SEARCH · PubMed Health

Results for “predictive modelling”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 775 records · Page 43Linked to original sources

PubMed2006

Kinetics of tropomyosin denaturation as a predictive model for verifying thermal processing of beef products.

An enzyme-linked immunosorbent assay (ELISA) was developed to study thermal denaturation of tropomyosin (Tm) using the time-temperature requirements for cooked beef. The ELISA employed a monoclonal antibody (MAb 2C9) raised against bovine Tm for quantifying residual Tm in muscle extracts. The specificity of MAb 2C9 to bovine Tm was demonstrated by Western blot and the analytical validity of ELISA was confirmed by dot blot. Thermal denaturation of Tm, in the temperature range between 54.4 and 70.0 degrees C, showed first-order dependency. Kinetic parameters of Tm denaturation were derived from isothermal heating of beef muscle extract at 54.4, 57.2, 60.0, and 62.8 degrees C. Temperature dependency of the rate constant (k) was demonstrated by Arrhenius plot; the activation energy (E(a)) of Tm denaturation was determined to be 484 kJ x mol(-1). A mathematic model describing the impact of the heating time-temperature on Tm denaturation was developed. Predicted Tm from the integrated time-temperature model agreed closely with the measured Tm in dynamically heat-processed beef samples. Percent errors between the measured and the predicted values ranged from -5.1 to 5.3%. The kinetic model provides an accurate and reproducible prediction of the impact of actual heating time-temperature on residual Tm in cooked beef. The MAb-based ELISA and kinetic model developed in this study have the potential to be adapted by the meat industry as a quality control tool.

Animals↗

PubMed2004

In silico prediction models for blood-brain barrier permeation.

The ability to permeate across the blood brain barrier (BBB) is essential for drugs acting on the central nervous system (CNS). Thus, for speeding up the drug discovery process in the CNS-area, it is of great importance to develop systems that allow rapid and inexpensive screening of the BBB-permeability properties of novel lead compounds or at least small subsets of combinatorial CNS-libraries. In this field, in silico prediction methods gain increasing importance. Starting with simple regression models based on calculation of lipophilicity and polar surface area, the field developed via PLS methods to grid based approaches (e.g. VolSurf). Additionally, the use of artificial neural networks gain increasing importance. However, permeation through the BBB is also influenced by active transport systems. For nutrients and endogenous compounds, such as amino acids, monocarboxylic acids, amines, hexoses, thyroid hormones, purine bases and nucleosides, several transport systems regulating the entry of the respective compound classes into the brain have been identified. The other way round there is striking evidence that expression of active efflux pumps like the multidrug transporter P-glycoprotein (P-gp) on the luminal membrane of the brain capillary endothelial cells accounts for poor BBB permeability of certain drugs. Undoubtedly, P-gp is an important impediment for the entry of hydrophobic drugs into the brain. Thus, proper prediction models should also take into account the active transport phenomena.

Biological Transport↗

PubMed2005

Comparison of field-observed and model-predicted plume trends at fuel-contaminated sites: implications for natural attenuation rates.

Subsequent to modeling of natural attenuation processes to predict contaminant trends and plume dynamics, monitoring data were used to evaluate the effectiveness of natural attenuation at reducing contaminant concentrations in groundwater at seven fuel-contaminated sites. Predicted and observed contaminant trends at seven sites were compared in order to empirically assess the accuracy of some fundamental model input parameters and assumptions. Most of the models developed for the study sites tended to overestimate plume migration distance, source persistence, and/or the time required for the benzene, toluene, ethyl benzene, and xylenes (BTEX) plumes to attenuate. Discrepancies between observed and predicted contaminant trends and plume behavior suggested that the influence of natural attenuation process may not have been accurately simulated. The conservatism of model simulations may be attributed to underestimation of natural source weathering rates, overestimation of the mass of contaminant present in the source area, and/or use of overly conservative first-order solute decay rates.

Benzene Derivatives↗

PubMed2006

A bioavailability model predicting the toxicity of nickel to rainbow trout (Oncorhynchus mykiss) and fathead minnow (Pimephales promelas) in synthetic and natural waters.

