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PubMed1999

A comparative model: reaction time performance in sleep-disordered breathing versus alcohol-impaired controls.

OBJECTIVES/HYPOTHESIS: Patients with sleep-disordered breathing have reaction time deficits that may lead to catastrophic accidents and loss of life. Although safety guidelines do not exist for unsafe levels of sleepiness, they have been established for unsafe levels of alcohol consumption. Since reaction time performance is altered in both, we prospectively used seven measures of reaction time performance as a comparative model in alcohol-challenged normal subjects with corresponding measures in subjects with sleep-disordered breathing. STUDY DESIGN: Institutional Review Board-approved, nonrandomized prospective controlled study. METHODS: Eighty healthy volunteers (29.1+/-7.5 y of age, 56.3% female subjects) performed four reaction time trials using a psychomotor test at baseline and at three subsequent rising alcohol-influenced time points. The same test without alcohol was given to 113 subjects (47.2+/-10.8 y of age, 19.3% female subjects) with mild to moderate sleep-disordered breathing. RESULTS: Mean blood alcohol concentrations (BACs) in the alcohol-influenced subjects at baseline and three trials were 0, 0.057, 0.080, and 0.083 g/dL. The sleep-disordered subjects had mean respiratory disturbance indices of 29.2 events per hour of sleep. On all seven reaction time measures, their performance was worse than that of the alcohol subjects when BACs were 0.057 g/dL. For three of the measures, the sleep-disordered subjects performed as poorly as or worse than the alcohol subjects when alcohol levels were 0.080 g/dL. These results could not be explained by sex or age differences. CONCLUSION: The data demonstrate that sleep-disordered subjects in this study (with a mean age of 47 y) with mild to moderate sleep-disordered breathing had worse test reaction time performance parameters than healthy, nonsleepy subjects (with a mean age of 29 y) whose BAC is illegally high for driving a commercial motor vehicle in California. This comparative model points out the potential risks of daytime sleepiness in those with sleep-disordered breathing relative to a culturally accepted standard of impairment.

Adult↗

PubMed2026

Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size.

OBJECTIVE: Radiogenomics aims to non-invasively predict tumour genotypes from imaging, but most studies assume molecular homogeneity by assigning a single biopsy-derived label to all lesions within a patient. This approach risks substantial label noise given well-documented interlesional heterogeneity. We investigated whether anchoring training to biopsy-confirmed lesions improves radiogenomic model performance and generalisability. MATERIALS AND METHODS: We retrospectively analysed 1646 patients (11473 segmented lesions) with contrast-enhanced CT and EGFR mutation status from next-generation sequencing at the Netherlands Cancer Institute, alongside an external NSCLC radiogenomics cohort (n = 158). All visible lesions were segmented, and the exact biopsy site was matched to its segmentation. Radiomic features were extracted, and machine learning models were trained with three lesion selection strategies: all lesions, non-biopsied lesions only, and biopsy-confirmed lesions only. To disentangle label quality from sample size, we created size-matched variants (one lesion per patient) for all-lesion and non-biopsied strategies. RESULTS: All models achieved significant discrimination of EGFR status on internal validation (AUC = 0.62-0.68). However, performance of the all-lesion and non-biopsied models declined on external validation (AUC = 0.55-0.63), while the biopsy-anchored model maintained stable performance (AUC = 0.62), despite having only 1/10th of the training sample size. When training sets were size-matched, the biopsy-anchored approach significantly outperformed a model trained on all available lesions on external validation (p = 0.037). CONCLUSIONS: Radiogenomic models trained on biopsy-confirmed lesions outperform conventional all-lesion strategies in external validation, despite using an order of magnitude fewer samples. Prioritising lesion-level label fidelity can mitigate heterogeneity-driven noise, enhancing robustness and clinical translation of imaging-based genomic prediction. KEY POINTS: Question Does assigning biopsy-derived molecular labels to all lesions introduce heterogeneity-driven label noise that reduces the generalisability of radiogenomic models? Findings Models trained exclusively on biopsy-confirmed lesions demonstrated superior external generalisability compared with all-lesion approaches, despite being trained on substantially fewer samples. Clinical relevance Biopsy-anchored radiogenomics improves the reliability of non-invasive mutation prediction by accounting for tumour heterogeneity, potentially supporting clinical decision-making when tissue sampling is limited or molecular results are discordant across lesions.

