Biology subjects

Smith, J. P.

Publications and source records attributed to Smith, J. P..

18 recordsLinked to original sources

bioRxiv2025

Hypoxia-Induced Metabolic Reprogramming and Markings of Cell Fate in Concentric Arterial Hypertrophy

Chronic inhibition of the renin-angiotensin system (RAS), while widely used to treat hypertension, can lead to an underrecognized form of vascular disease marked by concentric arteriolar and arterial hypertrophy (CAAH). Here, using two lineage-traced mouse models of genetic renin deletion and sustained RAS blockade, we uncover a pathogenic cascade initiated by renin-lineage cell fate reprogramming. Loss of endocrine identity and transformation of smooth muscle cells drives a shift toward a fibrotic, inflammatory, and secretory phenotype that remodels the extracellular matrix and promotes vascular thickening and luminal narrowing. Integrated transcriptomic, proteomic, and metabolomic profiling revealed a hypoxia-linked metabolic switch--characterized by succinate accumulation and NAD+ depletion--coupled to Hif activation and disease progression. We identify Cdh13 and collagens (including Col1a1 and Col12a1) as early urinary biomarkers and define a 10-gene molecular signature of CAAH with potential clinical application. These findings establish renin-lineage cell plasticity and metabolic dysfunction as central drivers of CAAH and nominate candidate biomarkers for early detection and therapeutic targeting in RAS-inhibited patients.

physiology↗

bioRxiv2025

Renin Cells Drive Kidney Neurovascular Development and Arterial Remodeling when Renin Activity is Deficient

Renin cells synthesize and release the hormone-enzyme renin to regulate blood pressure and fluid-electrolyte homeostasis. Their function and identity depend on communication with surrounding cells and nerve fibers within complex kidney structure. Because renin cells are rare -0.01 % of kidney cells-conventional histological approaches cannot capture their interaction with nerve fibers and surrounding cells within the nephron and its vasculature. Using a novel ultrabright renin cell-specific tdTomato reporter mouse, high-resolution 3D imaging, and single-cell RNA-Seq, we mapped the interactions of renin cells with growing axons during normal kidney vascular development, in response to threats to homeostasis, and a severe arterial disease caused by a defective renin enzyme. During embryonic kidney development, stromal and renin cell progenitors assemble the arterioles, express axon attractants and neurotrophins that establish the precise innervation of renin cells and arterioles in a centrifugal pattern. Hypotension and sodium depletion led to an increase in the volume and number of renin cells along the arterioles. Renin enzymatic deficiency led to hypertrophy and endocrine transformation of renal arterioles, aberrant axon sprouting and sympathetic hyperinnervation suggesting a feed-forward mechanism whereby renin cells and axons co-induce each other, orchestrate neurovascular development and arteriolar remodeling when renin cells are over stimulated.

