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At least 559 records · Page 31Linked to original sources
USGS2006

State-parameter estimation of ecosystem models using a smoothed ensemble Kalman filter

Much of the effort in data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be reduced if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) to estimate simultaneously the system states and model parameters of an eddy flux partition model. The approach is used to assimilate observed fluxes of carbon and major driving forces at an AmeriFlux forest station: Howland, Maine, USA. The aim of applying a kernel-smoothing algorithm to an ensemble Kalman filter is to overcome the dramatic, sudden change of parameter values in time and the loss of continuity between two consecutive points in time. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and so on, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the simultaneous parameter estimation procedure significantly improves model predictions. Results also show that the SEnKF can dramatically reduce variance in state variables stemming from the uncertainty of parameters and driving variables. The SEnKF is a robust and effective algorithm in evaluating and developing ecosystem models and in improving understanding and quantification of carbon cycle parameters and processes.

Conference Paper↗
USGS1996

Pattern recognition analysis and classification modeling of selenium-producing areas

Established chemometric and geochemical techniques were applied to water quality data from 23 National Irrigation Water Quality Program (NIWQP) study areas in the Western United States. These techniques were applied to the NIWQP data set to identify common geochemical processes responsible for mobilization of selenium and to develop a classification model that uses major-ion concentrations to identify areas that contain elevated selenium concentrations in water that could pose a hazard to water fowl. Pattern recognition modeling of the simple-salt data computed with the SNORM geochemical program indicate three principal components that explain 95% of the total variance. A three-dimensional plot of PC 1, 2 and 3 scores shows three distinct clusters that correspond to distinct hydrochemical facies denoted as facies 1, 2 and 3. Facies 1 samples are distinguished by water samples without the CaCO 3 simple salt and elevated concentrations of NaCl, CaSO 4 , MgSO 4 and Na 2 SO 4 simple salts relative to water samples in facies 2 and 3. Water samples in facies 2 are distinguished from facies 1 by the absence of the MgSO 4 simple salt and the presence of the CaCO 3 simple salt. Water samples in facies 3 are similar to samples in facies 2, with the absence of both MgSO 4 and CaSO 4 simple salts. Water samples in facies 1 have the largest selenium concentration (10 μg l −1 ), compared to a median concentration of 2·0 μg l −1 and less than 1·0 μg l −1 for samples in facies 2 and 3. A classification model using the soft independent modeling by class analogy (SIMCA) algorithm was constructed with data from the NIWQP study areas. The classification model was successful in identifying water samples with a selenium concentration that is hazardous to some species of water-fowl from a test data set comprised of 2,060 water samples from throughout Utah and Wyoming. Application of chemometric and geochemical techniques during data synthesis analysis of multivariate environmental databases from other national-scale environmental programs such as the NIWQP could also provide useful insights for addressing ‘real world’ environmental problems.

Journal of Chemometrics↗
USGS2014

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Massachusetts↗
USGS2014

Assessment of parametric uncertainty for groundwater reactive transport modeling,

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM (zs) ) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM (ZS) -based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Water Resources Research↗
USGS2015

A Bayesian kriging approach for blending satellite and ground precipitation observations

Drought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite-derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite-derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite-derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the “true” observed precipitation value at each grid cell.

Water Resources Research↗
USGS2016

Stratigraphy of the north polar layered deposits of Mars from high-resolution topography

The stratigraphy of the layered deposits of the polar regions of Mars is theorized to contain a record of recent climate change linked to insolation changes driven by variations in the planet's orbital and rotational parameters. In order to confidently link stratigraphic signals to insolation periodicities, a description of the stratigraphy is required based on quantities that directly relate to intrinsic properties of the layers. We use stereo Digital Terrain Models (DTMs) from the High Resolution Imaging Science Experiment (HiRISE) to derive a characteristic of North Polar Layered Deposits (NPLD) strata that can be correlated over large distances: the topographic protrusion of layers exposed in troughs, which is a proxy for the layers’ resistance to erosion. Using a combination of image analysis and a signal-matching algorithm to correlate continuous depth-protrusion signals taken from DTMs at different locations, we construct a stratigraphic column that describes the upper ~500 m of at least 7% of the area of the NPLD, and find accumulation rates that vary by factors of up to two. We find that, when coupled with observations of exposed layers in orbital images, the topographic expression of the strata is consistently continuous through large distances in the top 300 – 500 m of the NPLD, suggesting it is better related to intrinsic layer properties than brightness alone.