Humans↗

PubMed2005

Challenges in the transition to model-based development.

Practitioners of the art and science of pharmacometrics are well aware of the considerable effort required to successfully complete modeling and simulation activities for drug development programs. This is particularly true because of the current, ad hoc implementation wherein modeling and simulation activities are piggybacked onto traditional development programs. This effort, coupled with the failure to explicitly design development programs around modeling and simulation, will continue to be an important obstacle to the successful transition to model-based drug development. Challenges with timely data availability, high data discard rates, delays in completing modeling and simulation activities, and resistance of development teams to the use of modeling and simulation in decision making are all symptoms of an immature process capability for performing modeling and simulation. A process that will fulfill the promise of model-based development will require the development and deployment of three critical elements. The first is the infrastructure--the data definitions and assembly processes that will allow efficient pooling of data across trials and development programs. The second is the process itself--developing guidelines for deciding when and where modeling and simulation should be applied and the criteria for assessing performance and impact. The third element concerns the organization and culture--the establishment of truly integrated, multidisciplinary, and multiorganizational development teams trained in the use of modeling and simulation in decision-making. Creating these capabilities, infrastructure, and incentivizations are critical to realizing the full value of modeling and simulation in drug development.

Animals↗

PubMed1997

A model to describe growth patterns of the mammary gland during pregnancy and lactation.

Extensive proliferation and death of cells in the mammary gland occur during pregnancy and lactation. In this study, a mechanistic model was developed that yielded a single equation to describe the pattern of mammary growth of mammals throughout pregnancy and lactation. The model contains a single pool, which is the cell population of the mammary gland; one influx, representing cell proliferation; and one efflux, representing cell death. The parameters of the equation lend themselves to direct physiological interpretation. The model fitted data on mammary gland DNA adequately and can be related to current knowledge on factors and inhibitors of mammary gland growth. A unique definition of the parameters of the model can be difficult because of the high degree of variation among animals, an improper number of observations, or timing, as indicated by analyses of simulated data. The model can also be applied to the study of the entire lactation curve. The widely applied gamma equation and the equation that was developed in this study were compared using weekly production data from dairy cows. The new model performed well, particularly when a sharp peak in milk production occurred. The model has the advantage of providing, for the first time, a simple biological description of the lactation curve that can be used to discriminate changes in lactational performance that are associated with experimental treatments.

Animals↗

PubMed2006

Review article: scoring systems for assessing prognosis in critically ill adult cirrhotics.

BACKGROUND: Cirrhotic patients admitted to intensive care units (ICU) still have poor outcomes. Some current ICU prognostic models [Acute Physiology and Chronic Health Evaluation (APACHE), Organ System Failure (OSF) and Sequential Organ Failure Assessment (SOFA)] were used to stratify cirrhotics into risk categories, but few cirrhotics were included in the original model development. Liver-specific scores [Child-Turcotte-Pugh (CTP) and model for end-stage liver disease (MELD)] could be useful in this setting. AIM: To evaluate whether ICU prognostic models perform better compared with liver-disease specific ones in cirrhotics admitted to ICU. METHODS: We performed a structured literature review identifying clinical studies focusing on prognosis and risk factors for mortality in adult cirrhotics admitted to ICU. RESULTS: We found 21 studies (five solely dealing with gastrointestinal bleeding) published during the last 20 years (54-420 patients in each). APACHE II and III, SOFA and OSF had better discrimination for correctly predicting death compared with the CTP score. The MELD score was evaluated only in one study and had good predictive accuracy [receiver operator characteristic (ROC) curve: 0.81). Organ dysfunction models (OSF, SOFA) were superior compared with APACHE II and III (ROC curve: range 0.83-0.94 vs. 0.66-0.88 respectively). Cardiovascular, liver and renal system dysfunction were more frequently independently associated with mortality. CONCLUSIONS: General-ICU models had better performance in cirrhotic populations compared with CTP score; OSF and SOFA had the best predictive ability. Further prospective and validation studies are needed.

Critical Care↗

PubMed2006

Examining accuracy of screening mammography using an event order model.

