Assessment and prediction of the impacts of the Ok Tedi copper mine on fish catches in the Fly River system,Papua New Guinea

The Ok Tedi copper mine discharges overburden and ore residues into the Ok Tedi, a tributary of the Fly River. These discharges result in elevated suspended solids and dissolved and particulate associated copper. Analyses of covariance were performed to establish statistical model of the relationships between the mine discharges and fish catches between 1983 and 1988. These models were then extrapolated to predict the affects of future mine discharges on fish catches. The models predicted that if the observed effects were caused by particulate associated copper, the period of greatest impact will be between 1989 and 1993, following which catches should be close to 1988 levels for the remainder of mine life. Some additional catches not included in the data set used to derive the models were found to fit the model predictions well. As the predicted period of greatest impact is short and most species reproduce away from the river channel, the ability of the fish communities to undergo partial recovery after 1991 should be maintained.     

Evaluating a Canadian regional air quality model using ground-based observations in north-eastern Canada and United States

The simulated concentrations from a numerical 3-dimensional regional air quality model (MC2AQ) are compared to those of ground-based observations in north-eastern Canada and the United States. The model has oxidant chemistry for both inorganic and organic species and deposition routines driven online by a mesoscale compressible community meteorological model (MC2). A standard emission inventory of anthropogenic, natural and biogenic sources for the year 1990 for 21 atmospheric trace species was used in the simulation. The model was run for July 1999, because of the occurrence of a high ozone episode and the availability of the monitoring data for surface O3, SO2, NO, NO2 and NOx. The comparisons during the episode show that the model performs quite well for predicting concentrations and diurnal variations of the surface ozone. The predictions for other gaseous species show some discrepancies with observations, but they are consistent with the results from other models evaluated in the literature. The uncertainties in the emission inventory for these species might be the main causes of the discrepancies. Further studies are needed to improve the predictability of SO and NOx, especially as the model is developed to include particulate matter formation as a result of these gaseous precursors.     

Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies

Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial–temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008–2010). Results show that the developed models performed well for geosmin (R ²?=?0.83 for all training data and R ²?=?0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R ²?=?0.82 for all training data and R ²?=?0.70 for 7 out of 10 data points in the validation dataset).     

Monitoring fish communities in wadeable lowland streams: comparing the efficiency of electrofishing methods at contrasting fish assemblages

Electrofishing is considered a reliable tool to assess the assemblages and biodiversity of fish in wadeable streams. The most widely used electrofishing techniques (point [P], single-pass [S-P], and multiple-pass [M-P]) vary as to the effort needed for sample collection, and this may potentially influence the degree of accuracy. Moreover, little is known about the comparability of the methods and their specific performance in streams with different fish assemblages. The aim of this investigation was to validate (using M-P sampling as reference) the use of P and S-P electrofishing techniques to accurately assess the richness, density and size distribution of fishes in small streams at both regional and global scale independently of fish assemblages and geographical region. We sampled 50-m-long reaches in a total of 33 lowland stream reaches that were located in different climatic and biogeographical regions (Uruguay and Denmark) and hosted different fish assemblages. Subtropical fish communities exhibited higher richness (Uy: 12–32, Dk: 1–9) and densities (Uy: 1.3–5.2, DK: 0.1–4.9 in. m^?2) than temperate streams. We applied both "global models" using the entire database (33 sites) and "local models" including the same number of sites but using the climatic region as a model variable. Regression analyses revealed that the P, S-P and M-P methods all provided an adequate picture of the species composition and size distribution, and transfer equations for comparison between methods are thus not required. Conversely, richness was better predicted by S-P and by P techniques for regional and global models, respectively. Transfer equations obtained for abundance revealed that the P and S-P models can accurately transform catch data into M-P estimations. The transfer equations provided here may have great relevance as they allow relatively reliable comparisons to be made between data obtained by different techniques. We also show that less intensive sampling techniques may be equally useful for monitoring purposes as those requiring more intensive efforts (and costs). We encourage validation of our developed transfer equations on data from other regions of the world.     