The effects of Ca, Mg and pH on the toxicity of Ni to juvenile rainbow trout (Oncorhynchus mykiss) were examined during 17-26-day exposures to Ni in 15 synthetic test solutions. Higher chemical activities of Ca2+, Mg2+ and H+ reduced Ni toxicity, as demonstrated by increased 17-day median lethal concentrations expressed as Ni2+ activity (17-d LC50(Ni2+)). A non-linear increase of the 17-d LC50(Ni2+) with increasing H+ suggested that the effect of pH could not be appropriately described by single-site competition between Ni(2+) and H+ for sensitive sites on the fish gill. Instead, a linear increase of pNi2+ (=-log 17-d LC50(Ni2+)) with increasing pH was observed with a slope of 0.32. This slope was used as the basis for modelling the effect of pH. The effects of Ca and Mg were modelled according to single-site competition with logK(CaBL)=logK(MgBL)=3.6, both assumed to be independent of pH. The effect of pH was superimposed on this competition effect and was also assumed to be independent of Ca and Mg concentrations. The model was able to predict 17-d LC50s (expressed as dissolved Ni) in most synthetic test waters within a factor 2 deviation from observed toxicity. The model's predictive capacity was also evaluated using results of similar laboratory toxicity tests with juvenile rainbow trout in Ni-spiked European natural surface waters. For most of these waters, predicted 17-d LC50s did not deviate more than a factor 2 from observed toxicity. The same model, calibrated to account for sensitivity differences between species, life stages and/or exposure durations, was able to accurately predict 96-h LC50s for larval and juvenile fathead minnow (Pimephales promelas) and juvenile rainbow trout, based on data taken from literature. Although the developed model seems very promising, the uncertainty around the role of alkalinity and the exact mechanisms by which Ca, Mg and pH modify Ni toxicity need to be further explored.

Age Factors↗

PubMed2005

Establishing a clinically meaningful predictive model of hematologic toxicity in nonmyeloablative targeted radiotherapy: practical aspects and limitations of red marrow dosimetry.

In either heavily pretreated or previously untreated patient populations, dosimetry holds the promise of playing an integral role in the physician's ability to adjust therapeutic activity prescriptions to limit excessive hematologic toxicity in individual patients. However, red marrow absorbed doses have not been highly predictive of hematopoietic toxicity. Although the accuracy of red marrow dose estimates is expected to improve as more patient-specific models are implemented, these model-calculated absorbed doses more than likely will have to be adjusted by parameters that adequately characterize bone marrow tolerance in the heavily pretreated patients most likely to receive nonmyeloablative radiolabeled antibody therapy. Models need to be established that consider not only absorbed dose but also parameters that are indicative of pretherapy bone marrow reserve and radiosensitivity so that a clinically meaningful predictive model of hematologic toxicity can be established.

Bone Marrow↗

PubMed2002

Analysis and predictive models of stormwater runoff volumes, loads, and pollutant concentrations from watersheds in the Twin Cities metropolitan area, Minnesota, USA.

Urban nonpoint source pollution is a significant contributor to water quality degradation. Watershed planners need to be able to estimate nonpoint source loads to lakes and streams if they are to plan effective management strategies. To meet this need for the twin cities metropolitan area, a large database of urban and suburban runoff data was compiled. Stormwater runoff loads and concentrations of 10 common constituents (six N and P forms, TSS, VSS, COD, Pb) were characterized, and effects of season and land use were analyzed. Relationships between runoff variables and storm and watershed characteristics were examined. The best regression equation to predict runoff volume for rain events was based on rainfall amount, drainage area, and percent impervious area (R2 = 0.78). Median event-mean concentrations (EMCs) tended to be higher in snowmelt runoff than in rainfall runoff, and significant seasonal differences were found in yields (kg/ha) and EMCs for most constituents. Simple correlations between explanatory variables and stormwater loads and EMCs were weak. Rainfall amount and intensity and drainage area were the most important variables in multiple linear regression models to predict event loads, but uncertainty was high in models developed with the pooled data set. The most accurate models for EMCs generally were found when sites were grouped according to common land use and size.

Forecasting↗

PubMed2002

Predictive model for pain recurrence after posterior fossa surgery for trigeminal neuralgia.