Screening mammography is a widely used method for breast cancer detection. For each mammogram we propose a performance model based on order of outcomes. That is, we envision an initial assessment, a follow up assessment if the initial one is positive and, eventually, a determination of whether cancer was present or not. A model can be built at each stage reflecting effects due to patient characteristics, to the facility where mammogram was performed and to the radiologist reading the mammogram. Since assessment is not perfectly associated with outcome, familiar rates of agreement and disagreement are of interest. These rates can be investigated at various levels of risk factors of interest. The approach is illustrated with screening mammography data from the Group Health Cooperative in Seattle, WA. A Bayesian framework is adopted for inference and an analysis of the data set is presented.

Adult↗

PubMed2003

Spatial unmasking of nearby pure-tone targets in a simulated anechoic environment.

Detection thresholds were measured for different spatial configurations of 500- and 1000-Hz pure-tone targets and broadband maskers. Sources were simulated using individually measured head-related transfer functions (HRTFs) for source positions varying in both azimuth and distance. For the spatial configurations tested, thresholds ranged over 50 dB, primarily as a result of large changes in the target-to-masker ratio (TMR) with changes in target and masker locations. Intersubject differences in both HRTFs and in binaural sensitivity were large; however, the overall pattern of results was similar across subjects. As expected, detection thresholds were generally smaller when the target and masker were separated in azimuth than when they were at the same location. However, in some cases, azimuthal separation of target and masker yielded little change or even a small increase in detection threshold. Significant intersubject differences occurred as a result both of differences in monaural and binaural acoustic cues in the individualized HRTFs and of different binaural contributions to performance. Model predictions captured general trends in the pattern of spatial unmasking. However, subject-specific model predictions did not account for the observed individual differences in performance, even after taking into account individual differences in HRTF measurements and overall binaural sensitivity. These results suggest that individuals differ not only in their overall sensitivity to binaural cues, but also in how their binaural sensitivity varies with the spatial position of (and interaural differences in) the masker.

Acoustics↗

PubMed2000

A model of human performance on the traveling salesperson problem.

A computational model is proposed of how humans solve the traveling salesperson problem (TSP). Tests of the model are reported, using human performance measures from a variety of 10-, 20-, 40-, and 60-node problems, a single 48-node problem, and a single 100-node problem. The model provided a range of solutions that approximated the range of human solutions and conformed closely to quantitative and qualitative characteristics of human performance. The minimum path lengths of subjects and model deviated by average absolute values of 0.0%, 0.9%, 2.4%, 1.4%, 3.5%, and 0.02% for the 10-, 20-, 40-, 48-, 60-, and 100-node problems, respectively. Because the model produces a range of solutions, rather than a single solution, it may find better solutions than some conventional heuristic algorithms for solving TSPs, and comparative results are reported that support this suggestion.

Algorithms↗

PubMed1994

A test of the COPE model on motor performance and affect.

The effect of a model called COPE which suggests the use of cognitive-behavioral strategies in response to acute stress in sport was tested. Both rotary pursuit performance and changes in affect were similar for 33 subjects in groups who used the COPE model or used only a segment (one strategy) of the model. Both experimental groups performed better and experienced less negative affect after treatment than the control group.

Adaptation, Psychological↗

PubMed2000

Inter-study variability in population pharmacokinetic meta-analysis: when and how to estimate it?

Population pharmacokinetic analysis is being increasingly applied to individual data collected in different studies and pooled in a single database. However, individual pharmacokinetic parameters may change randomly from one study to another. In this article, we show by simulation that neglecting inter-study variability (ISV) does not introduce any bias for the fixed parameters or for the residual variability but may result in an overestimation of inter-individual (IIV) variability, depending on the magnitude of the ISV. Two random study-effect (RSE) estimation methods were investigated: (i) estimation, in a single step, of the three-nested random effects (inter-study, inter-individual and residual variability); (ii) estimation of residual variability and a mixture of ISV and IIV in the first step, then separation of ISV from IIV in the second. The one-stage RSE model performed well for population parameter assessment, whereas, the two-stage model yielded good estimates of IIV only with a rich sampling design. Finally, irrespective of the method used, ISV estimates were valid only when a large number of studies was pooled. The analysis of one real data set illustrated the use of an ISV model. It showed that the fixed parameter estimates were not modified, whether an RSE model was used or not, probably because of the homogeneity of the experimental designs of the studies, and suggest no study-effect in this example.