Laboratory-scale chlorination to estimate the levels of halogenated DBPs in full-scale distribution systems

Occurrence of halogenated disinfection by-products (DBPs) (trihalomethanes –THMs– and haloacetic acids –HAAs–) in the waters of two utilities in Quebec City (Canada) was investigated using two approaches: experimental chlorination studies and full-scale sampling within distribution systems. Experimental studies were designed to reproduce treatment plant and distribution system conditions (chlorine dose, water temperature, pH and water residence time). Differences in halogenated DBPs in the two distribution systems under study were significant and comparable to those observed in experimental laboratory studies. For the waters of both utilities, chlorination studies better reproduced the occurrence of halogenated DBPs in points of the distribution system located near the treatment plant (low residence time of water) than in other points. Multivariate regression models for THMs, HAAs and their species were developed using the data from experimental studies in order to predict halogenated DBP levels measured in the distribution system. Models were all statistically significant, but showed low ability to predict full-scale halogenated DBPs, particularly in points located at distribution system extremities. Specifically, experimental chlorination-based models are not able to simulate the decrease of HAA levels. Results of this research suggest that the use of experimental data to predict halogenated DBP levels in full-scale distribution systems – for operational, regulatory and epidemiological purposes – must be done with caution.     

Hierarchical Correlates of Bird Assemblage Structure on Northeastern U.S.A. Lakes

We investigated the factorsstructuring lake shore bird assemblages of thenortheastern U.S.A. using data collected from 158 lakesbetween 1992 and 1994. The assemblage data wereaggregated and standardized to produce assemblagecompositions consisting of proportions of individualsemploying different foraging, dietary, and migratorystrategies. The assemblage data were then re-expressedusing compositional analysis techniques and subjectedto regression tree analysis to identify environmentalcorrelates over a range of scales. Regionally, humandensity in the watershed was the most importantpredictor for the foraging, dietary, and migratorycompositions. A combination of anthropogenic andnon-anthropogenic factors likely contributed to thesebroad-scale associations because land use was largelyconfounded with climate and geomorphology. Morelocally, associations with lake shoreresidential-urban development were identified as beingimportant for the foraging and dietary compositions,as were associations with lake shore wetlands, butonly contingent there being little human developmentpresent locally and regionally. Assemblages wereassociated with increasing local and regional humandevelopment such that: hawking, aerial foraging, andground gleaning individuals increased relative tohover-and-gleaners, foliage gleaners, and barkgleaners; omnivores increased relative toinsectivores; and residents increased relative toneotropical migrants. The observed changes in birdcommunity structure were consistent with declines inforest interior species relative to edge species inresponse to forest fragmentation and suggest thatanthropogenic factors have played a prominent role instructuring lake shore bird assemblages of this region.     

Assessing the influence of nutrient reduction on water quality using a three-dimensional model: case study in a tidal estuarine system

A coupled three-dimensional hydrodynamic and water quality model has been developed and applied to the Danshuei River estuarine system and adjacent coastal sea. The water quality model considers various species of nitrogen, phosphorus, organic carbon, and phytoplankton as well as dissolved oxygen and is driven by a three-dimensional hydrodynamic model. The hydrodynamic and water quality models were validated with observations of water surface elevation, velocity, salinity distribution, and water quality parameters. Statistical error analysis shows that predictions of hydrodynamics, salinity, dissolved oxygen, and nutrients from the model simulation quantitatively agreed with the observed data. The validated model was then applied to predict water quality conditions as a result of a reduction in nutrient loadings based on different engineering strategies. The simulated results revealed that the dissolved oxygen concentration would increase significantly and would be higher than 2 mg/L in the main stream and in three tributaries to meet the minimum statutory requirement for dissolved oxygen. Active estuarine management focused on the reduction of anthropogenic nutrient loads is needed for improvement in water quality.     

Using species spectra to evaluate plant community conservation value along a gradient of anthropogenic disturbance

The aim of this study was to assess the impact of anthropogenic disturbance on the partitioning of plant communities (species spectra) across a landcover gradient of community types, categorizing species on the basis of their biogeographic, ecological, and conservation status. We tested a multinomial model to generate species spectra and monitor changes in plant assemblages as anthropogenic disturbance rise, as well as the usefulness of this method to assess the conservation value of a given community. Herbaceous and arborescent communities were sampled in five Azorean islands. Margins were also sampled to account for edge effects. Different multinomial models were applied to a data set of 348 plant species accounting for differences in parameter estimates among communities and/or islands. Different levels of anthropogenic disturbance produced measurable changes on species spectra. Introduced species proliferated and indigenous species declined, as anthropogenic disturbance and management intensity increased. Species assemblages of relevance other than economic (i.e., native, endemic, threatened species) were enclosed not only in natural habitats, but also in human managed arborescent habitats, which can positively contribute for the preservation of indigenous species outside remnants of natural areas, depending on management strategies. A significant presence of invasive species in margin transects of most community types will contribute to an increase in edge effect that might facilitate invasion. The multinomial model developed in this study was found to be a novel and expedient tool to characterize the species spectra at a given community and its use could be extrapolated for other assemblages or organisms, in order to evaluate and forecast the conservation value of a site.     