developmental biology↗

bioRxiv2025

Bright Cells, and Inefficient Enzymatic Activity in a Renin Hypomorph Mouse

Juxtaglomerular (JG) cells are crucial regulators of blood pressure and fluid-electrolyte homeostasis. Under normal conditions, renin secretion by JG cells is sufficient to maintain homeostasis. However, under physiological stress such as narrowing of one of the renal arteries, heart failure, dehydration, or chronic administration of renin-angiotensin system (RAS) inhibitors, additional cells along the renal arterioles are transformed to the renin phenotype to meet the demands for renin and regain homeostasis. In cases of prolonged and persistent stimulation of renin cells, concentric arteriolar hypertrophy develops. The study of renin cell identity, plasticity and function often requires the isolation of this rare cell type. Here, we report on the generation of a mouse model to label renin-expressing cells with a bright fluorescent reporter under control of the Ren1c locus for the tracking and isolation of renin cells. Kidneys from adult heterozygous (Het) Ren1ctdTomato/+ mice showed tdTomato signal confined to the JG area under basal conditions, and extending along the afferent arterioles and in the intraglomerular mesangium upon treatment with captopril + low-salt diet to induce the endocrine transformation of renin cells. Unexpectedly, homozygous (Homo) Ren1ctdTomato/tdTomato mice exhibited increased tdTomato signal that extended along the afferent arterioles and into the mesangium even under normal physiological conditions, with progressive thickening of the kidney arterioles with age. Despite reduced renin immunostaining in the renal cortex, Ren1ctdTomato/tdTomato Homo mice exhibited significantly higher kidney Ren1 mRNA and circulating renin levels when compared to Het controls. Moreover, Homo mice showed significantly lower blood pressure measured under anesthesia and angiotensin I (Ang I) plasma levels, indicating compromised renin activity. In addition, Homo mice developed interstitial fibrosis and compromised kidney function. The concentric arteriolar hypertrophy phenotype observed in these mice is identical to that described when RAS is genetically or pharmacologically inhibited, including the presence of mutations in the renin gene. Unlike mice with global deletion of renin, these animals did not require neonatal saline injections to survive and did not develop other kidney abnormalities, indicating that the bicistronic approach rendered a renin hypomorphic mouse. Ren1ctdTomato mice constitute an excellent model for the bright and strong labeling of renin-expressing cells and for the study of the mechanisms involved in the development of concentric vascular hypertrophy under RAS inhibition. In addition, this model may provide a better understanding of factors controlling renin protein folding, stability, packaging, and release.

physiology↗

bioRxiv2024

Angiotensin II elicits robust calcium oscillations coordinated within juxtaglomerular cell clusters to suppress renin secretion.

BACKGROUNDJuxtaglomerular (JG) cells are sensors that control blood pressure (BP) and fluid-electrolyte homeostasis. They are arranged as clusters at the tip of each afferent arteriole. In response to a decrease in BP or extracellular fluid volume, JG cells secrete renin, initiating an enzymatic cascade that culminates in the production of angiotensin II (AngII), a potent vasoconstrictor that restores BP and fluid-electrolyte homeostasis. In turn, AngII exerts negative feedback on renin release concomitantly with increased intracellular Ca2+, preventing excessive circulating renin and hypertension. However, within their native structural organization, the intricacies of intracellular Ca2+ signaling dynamics and their sources remain uncharacterized. METHODSWe generated mice expressing the JG cell-specific genetically encoded Ca2+ indicator (GCaMP6f) to investigate Ca2+ dynamics within JG cell clusters ex vivo and in vivo. For ex vivo Ca2+ imaging, acutely prepared kidney slices were perfused continuously with a buffer containing variable Ca2+ and AngII concentrations {+/-} Ca2+ channel inhibitors. For in vivo Ca2+ image capture, native mouse kidneys were imaged in situ using multi-photon microscopy with and without AngII administration. ELISA measurements of renin concentrations determined acute renin secretion ex vivo and in vivo, respectively. RESULTSEx vivo Ca2+ imaging revealed that JG cells exhibit robust and coordinated intracellular oscillatory signals with cell-cell propagation following AngII stimulation. AngII dose-dependently induced stereotypical burst patterns characterized by consecutive Ca2+ spikes, which inversely correlated with renin secretion. Pharmacological channel inhibition identified key sources of these oscillations: endoplasmic reticulum Ca2+ storage and release, extracellular Ca2+ uptake via ORAI channels, and intercellular communication through gap junctions. Blocking ORAI channels and gap junctions reduced AngII inhibitory effect on renin secretion. In vivo Ca2+ imaging demonstrated robust intracellular and intercellular Ca2+ oscillations within JG cell clusters under physiological conditions, exhibiting spike patterns consistent with those measured in ex vivo preparations. Administration of AngII enhanced the Ca2+ oscillatory signals and suppressed acute renin secretion in vivo. CONCLUSIONAngII elicits coordinated intracellular and intercellular Ca2+ oscillations within JG cell clusters, ex vivo and in vivo. The effect is driven by endoplasmic reticulum-derived Ca2+ release, ORAI channels, and gap junctions, leading to suppressed renin secretion.