Computer Simulation↗

PubMed2004

A new measure of prognostic separation in survival data.

Multivariable prognostic models are widely used in cancer and other disease areas, and have a range of applications in clinical medicine, clinical trials and allocation of health services resources. A well-founded and reliable measure of the prognostic ability of a model would be valuable to help define the separation between patients or prognostic groups that the model could provide, and to act as a benchmark of model performance in a validation setting. We propose such a measure for models of survival data. Its motivation derives originally from the idea of separation between Kaplan-Meier curves. We define the criteria for a successful measure and discuss them with respect to our approach. Adjustments for 'optimism', the tendency for a model to predict better on the data on which it was derived than on new data, are suggested. We study the properties of the measure by simulation and by example in three substantial data sets. We believe that our new measure will prove useful as a tool to evaluate the separation available-with a prognostic model.

Brain Neoplasms↗

PubMed2003

MEAD (part II)-Predictions of radioactivity concentrations in the Irish Sea.

The predictions from MEAD, a model that simulates the transport of radionuclides in the marine environment, are presented for the Irish Sea. MEAD predictions for (137)Cs and Pu(alpha) are presented following discharges from BNFL Sellafield and the predictions compared to measured data from near the discharge location and further a field in the Irish Sea. The model performs well in most circumstances given the uncertainties involved in both modelling and data collection although some inconsistencies in the predictions are found. MEAD is also compared to other models of radionuclide transport in the Irish Sea.

Cesium Radioisotopes↗

PubMed2004

Effects of potential models on the adsorption of ethane and ethylene on graphitized thermal carbon black. Study of two-dimensional critical temperature and isosteric heat versus loading.

Adsorption of ethylene and ethane on graphitized thermal carbon black and in slit pores whose walls are composed of graphene layers is studied in detail to investigate the packing efficiency, the two-dimensional critical temperature, and the variation of the isosteric heat of adsorption with loading and temperature. Here we used a Monte Carlo simulation method with a grand canonical Monte Carlo ensemble. A number of two-center Lennard-Jones (LJ) potential models are investigated to study the impact of the choice of potential models in the description of adsorption behavior. We chose two 2C-LJ potential models in our investigation of the (i) UA-TraPPE-LJ model of Martin and Siepmann for ethane and Wick et al. for ethylene and (ii) AUA4-LJ model of Ungerer et al. for ethane and Bourasseau et al. for ethylene. These models are used to study the adsorption of ethane and ethylene on graphitized thermal carbon black. It is found that the solid-fluid binary interaction parameter is a function of adsorbate and temperature, and the adsorption isotherms and heat of adsorption are well described by both the UA-TraPPE and AUA models, although the UA-TraPPE model performs slightly better. However, the local distributions predicted by these two models are slightly different. These two models are used to explore the two-dimensional condensation for the graphitized thermal carbon black, and these values are 110 K for ethylene and 120 K for ethane.

Adsorption↗

PubMed2004

Daily mortality and air pollution in Atlanta: two years of data from ARIES.

