Using Bayesian networks to incorporate uncertainty in habitat suitability index models

Habitat suitability index (HSI) models rarely characterize the uncertainty associated with their estimates of habitat quality despite the fact that uncertainty can have important management implications. The purpose of this paper was to explore the use of Bayesian belief networks (BBNs) for representing and propagating 3 types of uncertainty in HSI models—uncertainty in the suitability index relationships, the parameters of the HSI equation, and measurement of habitat variables (i.e., model inputs). I constructed a BBN–HSI model, based on an existing HSI model, using Netica™ software. I parameterized the BBN's conditional probability tables via Monte Carlo methods, and developed a discretization scheme that met specifications for numerical error. I applied the model to both real and dummy sites in order to demonstrate the utility of the BBN–HSI model for 1) determining whether sites with different habitat types had statistically significant differences in HSI, and 2) making decisions based on rules that reflect different attitudes toward risk—maximum expected value, maximin, and maximax. I also examined effects of uncertainty in the habitat variables on the model's output. Some sites with different habitat types had different values for E[HSI], the expected value of HSI, but habitat suitability was not significantly different based on the overlap of 90% confidence intervals for E[HSI]. The different decision rules resulted in different rankings of sites, and hence, different decisions based on risk. As measurement uncertainty in habitat variables increased, sites with significantly different (α = 0.1) E[HSI] became statistically more similar. Incorporating uncertainty in HSI models enables explicit consideration of risk and more robust habitat management decisions. © 2012 The Wildlife Society.     

Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.     

Developing and testing a habitat suitability index model for Korean water deer (Hydropotes inermis argyropus) and its potential for landscape management decisions in Korea

Geographic information system (GIS) and landscape-level data offer a new opportunity for modeling and evaluating the quality of wildlife habitats. Models of habitat quality have not been developed for some species, and existing models could be improved by incorporating updated information on wildlife–habitat relationships and habitat variables. We developed a GIS-based habitat suitability index (HSI) model for the Korean water deer (Hydropotes inermis argyropus), which often causes human–wildlife conflicts in the Chungnam Province of Korea because of industrialization and urbanization. The model is based on logistic regression analysis, which addresses the impact of multiple habitat variables, such as habitat components, topographic characteristics, and human disturbances. The model yielded a p-value of .289 (χ²?=?9.672) and 65.4% correct prediction level with the overall observation–prediction comparison data. The model demonstrated that a large portion of the province (61.6%) could be regarded as a poor habitat (mean HSI value of the province?=?0.22), while the current habitats of the province could be considered of moderate quality (mean HSI value?=?0.31). In addition, the chance of observation of the deer increases as the HSI level increases, which means that the model yields a good predictive power. Lastly, we used the model to produce a habitat suitability map. Our HSI model enabled us to quantify habitat preferences, which could be the basis for decision-making on habitat protection, mitigation, and enhancement of the Korean water deer. The proposed model is also applicable for improving and enhancing the existing management practices, as well as for establishing an effective wildlife protection policy.     

Evaluation of Habitat Suitability Models for Forest Passerines Using Demographic Data

ABSTRACT Habitat suitability is often used as a surrogate for demographic responses (i.e., abundance, survival, fecundity, or population viability) in the application of habitat suitability index (HSI) models. Whether habitat suitability actually relates to demographics, however, has rarely been evaluated. We validated HSI models of breeding habitat suitability for wood thrush (Hylocichla mustelina) and yellow-breasted chat (Icteria virens) in Missouri, USA. First, we evaluated HSI models as a predictor of 3 demographic responses: within-site territory density, site-level territory density, and nest success. We demonstrated a link between HSI values and all 3 types of demographic responses for the yellow-breasted chat and site-level territory density for the wood thrush. Second, we evaluated support for models containing HSI values, models containing measured habitat features (e.g., tree age, tree species, ecological land type), and models containing management treatments (e.g., even-aged and uneven-aged forest regeneration treatments) for each demographic response using model selection. Models containing HSI values received more support, in general, than models containing only habitat features or management treatments for all 3 types of wildlife response. The assumption that changes in habitat suitability represent wildlife demographic response to vegetation change is supported by our models. However, differences in species ecology may contribute to the degree to which HSI values are related to specific demographic responses. We recommend validation of HSI models with the particular demographic data of interest (i.e., density, productivity) to increase confidence in the model used for conservation planning.     