cell biology↗

bioRxiv2024

Real-time monitoring of attenuated cytomegalovirus using Raman spectroscopy allows non-destructive characterization during flow

Real-time monitoring of viral particles can have a crucial impact on vaccine manufacturing and can alleviate public health by supporting continuous supply. Spectroscopic methods such as Raman spectroscopy can provide rapid and non-invasive measurements. Here, we have developed a Raman spectroscopy-based tool to monitor the quality and quantity of viral particles in a continuous flow set-up. The attenuated human cytomegalovirus (CMV) is characterized across a wide range of concentrations (4.50 x 109 to 2.90 x 1011 particles/mL) and flow rates (100 {micro}m/s to 1000 {micro}m/s) within a square quartz capillary. This process analytical technology (PAT) tool enables the detection of viral particles even at high flow rates such as 1000 {micro}m/s. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and dynamic light scattering (DLS) demonstrated that the samples maintain their integrity even after laser exposure, reiterating the non-invasive nature of Raman spectroscopy. To the best of our knowledge, this is the first report on characterizing CMV particles using Raman spectroscopy. We have also demonstrated the limit of detection (LoD) (2.36 x 1010 particles/mL) for CMV particles in continuous flow (via the Raman spectroscopy method) by addressing the effect of flow rate, concentration, and integrity of samples. This technology could enhance our understanding of the quality control in bio-manufacturing processes required in vaccine production.

bioengineering↗

bioRxiv2024

Efficiency and reliability in biological neural network architectures

Simplified models of neural networks have demonstrated the importance of establishing a reasonable tradeoff between memory capacity and fault-tolerance in cortical coding schemes. The intensity of the tradeoff is mediated by the level of neuronal variability. Indeed, increased redundancy in neuronal activity enhances the robustness of the code at the cost of the its efficiency. We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate this tradeoff, thereby allowing different subpopulations of the same network to optimize for different objectives. To distinguish between subpopulations, we developed a metric based on the mathematical theory of simplicial complexes that captures the complexity of their connectivity, by contrasting its higher-order structure to a random control. To confirm the relevance of our metric we analyzed several openly available connectomes, revealing that they all exhibited wider distributions of simplicial complexity across subpopulations than relevant controls. Using a biologically detailed cortical model and an electron microscopic data set of cortical connectivity with co-registered functional data, we showed that subpopulations with low simplicial complexity exhibit efficient activity. Conversely, subpopulations of high simplicial complexity play a supporting role in boosting the reliability of the network as a whole, softening the robustness-efficiency tradeoff. Crucially, we found that both types of subpopulations can and do coexist within a single connectome in biological neuronal networks, due to the heterogeneity of their connectivity. Our work thus suggests an avenue for resolving seemingly paradoxical previous results that assume homogeneous connectivity.

neuroscience↗

bioRxiv2023

Protein Kinase A Inhibition Epigenetically Silences Ren1

RationaleRenin-expressing cells are myoendocrine cells crucial for survival which detect changes in blood pressure and release renin to maintain homeostasis. One of the pathways responsible for renin expression includes cAMP as a crucial factor. cAMP binds to subunits of protein kinase A (PKA), ultimately recruiting both CBP and p300. Binding to the cAMP-responsive element in the renin enhancer region thus amplifies renin transcription. ObjectiveTo evaluate transcriptomic and epigenomic changes occurring at the renin locus via cAMP pathway inhibition. Methods and ResultsWe treated As4.1 cells (a tumoral cell line that constitutively expresses renin) with the PKA inhibitor H89 (treated) or DMSO (control). We then performed independent ATAC-seq, scRNA-seq, and ChIP-seq for H3K27Ac and P300 binding on biological replicates of treated and control As4.1 cells. Ren1 expression is significantly reduced following PKA inhibition with a corresponding loss in H3K27Ac and P300 binding at the locus. A restricted set of nine genes with overlapping dynamically accessible regions, differential gene expression, and H3K27Ac and P300 binding were identified with roles among three primary renin regulatory paradigms. ConclusionsThe data suggests that cAMP pathway inhibition controls renin expression through a reduction not in accessibility alone, but via a switch from an active to poised state of epigenetic control, a shift towards a less differentiated cellular identity, and the disruption of not only cAMP, but baroreceptor and Notch mediated renin regulatory pathways.