Associations between daily mortality and air pollution were investigated in Fulton and DeKalb Counties, Georgia, for the 2-yr period beginning in August 1998, as part of the Aerosol Research and Inhalation Epidemiological Study (ARIES). Mortality data were obtained directly from county offices of vital records. Air quality data were obtained from a dedicated research site in central Atlanta; 15 separate air quality indicators (AQIs) were selected from the 70 particulate and gaseous air quality parameters archived in the ARIES ambient air quality database. Daily meteorological parameters, comprising 24-h average temperatures and dewpoints, were obtained from Atlanta's Hartsfield International Airport. Effects were estimated using Poisson regression with daily deaths as the response variable and time, meteorology, AQI, and days of the week as predictor variables. AQI variables entered the model in a linear fashion, while all other continuous predictor variables were smoothed via natural cubic splines using the generalized linear model (GLM) framework in S-PLUS. Knots were spaced either quarterly, monthly, or biweekly for temporal smoothing. A default model using monthly knots and AQIs averaged for lags 0 and 1 was postulated, with other models considered in sensitivity analyses. Lags up to 5 days were considered, and multipollutant models were evaluated, taking care to avoid overlapping (and thus collinear) AQIs. For this reason, PM(2.5) was partitioned into its three major constituents: SO(2-)(4), carbon (EC + 1.4 OC), and the remainder; sulfate was assumed to be (NH(4))(2)SO(4) for this purpose. Initial AQI screening was based on all-cause (ICD-9 codes <800) mortality for those aged 65 and over. For the (apparently) most important pollutants--PM(2.5) and its 3 major constituents, coarse PM mass [CM], 1-h maximum CO, 8-h maximum O(3)--we investigated 15 mortality categories in detail. (The 15 categories result from three age groups [all ages, <65, 65+] and five cause-of-death groups [all disease causes, cardiovascular, respiratory, cancer, and other "remainder" disease causes]). The GLM model outputs that were considered included mean AQI effects and their standard errors, and two indicators of relative model performance (deviance and deviance adjusted for the number of observations and model parameters). The latter indicator was considered to account for variations in the number of observations created by varying amounts of missing AQI data, which were not imputed. The single-AQI screening regressions on all-cause 65+ mortality show that CO, NO(2), PM(2.5), CM, SO(2), and O(3), followed by EC and OC, consistently have the best model fits, after adjusting for the number of observations. Their relative rankings, however, vary according to the smoothing knots used, and there is no correspondence between mean AQI effect and overall model fit.(Other regression runs often show that the best model fits are obtained with no AQI in the model.) There is no correspondence between mean AQI effect and statistical significance or between mean effect and serial correlation. There is a highly significant (.001 level) relationship between overall model fit and serial correlation; the best fitting models have the most frequent knot spacing and the most negative serial correlation. The regression analyses by cause of death find elderly circulatory deaths to be consistently associated with CO for all models.

Adult↗

PubMed1994

The dosimetric verification of a pencil beam based treatment planning system.

A new three-dimensional treatment planning system (TPS) based on convolution/superposition algorithms (TMS-Radix from HELAX AB, Uppsala, Sweden) was recently installed at the University Hospital in Lund. The purpose of the present study was to design a quality assurance and acceptance testing programme to meet the specific characteristics of this convolution model. The model is based on parametrization of a non-measurable quantity-the polyenergetic pencil beam. However, the verification of the treatment planning model is still dependent on numerous comparisons of measured depth-doses and dose profiles. The test programme was divided in two basic parts: (i) model implementation and beam data consistency and (ii) model performance and limitations in special situations. The first part was scheduled for all photon beam qualities available before they could be used for clinical treatment planning. The second part was performed for selected energies only. The results indicate clearly that the model is well suited for clinical three-dimensional dose planning and that the TPS handles data as expected. For example, calculated depth-doses for open and wedge beams at depths larger than the depth of dose maximum and profiles for open beams shows a very good agreement with measurements. However, depth-dose deviations at shallow depths, especially for high energies, were found. Monitor units calculated by the system were accurate for most fields except for very large fields, where deviations of several per cent were found.

Algorithms↗

PubMed1998

Parameterization and testing of a coupled photosynthesis-stomatal conductance model for boreal trees.

