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Why It Is Time to Put PHABSIM Out to Pasture: Response to Comments 1 and 2
... Several legislative bodies that aim to preserve ecosystem health and functionality-for example, the Spanish norm for hydrological planning (MAGRAMA, 2008)-advocate the use of physical habitat simulation approaches, because they render "objective" and "comparable" numerical outputs. However, they are not exempt of criticism (Railsback, 2016(Railsback, , 2017. Opperman et al. (2018, pp. ...
... Consequently they should be assisted with expert panels "to fill knowledge gaps for ecosystem components for which sufficient data to rigorously quantify flow relationships are lacking". Other authors criticized particular choices within each model component, such as the use of one-dimensional (1D) hydraulic models to simulate the distribution patterns of depth and velocity under different running flows, the use of univariate HSC, or focusing exclusively on a single species, to name a few (Railsback, 2016(Railsback, , 2017. Although these concerns and criticisms are still valid for a number of studies, over the years scientists and practitioners have improved the original practices to address most of these limitations (Reiser & Hilgert, 2018). ...
... Another well-known deficiency of several studies has been the choice of focusing on fish species of recreational or commercial interest (Railsback, 2016(Railsback, , 2017 because fish species have been assumed to be the most suitable impact indicators of the ecological status as they integrate numerous pressures (Melcher et al., 2018;. Nevertheless, although still scarce, examples focusing on multiple groups of taxa and habitat suitability models for species guilds exist (e.g., Alexander et al., 2018). ...
... Several legislative bodies that aim to preserve ecosystem health and functionality-for example, the Spanish norm for hydrological planning (MAGRAMA, 2008)-advocate the use of physical habitat simulation approaches, because they render "objective" and "comparable" numerical outputs. However, they are not exempt of criticism (Railsback, 2016(Railsback, , 2017. Opperman et al. (2018, pp. ...
... Consequently they should be assisted with expert panels "to fill knowledge gaps for ecosystem components for which sufficient data to rigorously quantify flow relationships are lacking". Other authors criticized particular choices within each model component, such as the use of one-dimensional (1D) hydraulic models to simulate the distribution patterns of depth and velocity under different running flows, the use of univariate HSC, or focusing exclusively on a single species, to name a few (Railsback, 2016(Railsback, , 2017. Although these concerns and criticisms are still valid for a number of studies, over the years scientists and practitioners have improved the original practices to address most of these limitations (Reiser & Hilgert, 2018). ...
... Another well-known deficiency of several studies has been the choice of focusing on fish species of recreational or commercial interest (Railsback, 2016(Railsback, , 2017 because fish species have been assumed to be the most suitable impact indicators of the ecological status as they integrate numerous pressures (Melcher et al., 2018;. Nevertheless, although still scarce, examples focusing on multiple groups of taxa and habitat suitability models for species guilds exist (e.g., Alexander et al., 2018). ...
Mountains and mountain rivers provide a multitude of invaluable goods and services to a profound portion of the planet’s population. As “water towers” of the Earth mountains are sources of the mightiest world rivers and play a pivotal role for global biodiversity, freshwater, and sediment supply. Distinct morphological, climatic, hydrological, hydrochemical, and biological features of mountainous river ecosystems, compared to lowland ones, make them particularly fragile and vulnerable to human interference. Despite a number of remote mountain areas and rivers still remaining intact from direct human pressures, the majority of mountain ecosystems, are being increasingly threatened by adverse local and global changes driven by market economy. To efficiently conserve and sustainably use mountain ecosystems and contribute to the survival of the planet, it is critical to change our standards and life attitudes by realizing and appreciating our immediate connection to the global ecosystem, change attitudes and current consumption patterns, and stimulate the ways our global society functions and interacts with the natural environment.
... Several legislative bodies that aim to preserve ecosystem health and functionality-for example, the Spanish norm for hydrological planning (MAGRAMA, 2008)-advocate the use of physical habitat simulation approaches, because they render "objective" and "comparable" numerical outputs. However, they are not exempt of criticism (Railsback, 2016(Railsback, , 2017. Opperman et al. (2018, pp. ...
... Consequently they should be assisted with expert panels "to fill knowledge gaps for ecosystem components for which sufficient data to rigorously quantify flow relationships are lacking". Other authors criticized particular choices within each model component, such as the use of one-dimensional (1D) hydraulic models to simulate the distribution patterns of depth and velocity under different running flows, the use of univariate HSC, or focusing exclusively on a single species, to name a few (Railsback, 2016(Railsback, , 2017. Although these concerns and criticisms are still valid for a number of studies, over the years scientists and practitioners have improved the original practices to address most of these limitations (Reiser & Hilgert, 2018). ...