BACKGROUND: Surgical exploration of the posterior fossa is the definitive treatment for trigeminal neuralgia refractory to medication, but predictors of its success in effecting long-term pain relief have not been established. OBJECTIVE: To develop a model that allows stratification of patients' risk of postoperative recurrence of pain based on pretreatment factors. METHODS: We reviewed the records of 420 consecutive patients who underwent posterior fossa exploration by one of us (C.B.W.) for the treatment of idiopathic trigeminal neuralgia. The primary outcome measure was recurrence of trigeminal pain. The predictive value of preoperative and intraoperative factors was evaluated. Multivariate analysis revealed the statistically significant predictors of pain recurrence, permitting creation of a risk model for recurrence of pain. RESULTS: After surgery, trigeminal pain had lessened in 98% of patients and completely resolved in 87%. There were no perioperative deaths. After a mean follow-up of 56.3 months, 93% of patients reported significant pain improvement and 72% continued to have no pain. The estimated likelihood of pain recurrence at 8 years was 34%. Significant predictors of eventual recurrence of pain were age younger than 53 years at the time of surgery, symptoms lasting longer than 11(1/2) years, female sex, and pain on the left side in men. These factors were weighted and incorporated into a risk model that revealed 4-year pain-free survival of 89% +/- 4% for the low-risk group, 80% +/- 4% for the moderate-risk group, and 58% +/- 6% for the high-risk group (data are mean +/- SD). CONCLUSIONS: We developed a predictive model that stratifies the risk for eventual recurrence of pain after posterior fossa exploration for trigeminal neuralgia. This information may be useful in counseling patients regarding treatment.

Disease-Free Survival↗

PubMed2005

Effects of Bcl-2 levels on Fas signaling-induced caspase-3 activation: molecular genetic tests of computational model predictions.

Fas-induced apoptosis is a critical process for normal immune system development and function. Although many molecular components in the Fas signaling pathway have been identified, a systematic understanding of how they work together to determine network dynamics and apoptosis itself has remained elusive. To address this, we generated a computational model for interpreting and predicting effects of pathway component properties. The model integrates current information concerning the signaling network downstream of Fas activation, through both type I and type II pathways, until activation of caspase-3. Unknown parameter values in the model were estimated using experimental data obtained from human Jurkat T cells. To elucidate critical signaling network properties, we examined the effects of altering the level of Bcl-2 on the kinetics of caspase-3 activation, using both overexpression and knockdown in the model and experimentally. Overexpression was used to distinguish among alternative hypotheses for inhibitory binding interactions of Bcl-2 with various components in the mitochondrial pathway. In comparing model simulations with experimental results, we find the best agreement when Bcl-2 blocks the release of cytochrome c by binding to both Bax and truncated Bid instead of Bax, truncated Bid, or Bid alone. Moreover, although Bcl-2 overexpression strongly reduces caspase-3 activation, Bcl-2 knockdown has a negligible effect, demonstrating a general model finding that varying the expression levels of signal molecules frequently has asymmetric effects on the outcome. Finally, we demonstrate that the relative dominance of type I vs type II pathways can be switched by varying particular signaling component levels without changing network structure.

Apoptosis↗

PubMed1989

Model predicting survival in stage I melanoma based on tumor progression.

We used the lesional steps in tumor progression and multivariable logistic regression to develop a prognostic model for primary, clinical stage I cutaneous melanoma. This model is 89% accurate in predicting survival. Using histologic criteria, we assigned melanomas to tumor progression steps by ascertaining their particular growth phase. These phases were the in situ and invasive radial growth phase and the vertical growth phase (the focal formation of a dermal tumor nodule or dermal tumor plaque within the radial growth phase or such dermal growth without an evident radial growth phase). After a minimum follow-up of 100.6 months and a median follow-up of 150.2 months, 122 invasive radial-growth-phase tumors were found to be without metastases. Eight-year survival among the 264 patients whose tumors had entered the vertical growth phase was 71.2%. Survival prediction in these patients was enhanced by the use of a multivariable logistic regression model. Twenty-three attributes were tested for entry into this model. Six had independently predictive prognostic information: (a) mitotic rate per square millimeter, (b) tumor-infiltrating lymphocytes, (c) tumor thickness, (d) anatomic site of primary melanoma, (e) sex of the patient, and (f) histologic regression. When mitotic rate per square millimeter, tumor-infiltrating lymphocytes, primary site, sex, and histologic regression are added to a logistic regression model containing tumor thickness alone, they are independent predictors of 8-year survival (P less than .0005).

Female↗

PubMed2003

A multivariate biomarker-based model predicting population-level responses of Daphnia magna.