Development and field validation of a regional,management‐scale habitat model: A koala Phascolarctos cinereus case study

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.     

Distribution and habitat suitability index model for the Andean catfish Astroblepus ubidiai (Pisces: Siluriformes) in Ecuador

In conservation biology there is a basic need to determine habitat suitability and availability. Astroblepus ubidiai (Siluriforms), the only native fish in the highlands of Imbabura province in the Ecuadorian Andes, was abundant in the past in the Imbakucha watershed and adjacent drainages, but currently it is restricted to a few isolated refuges. Conservation actions are needed if this unique fish is to persist. A Habitat Suitability Index (HSI) for the species has been developed in order to aid management decisions. In this HSI model biomass density (B) was selected as a better indicator of habitat quality than either abundance or density. A population well-being index (PI) was constructed with the combination of B and an indicator of fish health (proportion of fish in the population with parasites and deformities). Based in other models of benthic fish the habitat variables current velocity, flow, depth, width, cover, invertebrate composition, vegetation type, terrestrial vegetation, land use, substrate, temperature, pH, TDS, oxygen, altitude, and slope were included in the analysis. An anthropogenic perturbation index (H) and a fragment isolation index (FII) were developed and included as habitat variables as well. The HSI model was applied to refuges and a sample of 15 aquatic bodies without fish populations within the study region. From the sampled sites without A. ubidiai 26.6% presented low quality, and the remaining 73.3% had medium quality according to the HSI estimated. Good quality habitat for dispersal, escape or translocations is rare in the region. The low HSIs estimated in some of the refuges suggests that current populations are not settled in the most favorable habitat but in the habitat least favorable to the agents of decline.     

Too wet and too dry? Uncertainty of DEM as a potential source of significant errors in a model-based water level assessment in riparian and mire ecosystems

Modelling groundwater depths in floodplains and peatlands remains a basic approach to assessing hydrological conditions of habitats. Groundwater flow models used to compute groundwater heads are known for their uncertainties, and the calibration of these models and the uncertainty assessments of parameters remain fundamental steps in providing reliable data. However, the elevation data used to determine the geometry of model domains are frequently considered deterministic and hence are seldom considered a source of uncertainty in model-based groundwater level estimations. Knowing that even the cutting-edge laser-scanning-based digital elevation models have errors due to vegetation effects and scanning procedure failures, we provide an assessment of uncertainty of water level estimations that remain basic data for wetland ecosystem assessment and management. We found that the uncertainty of the digital elevation model (DEM) significantly influenced the results of the assessment of the habitat’s hydrological conditions expressed as groundwater depths. In extreme cases, although the average habitat suitability index (HSI) assessed in a deterministic manner was defined as ‘unsuitable’, in a probabilistic approach (grid-cell-scale estimation), it reached a value of 40% probability, signifying ‘optimum’ or ‘tolerant’. For the 24 habitats analysed, we revealed vast differences between HSI scores calculated for individual grid cells of the model and HSI scores computed as average values from the set of grid cells located within the habitat patches. We conclude that groundwater-modelling-based decision support approaches to wetland assessment can result in incorrect management if the quality of DEM has not been addressed in studies referring to groundwater depths.     

Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models

Concern over rapid global changes and the potential for interactions among multiple threats are prompting scientists to combine multiple modelling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land‐use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land‐use change, and altered disturbance regimes on species' extinction risk. Each modelling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land‐use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modelling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long‐run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species (Acanthomintha ilicifolia, or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long‐run populations.     