Journal of Geophysical Research E: Planets↗
USGS2017

Integrated geophysical characteristics of the 2015 Illapel, Chile, earthquake

On 16 September 2015, a Mw 8.3 earthquake ruptured the subduction zone offshore of Illapel, Chile, generating an aftershock sequence with 14 Mw 6.0–7.0 events. A double source W phase moment tensor inversion consists of a Mw 7.2 subevent and the main Mw 8.2 phase. We determine two slip models for the mainshock, one using teleseismic broadband waveforms and the other using static GPS and InSAR surface displacements, which indicate high slip north of the epicenter and west-northwest of the epicenter near the oceanic trench. These models and slip distributions published in other studies suggest spatial slip uncertainties of ~25 km and have peak slip values that vary by a factor of 2. We relocate aftershock hypocenters using a Bayesian multiple-event relocation algorithm, revealing a cluster of aftershocks under the Chilean coast associated with deep (20–45 km depth) mainshock slip. Less vigorous aftershock activity also occurred near the trench and along strike of the main aftershock region. Most aftershocks are thrust-faulting events, except for normal-faulting events near the trench. Coulomb failure stress change amplitudes and signs are uncertain for aftershocks collocated with deeper mainshock slip; other aftershocks are more clearly associated with loading from the mainshock. These observations reveal a frictionally heterogeneous interface that ruptured in patches at seismogenic depths (associated with many aftershocks) and with homogeneous slip (and few aftershocks) up to the trench. This event likely triggered seismicity separate from the main slip region, including along-strike events on the megathrust and intraplate extensional events.

Illapel↗
USGS2017

Integrating puffing and explosions in a general scheme for Strombolian-style activity

Strombolian eruptions are among the most common subaerial styles of explosive volcanism worldwide. Distinctive features of each volcano lead to a correspondingly wide range of variations of magnitude and erupted products, but most papers focus on a single type of event at a single volcano. Here, in order to emphasize the common features underlying this diversity of styles, we scrutinize a database from 35 different erupting vents, including 21 thermal infrared videos from Stromboli (Italy), Etna (Italy), Yasur (Vanuatu), and Batu Tara (Indonesia), from puffing, through rapid explosions to normal explosions, with variable ejection parameters and relative abundance of gas, ash, and bombs. Using field observations and high-speed thermal infrared videos processed by a new algorithm, we identify the distinguishing characteristics of each type of activity and how they may relate and interact. In particular, we record that ash-poor normal explosions may be preceded and followed by the onset or the increase of the puffing activity, while ash-rich explosions are emergent, i.e., with inflation of the free surface followed directly by emission of increasingly large gas pockets. Overall, we see that all Strombolian activities form a continuum arising from a common mechanism and are modulated by the combination of two well-established controls: (1) the length of the bursting gas pocket with respect to the vent diameter and (2) the presence and thickness of a high-viscosity layer in the uppermost part of the volcanic conduit.

Journal of Geophysical Research B: Solid Earth↗
USGS2017

Temporal variation of tectonic tremor activity in southern Taiwan around the 2010 ML6.4 Jiashian Earthquake

Deep tectonic tremor, which is extremely sensitive to small stress variations, could be used to monitor fault-zone processes during large earthquake cycles and aseismic processes before large earthquakes. In this study, we develop an algorithm for the automatic detection and location of tectonic tremor beneath the southern Central Range of Taiwan and examine the spatio-temporal relationship between tremor and the 4 March 2010 ML6.4 Jiashian earthquake, located about 20 km from active tremor sources. We find that tremor in this region has a relatively short duration, short recurrence time, and no consistent correlation with surface GPS data. We find a short-term increase in the tremor rate 19 days before the Jiashian mainshock, and around the time when the tremor rate began to rise, one GPS station recorded a flip in its direction of motion. We hypothesize that tremor is driven by a slow-slip event that preceded the occurrence of the shallower Jiashian mainshock, even though the inferred slip is too small to be observed by all GPS stations. Our study shows that tectonic tremor may reflect stress variation during the pre-nucleation process of a nearby earthquake.