... Another well-known deficiency of several studies has been the choice of focusing on fish species of recreational or commercial interest (Railsback, 2016(Railsback, , 2017 because fish species have been assumed to be the most suitable impact indicators of the ecological status as they integrate numerous pressures (Melcher et al., 2018;. Nevertheless, although still scarce, examples focusing on multiple groups of taxa and habitat suitability models for species guilds exist (e.g., Alexander et al., 2018). ...
The modification of sediment and flow regimes caused by damming and river regulation has deleterious effects on the ecological and morphological river processes. This alteration of river systems triggered the implementation of safeguarding environmental flows (e-flows) defined as “the quantity, timing, and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and wellbeing that depend on these ecosystems”. In the last decades, physical habitat simulation approaches emerged as fundamental stand-alone or supplementary methods for e-flow assessment. These approaches combine three main components: (1) hydraulic simulation, (2) habitat suitability modeling, to determine the quality of the available habitat, and (3) hydrological analyses (under current and climate change scenarios). E-flow regimes are finally defined, by assessing the spatial and temporal habitat variability for the target taxa or community, after combining these three components. During the process of physical habitat simulation some river processes, such as sediment transport and morphological changes, are often neglected while uncertainties arise from every component. We reviewed the elements that should be considered in every component of the physical habitat simulation to reduce uncertainties with emphasis on the actual trends on the topic and how sediment transport and river morphodynamics can be included within this methodological framework.
... Several legislative bodies that aim to preserve ecosystem health and functionality-for example, the Spanish norm for hydrological planning (MAGRAMA, 2008)-advocate the use of physical habitat simulation approaches, because they render "objective" and "comparable" numerical outputs. However, they are not exempt of criticism (Railsback, 2016(Railsback, , 2017. Opperman et al. (2018, pp. ...
... Consequently they should be assisted with expert panels "to fill knowledge gaps for ecosystem components for which sufficient data to rigorously quantify flow relationships are lacking". Other authors criticized particular choices within each model component, such as the use of one-dimensional (1D) hydraulic models to simulate the distribution patterns of depth and velocity under different running flows, the use of univariate HSC, or focusing exclusively on a single species, to name a few (Railsback, 2016(Railsback, , 2017. Although these concerns and criticisms are still valid for a number of studies, over the years scientists and practitioners have improved the original practices to address most of these limitations (Reiser & Hilgert, 2018). ...
... Another well-known deficiency of several studies has been the choice of focusing on fish species of recreational or commercial interest (Railsback, 2016(Railsback, , 2017 because fish species have been assumed to be the most suitable impact indicators of the ecological status as they integrate numerous pressures (Melcher et al., 2018;. Nevertheless, although still scarce, examples focusing on multiple groups of taxa and habitat suitability models for species guilds exist (e.g., Alexander et al., 2018). ...
In all available methodologies for the assessment of the environmental flow requirements, a sufficient knowledge of the natural hydrological regime is essential. In this chapter the hydrological data that are required in environmental flow assessment studies, their main characteristics, and their importance as well as the specific challenges in the case of mountainous areas are analyzed. The various available data sources, the measurement and processing of hydrological data, and the utilization of modeling techniques for the estimation of streamflow data in the case of ungauged or poorly gauged watersheds and for the naturalization of streamflow data are also presented. A short description of hydrological data series analysis for the determination of environmental water requirements is provided as well. Finally, sources for further reading are provided in each section.
... Its most recent critic, Railsback (2016), went so far as to title his paper "Why It Is Time to Put PHABSIM Out to Pasture." This prompted comments from Beecher (2017) and Stalnaker et al. (2017) and a corresponding response from Railsback (2017). Kemp and Katopodis (2017) also provided comments noting the timeliness of the Railsback (2017) paper and promoting further dialogue on the subject. ...
... This prompted comments from Beecher (2017) and Stalnaker et al. (2017) and a corresponding response from Railsback (2017). Kemp and Katopodis (2017) also provided comments noting the timeliness of the Railsback (2017) paper and promoting further dialogue on the subject. We read all three comments and the author's response to the first two, and as active instream flow practitioners and independent reviewers of the original Railsback (2016) article, we felt there was more to be said. ...