Uncertainty in predictions of range dynamics: black grouse climbing the Swiss Alps

Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species’ vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate‐induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual‐based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species’ responses to climate change.     

Influence of fine‐scale habitat requirements and riparian degradation on the distribution of the purple‐crowned fairy‐wren (Malurus coronatus coronatus) in northern Australia

Species distributions are influenced by variation in environmental conditions across many scales. Knowledge of fine‐scale habitat requirements is important for predicting species occurrence and identifying suitable habitat for target species. Here we investigate the perplexing distribution of a riparian habitat specialist, the western subspecies of the purple‐crowned fairy‐wren (Malurus coronatus coronatus), in relation to fine‐scale habitat associations and patterns of riparian degradation. Surveys of vegetation attributes, river structure and disturbance indicators that are likely to be causal determinants of the species occurrence were undertaken at 635 sites across 14 catchments. Generalized Linear Mixed Modelling demonstrated that the probability of purple‐crowned fairy‐wren occurrence increased with Pandanus aquaticus crown cover, shrub density and height of emergent trees, while riparian structure and signs of cattle were indirect predictors of occurrence. As our study area predominantly contained Pandanus type habitat, we failed to identify river grass as an important component of habitat. Predictions from a cross‐validated model of purple‐crowned fairy‐wren occurrence suggested distribution is constrained by three factors: (i) low quality of local habitat within catchments where the species occurs; (ii) broad‐scale reduction in habitat quality that has resulted in extinction of the species from parts of its range; and (iii) unmeasured variables that limit the exploitation of suitable habitat. The reliance of the species on dense shrubby understorey suggests conservation efforts should aim to maintain the complexity of understorey structure by managing fire and grazing intensity. Efforts to halt the continuing decline of riparian condition and maintain connectivity between areas of quality habitat will help to ensure persistence of riparian habitat specialists in northern Australia.     

Habitat suitability for tsessebe Damaliscus lunatus lunatus

The distribution and numbers of tsessebe (Damaliscus lunatus lunatus) have declined considerably in South Africa, partly due to deteriorating habitat conditions. Identifying important habitat variables will assist in managing the species. The objective of this study was to identify habitat variables important for tsessebe and to develop a predictive model of habitat selection for this species in a savanna biome. The study was conducted in the Nylsvley Nature Reserve over a 2‐year period. A total of eighteen habitat variables were measured in ten plant communities at 200 sites. Logistic regression analyses were used to identify predictor variables and to construct a habitat model. Tsessebe were found <2 km from the nearest source of water, in flat areas with slopes of <3° and with <10% rockiness. Their distribution was not influenced by the woody component. Sites where tsessebe were present had significantly lower grass heights and tuft heights, with a higher grass density compared with areas not utilized by tsessebe. Nitrogen and sodium levels were also higher at present sites. Habitat type and grass height were the most significant predictors of tsessebe presence. The selected model had an overall percentage prediction of 85.0%. The model was subdivided into five vegetation‐specific models and each model was tested with independent data.     

Modeling ecological minimum requirements for distribution of greater sage‐grouse leks: implications for population connectivity across their western range,U.S.A