Accuracy of bottled drinking water label content

The accurate predictions of ground ozone concentrations are required for proper management, control, and making public warning strategies. Due to the difficulties in handling phenomenological models that are based on complex chemical reactions of ozone production, neural network models gained popularity in the last decade. These models also have some limitations due to problems of overfitting, local minima, and tuning of network parameters. In this study, the predictions of daily maximum ozone concentrations are attempted using support vector machines (SVMs). The comparison between the accuracy of SVM and neural network predictions is performed to evaluate their performance. For this, the daily maximum ozone concentration data observed during 2002–2004 at a site in Delhi is utilized. The models are developed using the available meteorological parameters. The results indicated the promising performance of SVM over neural networks in predicting daily maximum ozone concentrations.     

Patterns of Spatial Autocorrelation in Stream Water Chemistry

Geostatistical models are typically based on symmetric straight-line distance, which fails to represent the spatial configuration, connectivity, directionality, and relative position of sites in a stream network. Freshwater ecologists have explored spatial patterns in stream networks using hydrologic distance measures and new geostatistical methodologies have recently been developed that enable directional hydrologic distance measures to be considered. The purpose of this study was to quantify patterns of spatial correlation in stream water chemistry using three distance measures: straight-line distance, symmetric hydrologic distance, and weighted asymmetric hydrologic distance. We used a dataset collected in Maryland, USA to develop both general linear models and geostatistical models (based on the three distance measures) for acid neutralizing capacity, conductivity, pH, nitrate, sulfate, temperature, dissolved oxygen, and dissolved organic carbon. The spatial AICC methodology allowed us to fit the autocorrelation and covariate parameters simultaneously and to select the model with the most support in the data. We used the universal kriging algorithm to generate geostatistical model predictions. We found that spatial correlation exists in stream chemistry data at a relatively coarse scale and that geostatistical models consistently improved the accuracy of model predictions. More than one distance measure performed well for most chemical response variables, but straight-line distance appears to be the most suitable distance measure for regional geostatistical modeling. It may be necessary to develop new survey designs that more fully capture spatial correlation at a variety of scales to improve the use of weighted asymmetric hydrologic distance measures in regional geostatistical models.     

Human impact on wild firewood species in the Rural Andes community of Apillapampa, Bolivia

Firewood is the basic fuel source in rural Bolivia. A study was conducted in an Andean village of subsistence farmers to investigate human impact on wild firewood species. A total of 114 different fuel species was inventoried during fieldtrips and transect sampling. Specific data on abundance and growth height of wild firewood species were collected in thirty-six transects of 50 ×2 m². Information on fuel uses of plants was obtained from 13 local Quechua key participants. To appraise the impact of fuel harvest, the extraction impact value (EIV) index was developed. This index takes into account local participants?? appreciation of (1) decreasing plant abundance; (2) regeneration capacity of plants; (3) impact of root harvesting; and (4) quality of firewood. Results suggest that several (sub-)woody plant species are negatively affected by firewood harvesting. We found that anthropogenic pressure, expressed as EIV, covaried with density of firewood species, which could entail higher human pressure on more abundant and/or more accessible species. The apparent negative impact of anthropogenic pressure on populations of wild fuel species is corroborated by our finding that, in addition to altitude, several anthropogenic variables (i.e. site accessibility, cultivation of exotics and burning practices) explain part of the variation in height of firewood species in the surroundings of Apillapampa.     

Predicting Water Quality Impaired Stream Segments using Landscape-Scale Data and a Regional Geostatistical Model: A Case Study in Maryland