... Despite the established status of habitat modelling in river management, users should be aware of limitations, uncertainties and reality checks associated with mesohabitat and any instream habitat modelling approaches (Beecher, 2017;Railsback, 2016Railsback, , 2017Stalnaker, Chisholm, & Paul, 2017). Limitations in habitat classification regarding partially subjective decision-making in the field have already been discussed in previous sections, and the interested reader may refer to Poole and Frissell (1998) (Vadas & Orth, 2000). ...
Modelling the linkage between physical habitat and aquatic organisms on multiple spatial scales has become an important tool in the management of rivers. The mesoscale (100–102 m) represents an intermediate resolution in modelling that bridges the gap between available resources and conservation efforts for riverine species. However, existing mesohabitat classification schemes for lotic systems vary significantly in the definition of habitat types as well as in their application in the field. This article aims to provide an overview of current attempts to model the mesoscale pattern of physical habitats with a focus on fish. First, we outline descriptive, qualitative as well as objective, quantitative classification methods that are available in the literature. Next, the ecological relevance of the mesohabitat concept is being discussed, using single‐species and community‐level approaches as examples. Different modelling approaches that describe and quantify riverine mesohabitats are presented, and finally, limitations and uncertainties in the modelling process are discussed, followed by an outline of future perspectives in mesohabitat modelling.
We compared whole life cycle population productivity to population density and streamflow experienced by rearing juveniles for ten Chinook salmon populations in the Salmon River drainage of central Idaho, USA. Three of the populations were in drainages with heavily developed water resources (developed drainages) and seven were in drainages with very little water use (undeveloped drainages). For two of the populations, one in a developed and one in an undeveloped drainage, we also compared productivity measured at the juvenile outmigrant and smolt life stages to population density and rearing streamflow. Productivity was positively related to flow experienced by rearing juveniles across the entire range of flows. The strength of the relationships increased with age, with the weakest and strongest relationships, respectively, for the outmigrant and adult return life stages. Both population size and productivity were substantially higher in undeveloped than in developed drainages, but the relationships of population productivity and rearing flow were similar. Productivity was negatively related to population density in both developed and undeveloped drainages and, as with flow, the strength of the relationships increased with age. Adding population density to the regression models usually did not improve relationships for flow, possibly due to high leverage of the low population density data points, especially for undeveloped drainages. Removing all data points in the lowest 25 percentile population density increased the strength of the productivity versus rearing flow relationships in both developed and undeveloped drainages, but did not appreciably change the slopes of the relationships. The positive relationships across the entire range of flows suggest that instream flows should be protected and enhanced whenever possible.
Modeller för att simulera effekter av flöde på strömlevande fiskpopulationer är kraftfulla verktyg för att avväga miljönytta och kostnad i samband med åtgärder för att minimera vattenkraftens miljöpåverkan. Vi jämförde en korrelativ och en individbaserad fiskhabitatmodell med avseende på vilka flöden respektive modell bedömde var gynnsammast för en potentiell havsöringspopulation i naturfåran vid Blankaströms kraftverk i Emån. Den korrelativa modellen förutspådde att ett optimalt flöde för att maximera arean med högkvalitativt öringhabitat låg mellan 2 och 3 m3/s. Den individbaserade modellen fann att flöde spelade mindre roll för överlevnad hos den yngsta årsklassen (0+), samt att tillväxten hos dessa var som högst vid 3 m3/s. Högre flöden krävdes dock för lyckad reproduktion och att överlevnaden och tillväxten hos äldre juveniler (1+) gynnades av flöden mellan 5 och 8 m3/s. Korrelativa modeller kan vara användbara, då de är enkla att använda, men det är möjligt att de framförallt förutsäger habitatförekomst för 0+-öringar och sämre speglar de miljöförhållanden som krävs för 1+-öringars uppväxt samt lekfiskars reproduktionsframgång. Individbaserade modeller, å andra sidan, är något mer komplicerade, men genererar mångfacetterad data för olika livsstadier, ger mekanistiska förklaringar till observerade fenomen och kan anpassas till dynamiska flöden.
Habitat Suitability Curves (HSCs) are the biological component of habitat simulation tools used to evaluate instream flow management trade‐offs (e.g., the Physical Habitat Simulation Model). However, traditional HSCs based on empirical observations of habitat use relative to availability have been criticized for generating biased estimates of flow requirements and for being poorly transferable across locations. For fish like salmonids that feed on drifting invertebrates, bioenergetics‐based foraging models that relate habitat conditions to net energy gain offer an alternative approach that addresses some of these shortcomings. To make this technique more accessible for practitioners, we present free and user‐friendly software for generating bioenergetics‐based HSCs. The software also allows sensitivity analyses of HSCs to factors like fish size or prey abundance as well as direct integration of hydraulic data. While some caveats remain, bioenergetic HSCs should offer a more rigorous and credible means for quantifying habitat suitability for instream flow modelling.