Greater sage‐grouse Centrocercus urophasianus (Bonaparte) currently occupy approximately half of their historical distribution across western North America. Sage‐grouse are a candidate for endangered species listing due to habitat and population fragmentation coupled with inadequate regulation to control development in critical areas. Conservation planning would benefit from accurate maps delineating required habitats and movement corridors. However, developing a species distribution model that incorporates the diversity of habitats used by sage‐grouse across their widespread distribution has statistical and logistical challenges. We first identified the ecological minimums limiting sage‐grouse, mapped similarity to the multivariate set of minimums, and delineated connectivity across a 920,000 km² region. We partitioned a Mahalanobis D² model of habitat use into k separate additive components each representing independent combinations of species–habitat relationships to identify the ecological minimums required by sage‐grouse. We constructed the model from abiotic, land cover, and anthropogenic variables measured at leks (breeding) and surrounding areas within 5 km. We evaluated model partitions using a random subset of leks and historic locations and selected D² (k = 10) for mapping a habitat similarity index (HSI). Finally, we delineated connectivity by converting the mapped HSI to a resistance surface. Sage‐grouse required sagebrush‐dominated landscapes containing minimal levels of human land use. Sage‐grouse used relatively arid regions characterized by shallow slopes, even terrain, and low amounts of forest, grassland, and agriculture in the surrounding landscape. Most populations were interconnected although several outlying populations were isolated because of distance or lack of habitat corridors for exchange. Land management agencies currently are revising land‐use plans and designating critical habitat to conserve sage‐grouse and avoid endangered species listing. Our results identifying attributes important for delineating habitats or modeling connectivity will facilitate conservation and management of landscapes important for supporting current and future sage‐grouse populations.     

Use of different approaches to model presence/absence of Salmo marmoratus in Piedmont (Northwestern Italy)

In Piedmont (Italy) the environmental changes due to human impact have had profound effects on rivers and their inhabitants. Thus, it is necessary to develop practical tools providing accurate ecological assessments of river and species conditions. We focus our attention on Salmo marmoratus, an endangered salmonid which is characteristic of the Po river system in Italy. In order to contribute to the management of the species, four different approaches were used to assess its presence: discriminant function analysis, logistic regression, decision tree models and artificial neural networks. Either all the 20 environmental variables measured in the field or the 7 coming from feature selection were used to classify sites as positive or negative for S. marmoratus. The performances of the different models were compared. Discriminant function analysis, logistic regression, and decision tree models (unpruned and pruned) had relatively high percentages of correctly classified instances. Although neither tree-pruning technique improved the reliability of the models significantly, they did reduce the tree complexity and hence increased the clarity of the models. The artificial neural network (ANN) approach, especially the model built with the 7 inputs coming from feature selection, showed better performance than all the others. The relative contribution of each independent variable to this model was determined by using the sensitivity analysis technique. Our findings proved that the ANNs were more effective than the other classification techniques. Moreover, ANNs achieved their high potentials when they were applied in models used to make decisions regarding river and conservation management.     

Microhabitat relationships among five lizard species associated with granite outcrops in fragmented agricultural landscapes of south‐eastern Australia

A fundamental part of developing effective biodiversity conservation is to understand what factors affect the distribution and abundance of particular species. However, there is a paucity of data on ecological requirements and habitat relationships for many species, especially for groups such as reptiles. Furthermore, it is not clear whether habitat relationships for particular species in a given environment are transferable to other environments within their geographical range. This has implications for the type of ‘landscape model’ used to guide management decisions in different environments worldwide. To test the hypothesis that species‐specific habitat relationships are transferable to other environments, we present microhabitat models for five common lizard species from a poorly studied habitat – insular granite outcrops, and then compared these relationships with studies from other environments in south‐eastern Australia. We recorded twelve species from five families, representing 699 individuals, from 44 outcrops in the south‐west slopes of New South Wales. Five lizard species were abundant and accounted for 95% of all observations: Egernia striolata, Ctenotus robustus, Cryptoblepharus carnabyi, Morethia boulengeri and Carlia tetradactyla (Scincidae). Linear regression modelling revealed suites of different variables related to the abundance patterns of individual species, some of which were broadly congruent with those measured for each species in other environments. However, additional variables, particular to rocky environments, were found to relate to reptile abundance in this environment. This finding means that species' habitat relationships in one habitat may not be readily transferable to other environments, even those relatively close by. Based on these data, management decisions targeting reptile conservation in agricultural landscapes, which contain rocky outcrops, will be best guided by landscape models that not only recognize gradients in habitat suitability, but are also flexible enough to incorporate intraspecies habitat variability.     