genomics↗

bioRxiv2023

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings

MotivationData from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower-dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. ResultsWe implemented our approach in scEmbed, an unsupervised machine learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, pre-trained models on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. AvailabilityscEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

genomics↗

bioRxiv2023

Determinants of renin cell differentiation: a single cell epi-transcriptomics approach

RationaleRenin cells are essential for survival. They control the morphogenesis of the kidney arterioles, and the composition and volume of our extracellular fluid, arterial blood pressure, tissue perfusion, and oxygen delivery. It is known that renin cells and associated arteriolar cells descend from FoxD1+ progenitor cells, yet renin cells remain challenging to study due in no small part to their rarity within the kidney. As such, the molecular mechanisms underlying the differentiation and maintenance of these cells remain insufficiently understood. ObjectiveWe sought to comprehensively evaluate the chromatin states and transcription factors (TFs) that drive the differentiation of FoxD1+ progenitor cells into those that compose the kidney vasculature with a focus on renin cells. Methods and ResultsWe isolated single nuclei of FoxD1+ progenitor cells and their descendants from FoxD1cre/+;R26R-mTmG mice at embryonic day 12 (E12) (ncells=1234), embryonic day 18 (E18) (ncells=3696), postnatal day 5 (P5) (ncells=1986), and postnatal day 30 (P30) (ncells=1196). Using integrated scRNA-seq and scATAC-seq we established the developmental trajectory that leads to the mosaic of cells that compose the kidney arterioles, and specifically identified the factors that determine the elusive, myo-endocrine adult renin-secreting juxtaglomerular (JG) cell. We confirm the role of Nfix in JG cell development and renin expression, and identified the myocyte enhancer factor-2 (MEF2) family of TFs as putative drivers of JG cell differentiation. ConclusionsWe provide the first developmental trajectory of renin cell differentiation as they become JG cells in a single-cell atlas of kidney vascular open chromatin and highlighted novel factors important for their stage-specific differentiation. This improved understanding of the regulatory landscape of renin expressing JG cells is necessary to better learn the control and function of this rare cell population as overactivation or aberrant activity of the RAS is a key factor in cardiovascular and kidney pathologies.

developmental biology↗

bioRxiv2022

Modeling and Simulation of Rat Non-Barrel Somatosensory Cortex. Part I: Modeling Anatomy

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and a 211,712 neuron subvolume is made openly available to the community.

neuroscience↗

bioRxiv2022

Replicative Instability Drives Cancer Progression

In the past decade, defective DNA repair has been increasingly linked with cancer progression. Human tumors with markers of defective DNA repair and increased replication stress have been shown to exhibit genomic instability and poor survival rates across tumor types. Here we utilize-omics data from two independent consortia to identify the genetic underpinnings of replication stress, therapy resistance, and primary carcinoma to brain metastasis in BRCA wildtype tumors. In doing so, we have defined a new pan-cancer class of tumors characterized by replicative instability (RIN). RIN is defined by genomic evolution secondary to replicative challenge. Our data supports a model whereby defective single-strand break repair, translesion synthesis, and non-homologous end joining effectors drive RIN. Collectively, we find that RIN accelerates cancer progression by driving copy number alterations and transcriptional program rewiring that promote tumor evolution. Statement of SignificanceDefining the genetic basis of genomic instability with wildtype BRCA repair effectors is a significant unmet need in cancer research. Here we identify and characterize a pan-cancer cohort of tumors driven by replicative instability (RIN). We find that RIN drives therapy resistance and distant metastases across multiple tumor types.

