consent was obtained from the patient for publication of this case report and accompanying images.


Solid tumours represent the most frequent type of tumours worldwide, and they are responsible for high morbidity and mortality rates.An ever‐increasing volume of data shed light on the effect of abnormal mechanically‐induced stimulation of the extracellular matrix (ECM) and corresponding distorted mechanotransduction of cancer cells and neighbouring stromal cells in tumorigenesis.Besides genetic mutations, other factors including chronic inflammation, tissue injury and accumulated mechanical stress reprogramme and activate differentiating regulatory circuits that were normally silent in adults.The term polycystins refers to a relatively new family of proteins that comprises eight protein species. Two representative members of the family are polycystin‐1 (PC1) and polycystin‐2 (PC2), which are encoded by the polycystic kidney disease 1 (PKD1) and polycystic kidney disease 2 (PKD2) genes respectively.2+ ions, which belongs to the family of transient receptor potential (TRP) channels.2+ ions.Polycystin‐1 is a transmembrane protein bearing a long, flexible, mechanosensitive N‐terminus and an intracellular C‐terminus that gives rise to transcriptionally active peptide fragments. It operates as a mechanosensor that perceives extracellular mechanical cues and regulates vital cellular functions such as proliferation, differentiation and apoptosis.Among the biological properties that characterize cancer cells is the continuous transmission of signals that promote proliferation, resistance to apoptosis and activation of invasive mechanisms and metastasis.2+ influx and PC1 acts as a membrane modulator of cell‐to‐cell and cell‐to‐ECM interactions with a putative capacity of modifying oncogenic transcription programmes.Polycystins are involved in the control of cellular homeostasis, particularly during embryonic morphogenesis and regeneration of differentiated tissues by regulating the cell‐cycle, intercellular interactions and interactions with the ECM. PC1 is mainly located at multiple focal adhesion structures, the primary cell structures that mediate communication of cells with the ECM.Increased expression of polycystins in the cystic epithelium as a reflection of the dedifferentiation of the tissue and the perception of cysteogenesis as a multistep process that requires the progressive accumulation of genetic alterations, have justifiably attributed to ADPKD the term ‘neoplasia in disguise’.Few studies have been published discussing the relationship of polycystins with carcinogenesis. Overexpression of PC1 in lung cancer, colon cancer and hepatocellular carcinoma cell lines led to promotion of ECM and intercellular interactions, thus inhibiting invasion and migration of tumour cells via the Wnt pathway, attributing to PC1 a potential tumour suppressing role.Strong evidence for the validity of this model comes from another study where PC1 and PC2 were demonstrated to be engaged in the development of aggressive phenotypes in colorectal cancer (CRC).Mechanotransduction and the overall investigation of the contribution of the mechanical forces exerted on normal cells and also on tumour cells during the development and invasion of solid tumours is a rapidly evolving research area over the past few years. Nevertheless, the exact molecular mechanisms by which mechanotransduction is involved in tumorigenesis remain a challenge. Polycystins are decisive components of mechanotransduction that regulate a wide gamut of cellular functions during embryogenesis and adult life.High heterogeneity of solid tumours, even among the same tumour cells, is a major obstacle to the development of effective treatment strategies. Even newly developed remedies demonstrate acquired resistance after a certain period of time; hence, elucidation of the molecular mechanisms underpinning the polycystins/TRP channels‐funneled mechanotransduction process is essential for the discovery of novel diagnostic, prognostic and therapeutic tools,The authors confirm that there are no conflicts of interest.Kostas A. Papavassiliou: Data curation (lead); Writing – original draft (lead). Antonios N. Gargalionis: Data curation (lead); Writing – original draft (lead). Athanasios G. Papavassiliou: Conceptualization (lead); Supervision (lead); Validation (lead); Writing – review & editing (lead).