Journal of Geophysical Research B: Solid Earth↗
USGS2016

Bounded fractional diffusion in geological media: Definition and Lagrangian approximation

Spatiotemporal Fractional-Derivative Models (FDMs) have been increasingly used to simulate non-Fickian diffusion, but methods have not been available to define boundary conditions for FDMs in bounded domains. This study defines boundary conditions and then develops a Lagrangian solver to approximate bounded, one-dimensional fractional diffusion. Both the zero-value and non-zero-value Dirichlet, Neumann, and mixed Robin boundary conditions are defined, where the sign of Riemann-Liouville fractional derivative (capturing non-zero-value spatial-nonlocal boundary conditions with directional super-diffusion) remains consistent with the sign of the fractional-diffusive flux term in the FDMs. New Lagrangian schemes are then proposed to track solute particles moving in bounded domains, where the solutions are checked against analytical or Eularian solutions available for simplified FDMs. Numerical experiments show that the particle-tracking algorithm for non-Fickian diffusion differs from Fickian diffusion in relocating the particle position around the reflective boundary, likely due to the non-local and non-symmetric fractional diffusion. For a non-zero-value Neumann or Robin boundary, a source cell with a reflective face can be applied to define the release rate of random-walking particles at the specified flux boundary. Mathematical definitions of physically meaningful nonlocal boundaries combined with bounded Lagrangian solvers in this study may provide the only viable techniques at present to quantify the impact of boundaries on anomalous diffusion, expanding the applicability of FDMs from infinite do mains to those with any size and boundary conditions.

Water Resources Research↗
USGS2018

Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America

Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here we construct such a budget for eastern North America using historical data, empirical models, remote sensing algorithms, and process‐based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO 2 uptake split evenly between tidal wetlands and shelf waters, and estuarine CO 2 outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they, respectively, make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters, and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling.

Global Biogeochemical Cycles↗
USGS2017

Methodology for time-domain estimation of storm time geoelectric fields using the 3-D magnetotelluric response tensors

Geoelectric fields at the Earth's surface caused by magnetic storms constitute a hazard to the operation of electric power grids and related infrastructure. The ability to estimate these geoelectric fields in close to real time and provide local predictions would better equip the industry to mitigate negative impacts on their operations. Here we report progress toward this goal: development of robust algorithms that convolve a magnetic storm time series with a frequency domain impedance for a realistic three-dimensional (3-D) Earth, to estimate the local, storm time geoelectric field. Both frequency domain and time domain approaches are presented and validated against storm time geoelectric field data measured in Japan. The methods are then compared in the context of a real-time application.

Space Weather↗
USGS2024

Crop type classification, trends, and patterns of central California agricultural fields from 2005 to 2020

California produces many key agricultural products in the United States. Current geospatial agricultural datasets are limited in mapping accuracy, spatial context, or observation period. This study uses machine learning and high-resolution imagery to produce a time series of crop maps to assess crop type trends and patterns across central California from 2005 to 2020. National Agriculture Imagery Program and Landsat imagery were used to classify nine crop types that are common in the study region: grain crops, field crops, rice, citrus and subtropical, deciduous fruit and nut, vineyard, berry and vegetable, pasture, and fallow/young perennial crop types. To create labeled data, we sampled 1253 fields and manually identified crop types for each examined year using high-resolution imagery and Landsat normalized difference vegetation index time series. We applied a random forest machine learning algorithm in Google Earth Engine. Results show that the mean overall classification accuracy of the nine-class map was 93.1%, with individual accuracies ranging from 99.3% (rice) to 89.5% (fallow/young perennial). Mann–Kendall trend tests showed significant ( p less than 0.05) declines in field crop and pasture area during the study period, while deciduous fruit and nut, citrus and subtropical, and fallow/young perennial crop types experienced significant increases. At an aggregate level, there was a general shift from annual crop types to perennial crop types. These data provide a 16-year time span of spatially explicit crop type classifications, trends, and patterns in central California that can be used to aid managers and decision makers for resource planning or hazard mitigation.