A multivariate model is proposed relating short-term biomarker measurements in Daphnia magna to chronic effects (21-d exposure) occurring at the population level (time to death, mean brood size, mean total young per female, intrinsic rate of natural increase, net reproductive rate, and growth). The results of the short-term exposure (48 h-96 h) to eight model toxicants (cadmium, chromium, mercury, tributyl tin, linear alkylsulfonic acid, sodium pentachlorophenolate, lindane, and 2,4-dichlorophenoxyacetic acid) on the following biomarkers were used for the multivariate model: digestive enzymes (amylase, cellulase, beta-galactosidase, trypsin, and esterase), enzymes of the intermediary metabolism (glycogen phosphorylase, glucose-6-phosphate dehydrogenase, pyruvate kinase, lactate dehydrogenase, and isocitrate dehydrogenase), cellular energy allocation (CEA) (protein, carbohydrate, and lipid content and electron transport activity), and DNA damage and antioxidative stress activity. Using partial least squares to latent structures (PLS), a two-component model was obtained with R2 of 0.68 and a Q2 value of 0.60 based on the combined analysis of a limited number of the 48- and 96-h biomarker responses. For the individual population-level responses, the R2 values varied from 0.66 to 0.77 and the Q2 values from 0.52 to 0.69. Energy-related biomarkers (cellular energy allocation, lipid contents, anaerobic metabolic activity--pyruvate kinase, and lactate dehydrogenase), combined with parameters related to oxidative stress (catalase) and DNA damage measured after 48 and 96 h of exposure, were able to predict long-term effects at higher levels of biological organization.

Animals↗

PubMed2005

Use of Geographic Information Systems in the development of prediction models for onchocerciasis control in Ethiopia.

A risk assessment model was developed for onchocerciasis distribution and its control in Ethiopia using Geographic Information System (GIS) methods. GIS data analysis was done to generate 3 separate risk models using selected environmental features of (1) earth observing satellite data on Normalized Difference Vegetation Index (NDVI) and midday Land Surface Temperature (LST) prepared from AVHRR sensor data of the Global land 1-km project for the years 1992 and 1995, (2) FAO agroclimatic databases from the Crop Production System Zone (CPSZ) of the Intergovernmental Authority on Development (IGAD) sub-region of East Africa, and (3) a climate-based forecast index based on the growing degree days (GDD) and water budget concepts. Parasitological data used for the analysis were published and unpublished reports of onchocerciasis surveillance made between 1969 and 2000 in various parts of the country. Analysis of queries based on 1992 and 1995 annual wet and dry season data of the Global land 1-km project resulted in a predictive value of 95.1%, 94.0% and 96.3%, respectively, using data values extracted from buffers centered on sites above 5% prevalence. The model based on CPSZ data predicted an endemic area that best fit the distribution of sites over 5% prevalence; the query was based on CPSZ values of average altitude (442-2134 m), annual mean temperature (18-28 degrees C), annual rainfall (822-1980 mm), annual potential evapotranspiration (1264-1938 mm), rain minus potential evapotranspiration (-124 - 792 mm), average NDVI x 100 (2000-5000) and average terrain percent slope (9-34). The climate-based model based on GDD and water-budget predicted high risk to severe risk areas in the western and southwestern parts of the country. All three of the models predicted suitable areas for the transmission of onchocerciasis outside known endemic areas, suggesting the need for ground-based validation and potential application to current community-directed treatment programs with ivermectin (CDTI) for control of onchocerciasis in Ethiopia.

Agriculture↗

PubMedDate not supplied

Protein catabolic rate in patients on continuous peritoneal dialysis. A multivariate predictive model.

Protein catabolic rate (PCR) and PCR normalized to standard weight (PCRN) are important indices of nutrition in patients on continuous peritoneal dialysis. The purpose of this study was to test whether urea clearance is among the predictors of PCR and PCRN in a multivariate analysis. Stepwise logistic regression was used to develop separate models for low PCR and low PCRN on a set of 143 urea kinetic studies in 92 patients on continuous peritoneal dialysis. The regression models were tested on an independent sample of 189 urea kinetic studies in 102 patients on continuous peritoneal dialysis by deriving the area under a receiver operating characteristic curve. In the derivation set, low serum urea, high serum creatinine, low urine and dialysate drain volumes, and low body surface area were identified as predictors of PCR < or = 50 g daily. The area under the receiver operating characteristic curve in the validation set was 0.930 (95% confidence interval: 0.915-0.945). Low serum urea, male gender, high body mass index and low urea fractional clearance (KT/V) were predictors of PCRN < or = 0.80 g/kg daily. The receiver operating characteristic area for this model was 0.948 (95% confidence interval: 0.926-0.970). Logistic regression analysis was repeated twice after adding urea nitrogen excretion normalized to standard weight (UNEN) as a candidate variable. This process identified low UNEN, male gender, and obesity as the predictors of low PCRN, and low UNEN, male gender, low urine volume, low drain volume normalized by body water, and high serum albumin as predictors of low KT/V urea. The authors conclude that PCR and PCRN can be predicted by models that incorporate serum azotemic indices, body size and composition, and direct or indirect measurements of urea clearance. Small body size and lean body composition predict low PCR but high PCRN values. Both PCRN and KT/V urea are predicted by UNEN. Multivariate analysis cannot, therefore, rule out the hypothesis that PCRN and KT/V are linked mathematically.