Managed relocation as an adaptation strategy for mitigating climate change threats to the persistence of an endangered lizard

The distributional ranges of many species are contracting with habitat conversion and climate change. For vertebrates, informed strategies for translocations are an essential option for decisions about their conservation management. The pygmy bluetongue lizard, Tiliqua adelaidensis, is an endangered reptile with a highly restricted distribution, known from only a small number of natural grassland fragments in South Australia. Land‐use changes over the last century have converted perennial native grasslands into croplands, pastures and urban areas, causing substantial contraction of the species' range due to loss of essential habitat. Indeed, the species was thought to be extinct until its rediscovery in 1992. We develop coupled‐models that link habitat suitability with stochastic demographic processes to estimate extinction risk and to explore the efficacy of potential climate adaptation options. These coupled‐models offer improvements over simple bioclimatic envelope models for estimating the impacts of climate change on persistence probability. Applying this coupled‐model approach to T. adelaidensis, we show that: (i) climate‐driven changes will adversely impact the expected minimum abundance of populations and could cause extinction without management intervention, (ii) adding artificial burrows might enhance local population density, however, without targeted translocations this measure has a limited effect on extinction risk, (iii) managed relocations are critical for safeguarding lizard population persistence, as a sole or joint action and (iv) where to source and where to relocate animals in a program of translocations depends on the velocity, extent and nonlinearities in rates of climate‐induced habitat change. These results underscore the need to consider managed relocations as part of any multifaceted plan to compensate the effects of habitat loss or shifting environmental conditions on species with low dispersal capacity. More broadly, we provide the first step towards a more comprehensive framework for integrating extinction risk, managed relocations and climate change information into range‐wide conservation management.     

Dynamic occupancy models reveal within‐breeding season movement up a habitat quality gradient by a migratory songbird

The timing of settlement decisions likely influences the quality of breeding site choices.This is particularly the case in migratory birds, because the conditions that enhance breeding success are often not apparent upon arrival after migration. A strategy that addresses this problem is to adjust settlement decisions when reliable information becomes available. We used a new indirect method – dynamic site occupancy modeling – to estimate apparent movement of black‐throated blue warblers Dendroica caerulescens among sites within a breeding season. Because individuals should disperse to sites that maximize their fitness, we hypothesized that warblers would move up a habitat quality gradient when opportunities arose. For our study species, that would involve moving into sites with greater shrub density and at higher elevation within northern hardwoods forest, as these two features are positively correlated with reproduction and apparent survival in this species. Although the probability of site occupancy in our study landscape remained consistent throughout the breeding season (range: 0.66–0.69), occupancy models revealed substantial support for apparent movement of individuals within the breeding season. The mean probability of emigration from a point count site was 0.21 (±0.03 SE), and the mean probability of immigration to a site not previously occupied was 0.51 (±0.05 SE). The spatial distribution of this movement was a function of habitat quality. A portion of the black‐throated blue warbler population appears to arrive on the breeding grounds and settle initially in sub‐optimal habitat, moving subsequently into high quality densely shrubbed habitat at higher elevations. This modeling approach provides a new means to test hypotheses about habitat selection and movement by using presence–non‐detection data.     