In the United States, probability-based water quality surveys are typically used to meet the requirements of Section 305(b) of the Clean Water Act. The survey design allows an inference to be generated concerning regional stream condition, but it cannot be used to identify water quality impaired stream segments. Therefore, a rapid and cost-efficient method is needed to locate potentially impaired stream segments throughout large areas. We fit a set of geostatistical models to 312 samples of dissolved organic carbon (DOC) collected in 1996 for the Maryland Biological Stream Survey using coarse-scale watershed characteristics. The models were developed using two distance measures, straight-line distance (SLD) and weighted asymmetric hydrologic distance (WAHD). We used the Corrected Spatial Akaike Information Criterion and the mean square prediction error to compare models. The SLD models predicted more variability in DOC than models based on WAHD for every autocovariance model except the spherical model. The SLD model based on the Mariah autocovariance model showed the best fit (r² = 0.72). DOC demonstrated a positive relationship with the watershed attributes percent water, percent wetlands, and mean minimum temperature, but was negatively correlated to percent felsic rock type. We used universal kriging to generate predictions and prediction variances for 3083 stream segments throughout Maryland. The model predicted that 90.2% of stream kilometers had DOC values less than 5 mg/l, 6.7% were between 5 and 8 mg/l, and 3.1% of streams produced values greater than 8 mg/l. The geostatistical model generated more accurate DOC predictions than previous models, but did not fit the data equally well throughout the state. Consequently, it may be necessary to develop more than one geostatistical model to predict stream DOC throughout Maryland. Our methodology is an improvement over previous methods because additional field sampling is not necessary, inferences about regional stream condition can be made, and it can be used to locate potentially impaired stream segments. Further, the model results can be displayed visually, which allows results to be presented to a wide variety of audiences easily.     

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.     

Ecohydrological impact–assessment modeling: an example for terrestrial ecosystems in Noord-Holland, the Netherlands

In the Netherlands the increasing demand for ecohydrological models on a regional (provincial) scale has resulted in the development of an empirical statistical impact–assessment model for semi-natural terrestrial herbaceous ecosystems. This model, called ITORS, describes the relationship between plant species and site factors such as soil, groundwater and management. The model, developed for the region of Noord-Holland, is applied to support provincial policy on soil and water management. The empirical statistical approach required the collection of a large set of field data, describing the biotic and environmental variation of a variety of semi-natural terrestrial ecosystems in Noord-Holland. These data were used to calculate species response models with help of logistic regression, resulting in significant models for 130 out of 144 species. Significant species response models were incorporated in the computer program ITORS. With help of ITORS the effect of various management scenarios can be easily evaluated, which is demonstrated by an example. This revised version was published online in July 2006 with corrections to the Cover Date.     

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.     

Regional impact analysis of climate change on natural and managed forests in the Federal State of Brandenburg, Germany

A methodology for regional application of forest simulation models has been developed as part of an assessment of possible climate change impacts in the Federal state of Brandenburg (Germany). Here we report on the application of a forest gap model to analyse the impacts of climate change on species composition and productivity of natural and managed forests in Brandenburg using a statistical method for the development of climate scenarios. The forest model was linked to a GIS that includes soil and groundwater table maps, as well as gridded climate data with a resolution of 10 × 10 km and simulated a steady-state species composition which was classified into forest types based on the biomass distribution between species. Different climate scenarios were used to assess the sensitivity of species composition to climate change. The simulated forest distribution patterns for current climate were compared with a map of Potential Natural Vegetation (PNV) of Brandenburg.In order to analyse the possible consequences of climate change on forest management, we used forest inventory data to initialize the model with representative forest stands. Simulation experiments with two different management strategies indicated how forest management could respond to the projected impacts of climate change. The combination of regional analysis of natural forest dynamics under climate change with simulation experiments for managed forests outlines possible trends for the forest resources. The implications of the results are discussed, emphasizing the regional differences in environmental risks and the adaptation potentials of forestry in Brandenburg.     

Spatial modeling of forest stand susceptibility to logging operations

The susceptibility of residual, non-harvested, live trees to damage caused by the harvesting of other nearby trees has received moderate attention over the last four decades through observational studies prompted by concerns over ecological and economic consequences of logging operations. We developed models to predict the potential level of damage to residual trees that could be caused by selective timber harvesting. Three machine-learning methods, i.e., classification and regression tree (CART), random forest (RF), and boosted regression tree (BRT), were assessed for this purpose. Through an observational study of a harvested area in the Hyrcanian forests of Iran, we recorded damage to trees >7.5 cm diameter at breast height along transects and grouped them into three types: (1) scars >100 cm², (2) >50% crown removal, and (3) trees leaning >10°. These field observations were associated with the spatially explicit characteristics of the forest stand, i.e., slope angle, slope aspect, altitude, slope length, topographic position index, stand type, stand density, and distance from the nearest roads and skid trails, that were considered as the explanatory variables to the modeling processes. To determine whether the CART, RF, and BRT models performed well in estimating the probability of damage occurrence, they were validated using the Akaike information criterion (AIC) and area under the receiver operating characteristics (AUC) curve. The results revealed that the BRT model with AIC = −276 and AUC = 0.89 generated the most accurate spatially explicit distribution map of stand susceptibility to damage from logging operations, followed by RF (AIC = −263 and AUC = 0.87) and CART (AIC = −23 and AUC = 0.62). We found that the spatial extent of residual stand damage was highly influenced by slope terrain and stand density. Our study has practical implications for reorganizing and planning reduced-impact logging operations and provides forest engineers with insights into the utility of machine learning methods in domains of forestry and forest engineering.     