A coupled photosynthesis-stomatal conductance model was parameterized and tested with branches of black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.) trees growing in the Northern Study Area of the Boreal Ecosystem-Atmosphere Study (BOREAS) in Manitoba, Canada. Branch samples containing foliage of all age-classes were harvested from a lowland old black spruce (OBS) and an old jack pine (OJP) stand and the responses of photosynthesis (A(n)) and stomatal conductance (g(s)) to temperature, CO(2), light, and leaf-to-air vapor pressure difference (VPD) were determined under controlled laboratory conditions at the beginning, middle, and end of the growing season (Intensive Field Campaigns (IFC) 1, 2, and 3, respectively). The parameterized model was then tested against in situ field gas-exchange measurements in a young jack pine (YJP) and an upland black spruce (UBS) stand as well as in the OBS and OJP stands. Parameterization showed that Rubisco capacity (V(max)), apparent quantum yield (alpha') and Q(10) for sink limitation were the most crucial parameters for the photosynthesis sub-model and that V(max) varied among different measurement series in the laboratory. Verification of the model against the data used to parameterize it yielded correlation coefficients (r) of 0.97 and 0.93 for black spruce and jack pine, respectively, when IFC-specific parameters were used, and 0.77 and 0.87 when IFC-2 parameters were applied to all IFCs. For both measured and modeled g(s), the stomatal conductance sub-model, which linearly relates g(s) to (A(n)h(s))/c(s) (where h(s) and c(s) are relative humidity and CO(2) mole fraction at the leaf surface, respectively), had significantly steeper slopes and higher r values when only the VPD response data were used for parameterization than when all of the response data were used for parameterization. Testing the photosynthesis sub-model against upper canopy field data yielded poor results when laboratory estimates of V(max) were used. Use of the mean V(max) estimated for all upper canopy branches measured on a given day improved model performance for jack pine (from a nonsignificant correlation between measured and modeled A(n) to r = 0.45), but not for black spruce (r = 0.45 for both cases). However, when V(max) was estimated for each branch sample individually, the model accurately predicted the 23 to 137% diurnal variation in A(n) for all stands for both the upper and lower canopy. This was true both when all of the other parameters were IFC-specific (r = 0.93 and 0.92 for black spruce and jack pine, respectively) and when only mid-growing season (IFC-2) values were used (r = 0.92 for both species). Branch-specific V(max) estimates also permitted accurate prediction of field g(s) (r = 0.75 and 0.89 for black spruce and jack pine, respectively), although parameterization with all of the response data overestimated g(s) in the field, whereas parameterization with only the VPD response data provided unbiased predictions. Thus, after parameterization with the laboratory data, accurately modeling the range of A(n) and g(s) encountered in the field for both black spruce and jack pine was reduced to a single unknown parameter, V(max).

Journal Article↗

PubMed2003

Tooth retention as an indicator of quality dental care: development of a risk adjustment model.

BACKGROUND: Retaining teeth improves oral health and quality of life. Thus, receipt of a root canal (vs. a tooth extraction) is a useful indicator of the quality of dental care. However, use of this quality measure without adjusting for the severity of oral disease could lead to spurious conclusions. OBJECTIVES: This paper describes the development of a dental severity adjustment methodology. RESEARCH DESIGN: Retrospective study. SUBJECTS: 54,423 users of Department of Veterans Affairs (VA) dental care who had either root canal therapy or a tooth extraction at a VA facility in Fiscal year 1998. MEASURES: International Classification of Disease Clinical Modification codes for dental diagnoses and comorbid medical conditions. We modeled the effects of dental disease severity in logistic regression models of the probability of receiving a root canal, using both conceptual and Modified Delphi-Panel derived models, adjusting for age, and medical comorbidities. RESULTS: Conceptual and Modified Delphi models performed similarly. The dental disease severity adjustments increased the fit in models of the probability of receiving a root canal (C-statistic = 0.822 for the conceptual model and 0.804 for the Modified Delphi Panel model) compared with the model including comorbid medical conditions alone (C-statistic = 0.561). CONCLUSIONS: Risk adjustment for dental disease severity improves the fit of models of the probability of receiving a root canal. Studies of the quality of dental care should consider employing risk-adjusted models.

Alcoholism↗

PubMed2006

Abundance, spatial variance and occupancy: arthropod species distribution in the Azores.

1. The positive abundance-occupancy and abundance-variance relationships are two of the most widely documented patterns in population and community ecology. 2. Recently, a general model has been proposed linking the mean abundance, the spatial variance in abundance, and the occupancy of species. A striking feature of this model is that it consists explicitly of the three variables abundance, variance and occupancy, and no extra parameters are involved. However, little is known about how well the model performs. 3. Here, we show that the abundance-variance-occupancy model fits extremely well to data on the abundance, variance and occupancy of a large number of arthropod species in natural forest patches in the Azores, at three spatial extents, and distinguishing between species of different colonization status. Indeed, virtually all variation about the bivariate abundance-occupancy and abundance-variance relationships is effectively explained by the third missing variable (variance in abundance in the case of the abundance-occupancy relationship, and occupancy in the case of the abundance-variance relationship). 4. Introduced species tend to exhibit lower densities, less spatial variance in these densities, and occupy fewer sites than native and endemic species. None the less, they all lie on the same bivariate abundance-occupancy and abundance-variance, and trivariate abundance-variance-occupancy, relationships. 5. Density, spatial variance in density, and occupancy appear to be all the things one needs to know to describe much of the spatial distribution of species.

Animals↗