Gravel dredging alters diversity and structure of riverine fish assemblages

1. Human activities affect fish assemblages in a variety of ways. Large‐scale and long‐term disturbances such as in‐stream dredging and mining alter habitat and hydrodynamic characteristics within rivers which can, in turn, alter fish distribution. Habitat heterogeneity is decreased as the natural riffle–pool–run sequences are lost to continuous pools and, as a consequence, lotic species are displaced by lentic species, while generalist and invasive species displace native habitat specialists. Sediment and organic detritus accumulate in deep, dredged reaches and behind dams, disrupting nutrient flow and destroying critical habitat for habitat specialist species. 2. We used standard ecological metrics such as species richness and diversity, as well as stable isotope analysis of δ¹³C and δ¹⁵N, to quantify the differences in fish assemblages sampled by benthic trawls among dredged and undredged sites in the Allegheny River, Pennsylvania, U.S.A. 3. Using mixed‐effects models, we found that total catch, species richness and diversity were negatively correlated with depth (P < 0.05), while species richness, diversity and proportion of species in lithophilic (‘rock‐loving’) reproductive guilds were lower at dredged than at undredged sites (P < 0.05). 4. Principal components analysis and manova revealed that taxa such as darters in brood hider and substratum chooser reproductive guilds were predominantly associated with undredged sites along principal component axis 1 (PC1 and manova P < 0.05), while nest spawners such as catfish and open substratum spawners including suckers were more associated with dredged sites along PC2 (P < 0.05). 5. Stable isotope analysis of δ¹³C and δ¹⁵N revealed shifts from reliance on shallow water and benthic‐derived nutrients at undredged sites to reliance on phytoplankton and terrestrial detritus at deep‐water dredged sites. Relative trophic positions were also lower at dredged sites for many species; loss of benthic nutrient pathways associated with depth and dredging history is hypothesised. 6. The combination of ecological metrics and stable isotope analysis thus shows how anthropogenic habitat loss caused by gravel dredging can decrease benthic fish abundance and diversity, and that species in substratum‐specific reproductive guilds are at particular risk. The effects of dredging also manifest by altering resource use and nutrient pathways within food webs. Management and conservation decisions should therefore consider the protection of relatively shallow areas with suitable substratum for spawning for the protection of native fishes.     

Multi‐model comparison highlights consistency in predicted effect of warming on a semi‐arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi‐model approach to predict climate change impacts on big sagebrush (Artemisia tridentata), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata, we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi‐model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.     

Good modeling practice for PAT applications: Propagation of input uncertainty and sensitivity analysis

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model‐building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base‐consumption were found low compared to the large uncertainty observed in the antibiotic and off‐gas CO₂ predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases ‐ meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass‐transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Simple predictions of instream habitat model outputs for fish habitat guilds in large streams

1. In the context of a generalised modification of hydraulic conditions in medium to large streams, modelling the impacts of stream regulation on fish communities in multiple streams is an important challenge for basic and applied freshwater ecology. Conventional instream habitat models such as PHABSIM link a hydraulic model with preference curves for various species to estimate habitat value changes with discharge in stream reaches. Despite world‐wide applications, they have been scarcely used in multiple sites with multiple species. 2. We assigned 21 size classes of European fish species to four habitat guilds (cluster analysis grouping size classes with comparable microhabitat preference curves). Then, we ran a conventional instream habitat model on 28 French stream reaches belonging to the `barbel zone', to estimate habitat values versus discharge curves for the 21 size classes. We summarised the outputs as mean habitat values for guilds, and tested if they were predictable from average characteristics of reaches (discharge, depth, width, particle size). 3. As was obtained elsewhere for populations, habitat values for guilds were strongly related to average, dimensionless characteristics of reaches. The Reynolds number of reaches, equivalent to a discharge per width unit, reflected most of the discharge‐dependent changes in habitat values (within reaches). In particular, habitat values of species preferring bank (respectively midstream) microhabitats decreased (respectively increased) with increasing Reynolds number. The Froude number at median discharge was the major predictor of reach‐dependent but discharge‐independent variations in habitat values. Habitat values of species preferring riffle versus pool or bank microhabitats were higher in reaches with high Froude numbers. These relationships were consistent with existing knowledge on the different species. 4. Such results suggest that the input variables required to estimate habitat values for fish communities can be greatly simplified, as illustrated by a general estimation of the sensitivity of species preferring midstream habitats to discharge changes in any reach. Cost‐efficient alternatives to conventional instream habitat models should facilitate their validation in multiple sites, a point that remains critical in instream habitat modelling of fish communities.