Body Weight↗

PubMed2001

Making the most of sparse clinical data by using a predictive-model-based analysis, illustrated with a stavudine pharmacokinetic study.

A small-scale clinical investigation was done to quantify the penetration of stavudine (D4T) into cerebrospinal fluid (CSF). A model-based analysis estimates the steady-state ratio of AUCs of CSF and plasma concentrations (R(AUC)) to be 0.270, and the mean residence time of drug in the CSF to be 7.04 h. The analysis illustrates the advantages of a causal (scientific, predictive) model-based approach to analysis over a noncausal (empirical, descriptive) approach when the data, as here, demonstrate certain problematic features commonly encountered in clinical data, namely (i) few subjects, (ii) sparse sampling, (iii) repeated measures, (iv) imbalance, and (v) individual design variation. These features generally require special attention in data analysis. The causal-model-based analysis deals with features (i) and (ii), both of which reduce efficiency, by combining data from different studies and adding subject-matter prior information. It deals with features (iii)--(v), all of which prevent 'averaging' individual data points directly, first, by adjusting in the model for interindividual data differences due to design differences, secondly, by explicitly differentiating between interpatient, interoccasion, and measurement error variation, and lastly, by defining a scientifically meaningful estimand (R(AUC)) that is independent of design.

Anti-HIV Agents↗

PubMed2003

Comparing zonal and CFD model predictions of isothermal indoor airflows to experimental data.

It is inappropriate to use the assumption of instantaneously well-mixed zones to model airflows and pollutant transport in large indoor spaces. We investigate two approaches for describing the details of airflows in large indoor spaces, for accuracy and suitability for integration with multi-zone infiltration models. One approach, called the zonal method, was developed over the last 15 years to provide an improvement over the well-mixed assumption. The second approach is the use of a computational fluid dynamics simulation using a coarse grid model of the large indoor space. We compare velocity predictions from different formulations of zonal methods and coarse-grid k-epsilon computational fluid dynamics (CFD) models, to measurements, in a 2D mechanically ventilated isothermal room. Our results suggest that, when airflow details are required, coarse-grid CFD is a better-suited method to predict airflows in large indoor spaces coupled with complex multi-zone buildings, than are the zonal methods. Based on the comparison of pressure predictions from different models, we offer guidance regarding the coupling of a model of detailed airflow in large spaces to algebraic multi-zone infiltration models.

Air Movements↗

PubMed1998

Predictive models for GABAA/benzodiazepine receptor subtypes: studies of quantitative structure-activity relationships for imidazobenzodiazepines at five recombinant GABAA/benzodiazepine receptor subtypes [alphaxbeta3gamma2 (x = 1-3, 5, and 6)] via comparative molecular field analysis.