A Three-Dimensional, Integrated Seasonal Separate Advection–Diffusion Model (ISSADM) to Predict Water Quality Patterns in the Chahnimeh Reservoir

Seasonal rivers are the main sources of discharge for many lakes and reservoirs. These rivers can deliver pollutants into these water bodies, especially during large events. The fate and distribution of these pollutants within lakes is difficult to predict. Here, a three-dimensional, finite-volume model for predicting lake water quality is used to account for internal advection and diffusion, including the impacts of the inflowing rivers on the velocity field. We used parsimonious sub-models for the source/sink terms for temperature, dissolved oxygen, ammonia nitrogen, phosphorous, phytoplankton, and zooplankton concentrations and tested the model predictions against field measurements from the Chahnimeh Reservoir in Iran. The modeled water quality parameters were in good agreement with the measured values. Results were notably poorer when the three-dimensionality of the model was removed. This study suggests that properly simulating three-dimensional advection is important to properly predict the distribution of pollutants within some lakes and reservoirs and that this model may be directly applicable to systems similar to Chahnimeh Reservoir.     

Eight nonnative plants in western Oregon forests: Associations with environment and management

Nonnative plants have tremendous ecological and economic impacts on plant communities globally, but comprehensive data on the distribution and ecological relationships of individual species is often scarce or nonexistent. The objective of this study was to assess the influence of vegetation type, climate, topography, and management history on the distribution and abundance of eight selected nonnative plant taxa in forests in western Oregon. These eight taxa were selected as being reliably detected by a multi-resource inventory of 1127 systematically-placed plots on nonfederal forest lands from 1995 to 1997 by the USFS Forest Inventory and Analysis (FIA) program. One or more of the eight nonnative taxa studied were found on 20% of the sampled subplots in the study area, but relatively few stands were dominated by them. Overall abundance of nonnative taxa was likely much greater, because few composites and graminoids were identified to species in this general-purpose inventory. Distribution of most taxa was more closely associated with low density of overstory trees than with climate. Nonnative taxa were significantly more abundant in stands that had been recently clearcut or thinned than in stands that had not. Frequencies of several taxa decreased with elevation, which may reflect proximity to source populations and intensive land use rather than any climatic constraints. Although the greatest potential for displacement of native forest species appears to be in early-successional communities, the potential for spread of some shade-tolerant evergreen shrubs also seems high.     

Correspondence of biological condition models of California streams at statewide and regional scales

We used boosted regression trees (BRT) to model stream biological condition as measured by benthic macroinvertebrate taxonomic completeness, the ratio of observed to expected (O/E) taxa. Models were developed with and without exclusion of rare taxa at a site. BRT models are robust, requiring few assumptions compared with traditional modeling techniques such as multiple linear regression. The BRT models were constructed to provide baseline support to stressor delineation by identifying natural physiographic and human land use gradients affecting stream biological condition statewide and for eight ecological regions within the state, as part of the development of numerical biological objectives for California’s wadeable streams. Regions were defined on the basis of ecological, hydrologic, and jurisdictional factors and roughly corresponded with ecoregions. Physiographic and land use variables were derived from geographic information system coverages. The model for the entire state (n?=?1,386) identified a composite measure of anthropogenic disturbance (the sum of urban, agricultural, and unmanaged roadside vegetation land cover) within the local watershed as the most important variable, explaining 56 % of the variance in O/E values. Models for individual regions explained between 51 and 84 % of the variance in O/E values. Measures of human disturbance were important in the three coastal regions. In the South Coast and Coastal Chaparral, local watershed measures of urbanization were the most important variables related to biological condition, while in the North Coast the composite measure of human disturbance at the watershed scale was most important. In the two mountain regions, natural gradients were most important, including slope, precipitation, and temperature. The remaining three regions had relatively small sample sizes (n?≤?75 sites) and had models that gave mixed results. Understanding the spatial scale at which land use and land cover affect taxonomic completeness is imperative for sound management. Our results suggest that invertebrate taxonomic completeness is affected by human disturbance at the statewide and regional levels, with some differences among regions in the importance of natural gradients and types of human disturbance. The construction and application of models similar to the ones presented here could be useful in the planning and prioritization of actions for protection and conservation of biodiversity in California streams.