cancer biology↗

bioRxiv2022

Single-cell Transcriptome and Accessible Chromatin Dynamics During Endocrine Pancreas Development

Delineating gene regulatory networks that orchestrate cell-type specification is an ongoing challenge for developmental biology studies. Single-cell analyses offer opportunities to address these challenges and accelerate discovery of rare cell lineage relationships and mechanisms underlying hierarchical lineage decisions. Here, we describe the molecular analysis of pancreatic endocrine cell differentiation using single-cell gene expression, chromatin accessibility assays coupled to genetic labeling and cell sorting. We uncover transcription factor networks that delineate {beta}-, - and{delta} -cell lineages. Through genomic footprint analysis we identify transcription factor-regulatory DNA interactions governing pancreatic cell development at unprecedented resolution. Our analysis suggests that the transcription factor Neurog3 may act as a pioneer transcription factor to specify the pancreatic endocrine lineage. These findings could improve protocols to generate replacement endocrine cells from renewable sources, like stem cells, for diabetes therapy.

developmental biology↗

bioRxiv2021

Embeddings of genomic region sets capture rich biological associations in lower dimensions

MotivationGenomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. ResultsWe propose a new method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. We compared our approach to two simpler methods based on interval unions or term frequency-inverse document frequency and evaluated the methods in three ways: First, by classifying the cell line, antibody, or tissue type of the region set; second, by assessing whether similarity among embeddings can reflect simulated random perturbations of genomic regions; and third, by testing robustness of the proposed representations to different signal thresholds for calling peaks. Our word2vec-based region set embeddings reduce dimensionality from more than a hundred thousand to 100 without significant loss in classification performance. The vector representation could identify cell line, antibody, and tissue type with over 90% accuracy. We also found that the vectors could quantitatively summarize simulated random perturbations to region sets and are more robust to subsampling the data derived from different peak calling thresholds. Our evaluations demonstrate that the vectors retain useful biological information in relatively lower-dimensional spaces. We propose that vector representation of region sets is a promising approach for efficient analysis of genomic region data. Availabilityhttps://github.com/databio/regionset-embedding

bioinformatics↗

bioRxiv2020

Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies

1In motor-related brain regions, movement intention has been successfully decoded from in-vivo spike train by isolating a lower-dimension manifold that the high-dimensional spiking activity is constrained to. The mechanism enforcing this constraint remains unclear, although it has been hypothesized to be implemented by the connectivity of the sampled neurons. We test this idea and explore the interactions between local synaptic connectivity and its ability to encode information in a lower dimensional manifold through simulations of a detailed microcircuit model with realistic sources of noise. We confirm that even in isolation such a model can encode the identity of different stimuli in a lower-dimensional space. We then demonstrate that the reliability of the encoding depends on the connectivity between the sampled neurons by specifically sampling populations whose connectivity maximizes certain topological metrics. Finally, we developed an alternative method for determining stimulus identity from the activity of neurons by combining their spike trains with their recurrent connectivity. We found that this method performs better for sampled groups of neurons that perform worse under the classical approach, predicting the possibility of two separate encoding strategies in a single microcircuit.

neuroscience↗

bioRxiv2020

PEPATAC: An optimized ATAC-seq pipeline with serial alignments

MotivationAs chromatin accessibility data from ATAC-seq experiments continues to expand, there is continuing need for standardized analysis pipelines. Here, we present PEPATAC, an ATAC-seq pipeline that is easily applied to ATAC-seq projects of any size, from one-off experiments to large-scale sequencing projects. ResultsPEPATAC leverages unique features of ATAC-seq data to optimize for speed and accuracy, and it provides several unique analytical approaches. Output includes convenient quality control plots, summary statistics, and a variety of generally useful data formats to set the groundwork for subsequent project-specific data analysis. Downstream analysis is simplified by a standard definition format, modularity of components, and metadata APIs in R and Python. It is restartable, fault-tolerant, and can be run on local hardware, using any cluster resource manager, or in provided Linux containers. We also demonstrate the advantage of aligning to the mitochondrial genome serially, which improves the accuracy of alignment statistics and quality control metrics. PEPATAC is a robust and portable first step for any ATAC-seq project. AvailabilityBSD2-licensed code and documentation at https://pepatac.databio.org.