Affinities of a series of substituted imidazobenzodiazepines at recombinant alpha1beta3gamma2, alpha2beta3gamma2, alpha3beta3gamma2, alpha5beta3gamma2, and alpha6beta3gamma2 GABAA/benzodiazepine receptor subtypes are reported. Many of these ligands displayed high affinities (low-nanomolar to subnanomolar scale) at all five receptor subtypes. Furthermore, a number of imidazobenzodiazepines exhibited relatively good selectivity at the alpha5-containing receptor isoform. For example, ligand 27 (RY-023) demonstrated a 55-fold higher selectivity at alpha5beta3gamma2 isoforms in comparison to other receptor subtypes. The affinity ratio of alpha1 (the most prevalent subtype in the brain) to alpha5 of this series of ligands ranged from 60- to 75-fold for the most selective ligands. Studies of quantitative structure-activity relationships (QSAR) by means of comparative molecular field analysis (CoMFA) were carried out. As a result, examination of CoMFA models for all five receptor subtypes demonstrated their predictability for affinities of imidazobenzodiazepines at the five receptor subtypes. Regions of molecular fields which would favor or disfavor the binding affinity of a ligand at a specific receptor subtype were examined via CoMFA for alpha1, alpha2, alpha3, alpha5, and alpha6 subtypes. A CoMFA regression analysis was applied to predict the ratio of Ki alpha1/Ki alpha5, an index for the selectivity of a ligand at the alpha5 subtype. All of the CoMFA models offered good cross-validated correlations for the ligands in the test set as well as the ratios of Ki alpha1/Ki alpha5, which demonstrated their potential for prediction.

Animals↗

PubMed1998

Presurgical finite element analysis from routine computed tomography studies for craniofacial distraction: II. An engineering prediction model for gradual correction of asymmetric skull deformities.

Finite element analysis from routine computed tomography studies (CT/FEA) allows clinicians to predict the mechanical and anatomic consequences of specific distraction systems before human application. A realistic three-dimensional CT/FEA engineering model of an actual plagiocephalic infant with unicoronal synostosis was developed using 4215 parabolic triangular shell elements and intracranial pressure conditions ranging from 10 to 20 mmHg. The completed finite element analysis model was used to predict the anatomic outcome of multiaxial distraction delivered by hypothetical patterns of rod and node distraction units. The predictions for the various patterns of distraction units were also compared quantitatively with respect to force, stress, strain, and intracranial volume. Best anatomic corrections were achieved with bilateral patterns of distraction units that simultaneously elongated the ipsilateral cranium and shortened the contralateral cranium. Greatest strain levels were experienced within the osteotomy callus, greatest stress levels at the appliance anchorage sites, and the greatest rod force at the ipsilateral lower coronal position.

Biomechanical Phenomena↗

PubMed2001

Artificial neural network-based predictive model for bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products.

The data of Devilieghere et al. (Int. J. Food Microbiol. 1999, 46, 57--70) on bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products was processed for estimating maximum specific growth rate mu(max) and lag phase lambda of Lactobacillus sake using artificial neural networks-based model (ANNM) computation. The comparison between ANNM and response surface methodology (RSM) model showed that the accuracy of ANNM prediction was higher than that of RSM. Two-dimensional and three-dimensional plots of the response surfaces revealed that the relationships of water activity a(w), temperature T, and dissolved CO(2) concentration with mu(max) and lambda were complicated, not just linear or second-order relations. Furthermore, it was possible to compute the sensitivity of the model outputs against each input parameter by using ANNM. The results showed that mu(max) was most sensitive to a(w), T, and dissolved CO(2) in this order; whereas lambda was sensitive to T the most, followed by a(w), and dissolved CO(2) concentrations.

Animals↗

PubMed2026

Species distribution models predict genome-wide polymorphism and gene flow in an endangered amphibian.

Species distribution models (SDMs) are widely used to predict habitat suitability but their usefulness and accuracy for inferring population health is still debated. Here, we evaluate whether SDM-derived relative habitat suitability (RHS) predicts genome-wide genetic diversity and connectivity-which are key proxies for population health and the functional integrity of landscapes. We addressed this issue in the Yellow-bellied toad (Bombina variegata), an endangered amphibian species with limited dispersal. We combined hierarchical SDMs, integrating both continental-level bioclimatic data and regional-level landscape variables, with genome-wide SNP data from 404 individuals sampled across 92 sites in southeastern France. We then used a multi-scale modelling framework to test the effect of bioclimatic (BRHS) and landscape (LRHS) habitat suitability on observed heterozygosity and pairwise genetic differentiation, accounting for heterogeneous genetic drift using gravity models. Our results show that both BRHS and LRHS are significant predictors of heterozygosity, with their effects expressed at different spatial scales-11 km and 3 km for BRHS and LRHS, respectively. Connectivity patterns also widely varied depending on scale and were best explained by gravity models integrating LRHS, BRHS, and local heterozygosity, underscoring the combined role of landscape resistance and population size in shaping patterns of genetic differentiation. These findings show that SDMs, when carefully calibrated and interpreted, can provide proxies for genetic diversity and landscape resistance in species with limited dispersal.

Journal Article↗