Movement ecology has developed rapidly over the past decade, driven by advances in tracking technology that have largely removed data limitations. Development of rigorous analytical tools has lagged behind empirical progress, and as a result, relocation data sets have been underutilized.
Discrete‐time correlated random walk models ( CRW ) have long served as the foundation for analyzing relocation data. Unfortunately, CRW s confound the sampling and movement processes. CRW parameter estimates thus depend sensitively on the sampling schedule, which makes it difficult to draw sampling‐independent inferences about the underlying movement process. Furthermore, CRW s cannot accommodate the multiscale autocorrelations that typify modern, finely sampled relocation data sets.
Recent developments in modelling movement as a continuous‐time stochastic process ( CTSP ) solve these problems, but the mathematical difficulty of using CTSP s has limited their adoption in ecology. To remove this roadblock, we introduce the ctmm package for the R statistical computing environment. ctmm implements all of the CTSP s currently in use in the ecological literature and couples them with powerful statistical methods for autocorrelated data adapted from geostatistics and signal processing, including variograms, periodograms and non‐Markovian maximum likelihood estimation.
ctmm is built around a standard workflow that begins with visual diagnostics, proceeds to candidate model identification, and then to maximum likelihood fitting and AIC ‐based model selection. Once an accurate CTSP for the data has been fitted and selected, analyses that require such a model, such as quantifying home range areas via autocorrelated kernel density estimation or estimating occurrence distributions via time‐series Kriging, can then be performed.
We use a case study with African buffalo to demonstrate the capabilities of ctmm and highlight the steps of a typical CTSP movement analysis workflow.
During the last decade, there has been a proliferation of statistical methods for studying resource selection by animals. While statistical techniques are advancing at a fast pace, there is confusion in the conceptual understanding of the meaning of various quantities that these statistical techniques provide.
Terms such as selection, choice, use, occupancy and preference often are employed as if they are synonymous. Many practitioners are unclear about the distinctions between different concepts such as ‘probability of selection,’ ‘probability of use,’ ‘choice probabilities’ and ‘probability of occupancy’.
Similarly, practitioners are not always clear about the differences between and relevance of ‘relative probability of selection’ vs. ‘probability of selection’ to effective management.
Practitioners also are unaware that they are using only a single statistical model for modelling resource selection, namely the exponential probability of selection, when other models might be more appropriate. Currently, such multimodel inference is lacking in the resource selection literature.
In this paper, we attempt to clarify the concepts and terminology used in animal resource studies by illustrating the relationships among these various concepts and providing their statistical underpinnings.
Resource selection functions (RSFs) are statistical models defined to be proportional to the probability of use of a resource unit. My objective with this review is to identify how RSFs can be used to unravel the influence of scale in habitat selection. In wildlife habitat studies, including radiotelemetry, RSFs can be estimated using a variety of statistical methods, all of which can be used to explore the role of scale. All RSFs are bounded by the resolution of data and the spatial extent of the study area, but also allow predictor covariates to be measured at a variety of scales. Conditional logistic regression permits designs (e.g. matched case) that relate the process of habitat selection to a limited domain of resource units that might better characterize what is truly ‘available’ to the animal. Scale influences the process of habitat selection, e.g. food resources are often selected at fine spatial scales, whereas landscape patterns at much larger scales typically influence the location of home ranges. Scale also influences appropriate sampling in many ways: (1) heterogeneity might be obliterated (transmutation) if resolution or grain size is too large, (2) variance of habitat characteristics might be undersampled if extent or domain is too small, (3) timing and duration of observations can influence RSF models, and (d) both spatial and temporal autocorrelations can vary directly with the intensity of sampling. Using RSFs, researchers can examine habitat selection at multiple scales, and predictive models that bridge scales can be estimated. Using Geographical Information Systems, predictor covariates in RSF models can be measured at different scales easily so that the predictive ability of models at alternative spatial and temporal domains can be explored by the investigator. Identification of the scale that best explains the data can be evaluated by comparing alternative models using information-theoretic metrics such as Akaike Information Criteria, and predictive capability of the models can be assessed using k-fold cross validation.