genomics↗

bioRxiv2020

Quality control and processing of nascent RNA profiling data

Nascent RNA profiling is growing in popularity; however, there is no standard analysis pipeline to uniformly process the data and assess quality. Here, we introduce PEPPRO, a comprehensive, scalable work-flow for GRO-seq, PRO-seq, and ChRO-seq data. PEPPRO produces uniformly processed output files for downstream analysis and assesses adapter abundance, RNA integrity, library complexity, nascent RNA purity, and run-on efficiency. PEPPRO is restartable and fault-tolerant, records copious logs, and provides a web-based project report. PEPPRO can be run locally or using cluster, providing a portable first step for genomic nascent RNA analysis. AvailabilityBSD2-licensed code and documentation: https://peppro.databio.org.

bioinformatics↗

bioRxiv2019

The Anadromous Hickory Shad (Clupeiformes: Clupeidae, Alosa mediocris ): Morphometric and Meristic Variation

The anadromous Hickory Shad Alosa mediocris (Mitchill, 1814) (Clupeiformes: Clupeidae) is reviewed, specifically regarding morphometric and meristic variation. Despite its long history as recognized species, few descriptions of Hickory Shad morphometric and meristic characters exist in the literature. Most authors of the historic literature have failed to provide capture location for specimens, analyze large numbers of Hickory Shad, or document how morphometric and meristic characters of the species vary spatially. To address this information gap, a total of 717 mature Hickory Shad were collected from 23 different locations in Maryland, Delaware, Virginia, North Carolina, South Carolina, Georgia, and Florida using electroshocking, gill net, or rod and reel. All specimens were frozen, thawed, and 17 morphometric characters and four meristic characters were examined; a random subset (n = 463) were analyzed for an additional four meristic counts of gill rakers. Overall specimens ranged from 206-389 mm SL with a mean + SD of 278.41 + 27.69 mm, 232-435 mm FL with a mean of 310.98 + 30.35 mm, and 272-508 mm TL with a mean of 365.62 + 35.52 mm. The linear relationships between FL and TL, and FL and SL, were investigated and found to be: TL = 1.169*FL + 1.660 (n=705, r2=0.995) and SL = 0.909*FL - 4.274 (n=717, r2=0.992). Substantial differences in character means for many morphometric measurements were found between male and female specimens, suggesting strong sexual dimorphisms relating to shape. However, meristic characters did not show differences in character means by sex. No one morphometric measurement could distinguish Hickory Shad from other morphologically similar clupeids, but the meristic count of gill rakers on the lower limb of the first arch were important to separate Hickory Shad (19-22) from American Shad A. sapidissima (Wilson, 1811), Alewife A. pseudoharengus (Wilson, 1811), and Blueback Herring A. aestivalis (Mitchill, 1814).

zoology↗

bioRxiv2018

Modelling the distribution of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competitive effects

Florida faces the challenge of repeated introduction and autochthonous transmission of arboviruses transmitted by Aedes aegypti and Aedes albopictus. Empirically-based predictive models of the spatial distribution of these species would aid surveillance and vector control efforts. To predict the occurrence and abundance of these species, we fit mixed-effects zero-inflated negative binomial regression to a mosquito surveillance dataset with records from more than 200,000 trap days, covering 73% of the land area and ranging from 2004 to 2018 in Florida. We found an asymmetrical competitive interaction between adult populations of Aedes aegypti and Aedes albopictus for the sampled sites. Wind speed was negatively associated with the occurrence and abundance of both vectors. Our model predictions show high accuracy (72.9% to 94.5%) in the validation tests leaving out a random 10% subset of sites and data from 2018, suggesting a potential for predicting the distribution of the two Aedes vectors.

ecology↗