California↗
USGS2004

Extent of reaction in open systems with multiple heterogeneous reactions

The familiar batch concept of extent of reaction is reexamined for systems of reactions occurring in open systems. Because species concentrations change as a result of transport processes as well as reactions in open systems, the extent of reaction has been less useful in practice in these applications. It is shown that by defining the extent of the equivalent batch reaction and a second contribution to the extent of reaction due to the transport processes, it is possible to treat the description of the dynamics of flow through porous media accompanied by many chemical reactions in a uniform, concise manner. This approach tends to isolate the reaction terms among themselves and away from the model partial differential equations, thereby enabling treatment of large problems involving both equilibrium and kinetically controlled reactions. Implications on the number of coupled partial differential equations necessary to be solved and on numerical algorithms for solving such problems are discussed. Examples provided illustrate the theory applied to solute transport in groundwater flow.

American Institute of Chemical Engineers↗
USGS2023

Evaluation of spectral collection strategies for identification of Dalbergia spp. using handheld laser-induced breakdown spectroscopy

The illegal timber trade has significant impact on the survival of endangered tropical hardwood species like Dalbergia spp. (rosewood), a world-wide protected genus from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Due to increased threat to Dalbergia spp., and lack of action to reduce threats, port of entry analysis methods are required to identify Dalbergia spp. Handheld laser-induced breakdown spectroscopy (LIBS) has been shown to be capable of identifying species and establishing provenance of Dalbergia spp. and other tropical hardwoods, but analysis methods for this work have yet to be investigated in detail. The present work investigates five well-known algorithms—partial least squares discriminant analysis (PLS-DA), classification and regression trees (CART), k -nearest neighbor ( k -NN), random forest (RF), and support vector machine (SVM)—two training/test set sampling regimes, and data collection at two signal-to-noise (S/N) ratios to assess the potential for handheld LIBS analyses. Additionally, imbalanced classes are addressed. For this application, SVM and RF yield near identical results (though RF takes nearly 100 longer to compute), while the S/N ratio has a significant effect on model success assuming all else is equal. It was found that forming a training set with replicate low S/N analyses can perform as well as higher precision training sets for true prediction, even if the predicted samples have low signal to noise! This work confirms handheld LIBS analyzers can provide a viable method for classification of hardwood species, even within the same genus.

Journal of Chemometrics↗
USGS2017

Matching seed to site by climate similarity: techniques to prioritize plant materials development and use in restoration

Land management agencies are increasing the use of native plant materials for vegetation treatments to restore ecosystem function and maintain natural ecological integrity. This shift toward the use of natives has highlighted a need to increase the diversity of materials available. A key problem is agreeing on how many, and which, new accessions should be developed. Here we describe new methods that address this problem. Our methods use climate data to calculate a climate similarity index between two points in a defined extent. This index can be used to predict relative performance of available accessions at a target site. In addition, the index can be used in combination with standard cluster analysis algorithms to quantify and maximize climate coverage (mean climate similarity), given a modeled range extent and a specified number of accessions. We demonstrate the utility of this latter feature by applying it to the extents of 11 western North American species with proven or potential use in restoration. First, a species-specific seed transfer map can be readily generated for a species by predicting performance for accessions currently available; this map can be readily updated to accommodate new accessions. Next, the increase in climate coverage achieved by adding successive accessions can be explored, yielding information that managers can use to balance ecological and economic considerations in determining how many accessions to develop. This approach identifies sampling sites, referred to as climate centers, which contribute unique, complementary, climate coverage to accessions on hand, thus providing explicit sampling guidance for both germplasm preservation and research. We examine how these and other features of our approach add to existing methods used to guide plant materials development and use. Finally, we discuss how these new methods provide a framework that could be used to coordinate native plant materials development, evaluation, and use across agencies, regions, and research groups.

Ecological Applications↗
USGS2019

Spatial conservation planning under uncertainty: Adapting to climate change risks using modern portfolio theory

Climate change and urban growth impact habitats, species, and ecosystem services. To buffer against global change, an established adaptation strategy is designing protected areas to increase representation and complementarity of biodiversity features. Uncertainty regarding the scale and magnitude of landscape change complicates reserve planning and exposes decision makers to risk of failing to meet conservation goals. Conservation planning tends to treat risk as an absolute measure, ignoring the context of the management problem and risk preferences of stakeholders. Application to conservation of risk management theory emphasizes diversification of portfolio of assets, with the goal of reducing the impact of system volatility on investment return. We use principles of Modern Portfolio Theory (MPT), which quantifies risk as the variance and correlation among assets, to formalize diversification as an explicit strategy for managing risk in climate‐driven reserve design. We extend MPT to specify a framework that evaluates multiple conservation objectives, allows decision makers to balance management benefits and risk when preferences are contested or unknown, and includes additional decision options such as parcel divestment when evaluating candidate reserve designs. We apply an efficient search algorithm that optimizes portfolio design for large conservation problems and a game theoretic approach to evaluate portfolio tradeoffs that satisfy decision makers with divergent benefit and risk tolerances, or when a single decision maker cannot resolve their own preferences. Evaluating several risk profiles for a case study in South Carolina, our results suggest that a reserve design may be somewhat robust to differences in risk attitude but that budgets will likely be important determinants of conservation planning strategies, particularly when divestment is considered a viable alternative. We identify a possible fiscal threshold where adequate resources allow protecting a sufficiently diverse portfolio of habitats such that the risk of failing to achieve conservation objectives is considerably lower. For a range of sea‐level rise projections, conversion of habitat to open water (14‐180%) and wetland loss (1‐7%) are unable to be compensated under the current protected network. In contrast, optimal reserve design outcomes are predicted to ameliorate expected losses relative to current and future habitat protected under the existing conservation estate.

South Carolina↗
USGS2021

A Bayesian Dirichlet process community occupancy model to estimate community structure and species similarity

Community occupancy models estimate species‐specific parameters while sharing information across species by treating parameters as sampled from a common distribution. When communities consist of discrete groups, shrinkage of estimates towards the community mean can mask differences among groups. Infinite mixture models using a Dirichlet process (DP) distribution, in which the number of latent groups is estimated from the data, have been proposed as a solution. In addition to community structure, these models estimate species similarity, which allows testing hypotheses about whether traits drive species response to environmental conditions. We develop a community occupancy model (COM) using a DP distribution to model species‐level parameters. Because clustering algorithms are sensitive to dimensionality and distinctiveness of clusters, we conducted a simulation study to explore performance of the DP‐COM with different dimensions (i.e., different numbers of model parameters with species‐level DP random effects) and under varying cluster differences. Because the DP‐COM is computationally expensive, we compared its estimates to a COM with a normal random species effect. We further applied the DP‐COM model to a bird dataset from Uganda. Estimates of the number of clusters and species cluster identity improved with increasing difference among clusters and increasing dimensions of the DP; but the number of clusters was always overestimated. Estimates of number of sites occupied and species and community level covariate coefficients on occupancy probability were generally unbiased with (near‐) nominal 95% Bayesian Credible Interval coverage. Accuracy of estimates from the normal and the DP‐COM were similar. The DP‐COM clustered 166 bird species into 27 clusters regarding their affiliation with open or woodland habitat and distance to oil wells. Estimates of covariate coefficients were similar between a normal and the DP‐COM. Except sunbirds, species within a family were not more similar in their response to these covariates than the overall community. Given that estimates were consistent between the normal and the DP‐COM, and considering the computational burden for the DP models, we recommend using the DP‐COM only when the analysis focuses on community structure and species similarity, as these quantities can only be obtained under the DP‐COM.

Ecological Applications↗