Integrative assessment of Western Himalayas streams using multimeric index

Nazeer, Summya; Hashmi, Muhammad Zaffar; Malik, Riffat Naseem; Qadir, Abdul; Ahmad, Altaf; Ullah, Kalim

doi:10.1016/j.ecolind.2015.12.016

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…However, some tolerant species, known to dominate in conditions of diminished integrity (Karr ), can belong to different trophic guilds linked to either high or low integrity, such as top carnivores or omnivores, respectively (see Nazeer et al. and our results for tolerant species, de Freitas Terra et al. , for intolerant species).…”

Section: Discussionmentioning

confidence: 49%

“…-In our IBI and CCA, we found that the richness of tolerant species (e.g., sunfish) was associated with variables that indicated high integrity (e.g., carnivores), which is contradictory to the IBI approach. However, some tolerant species, known to dominate in conditions of diminished integrity (Karr 1981), can belong to different trophic guilds linked to either high or low integrity, such as top carnivores or omnivores, respectively (see Nazeer et al 2016 and our results for tolerant species, de Freitas Terra et al 2013, for intolerant species). Hence, under the IBI methodology, the same species could be contributing to high and low integrity scores in the same assessment depending on their tolerance and feeding habits.…”

Section: Reconsidering the Categorization Of Species In Ibimentioning

confidence: 51%

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Assessing the causal relationships of ecological integrity: a re‐evaluation of Karr's iconic Index of Biotic Integrity

2018

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The Index of Biotic Integrity (IBI) has been widely used since the 1980s to estimate ecological integrity—the capacity of an ecosystem to support and maintain its full range of components and processes. Despite this, IBI approaches have been criticized for their lack of objectivity, of justification for the selection of metrics, and of statistical rigor. In this paper, we assessed the potential of canonical correspondence analysis (CCA) and Structural equation modeling (SEM) as complementary methods for assessing ecological integrity. We use an iconic freshwater ecosystem dataset from Northeastern Illinois to assess ecological integrity using both classical IBI approach, and compare this with alternative methods. When we attempt to replicate the IBI methodology, we find issues with the approach including the possibility of the same IBI value for many possible levels of ecological integrity. We showed with the use of other methods (CCA) that the use of tolerant species, total abundance, and total richness—all defining components in IBI—can result in misleading interpretations of ecological integrity and that IBI scores might not comprehensively depict integrity scenarios. Structural equation modeling allowed us to test a conceptual causal model of ecological integrity. We found that water quality had an effect on the diversity and abundance of fish, and these in turn had an effect on trophic function, revealing important relationships between variables that contribute to ecological integrity. We conclude that CCA and SEM can complement multimetric indices, like IBI, and help with the development of more reliable, objective, and science‐based estimations of ecosystem integrity, as well as generate testable hypotheses about ecological integrity.

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Section: Discussionmentioning

confidence: 49%

Section: Reconsidering the Categorization Of Species In Ibimentioning

confidence: 51%

Assessing the causal relationships of ecological integrity: a re‐evaluation of Karr's iconic Index of Biotic Integrity

2018

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“…Nazeer et al (2016) found that poor ecological conditions in streams were generally related to the urban sewage discharge and habitat fragmentation in the lower Himalayas. Their degrees of urbanization were relatively low, including superior ecological integrity (Nazeer et al, 2016). The results of these work show that the ecological conditions in the urban area are poor, whereas those of the streams in the grassland and forest areas are good, which is consistent with the conclusions reached in this study regarding the poor ecological health of the urban areas and good river health conditions in the mountainous areas.…”

Section: Discussionmentioning

confidence: 99%

“…However, after implementing the pollution control of the water environment and a sufficiently long time of the water quality adjustment, various indices of the river ecosystem and the overall health index increase again. Nazeer et al (2016) found that poor ecological conditions in streams were generally related to the urban sewage discharge and habitat fragmentation in the lower Himalayas. Their degrees of urbanization were relatively low, including superior ecological integrity (Nazeer et al, 2016).…”

Section: Analysis Of the Ecosystem Health Indicatorsmentioning

confidence: 99%

Standards for environmental flow verification

et al. 2020

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Healthy river ecosystems are conducive to the sustainable use of water resources. As the climate change and human activities become more intense, the integrity of such ecosystems is seriously threatened. The maintenance of enough environmental flow (e‐flow) for river ecosystems is the most effective way of protecting river health. Accurately and reasonably estimating e‐flow is essential for river health maintenance. So far, few methods can quantitatively verify the e‐flow results calculated by various e‐flow methods, which reduces the success rate of ecological restoration projects on a global scale. Therefore, the Tennant method, wetted perimeter (WP) method, and adapted ecological hydraulic radius (AEHRA) method have been recognized by many scholars. The article used the combination of these three methods to calculate the e‐flow and its meeting rate (actual flow/e‐flow) and formed a new framework to verify e‐flow results. First, the Shannon diversity index, index of biological integrity (IBI), and river health index are used to evaluate river health status. Then, we studied the relationship between e‐flow meeting rates and three indices using field monitoring data on hydrology, water quality, and biological communities. Finally, we investigated the effects of different e‐flow calculation methods on river health. Results show that the e‐flow in the centre of the study area calculated by the Tennant and WP methods are relatively high (48.33–317 m3/s), whereas lower e‐flow (0.03–21 m3/s) are observed in its southern and northern parts. The e‐flow in the southern mountain area calculated by the AEHRA method (1.42 m3/s) is higher than those in the other regions determined by the same method. Furthermore, the highest values of river health—the Shannon diversity index (3.19), IBI (67.47), and river health index (0.65)—appear in mountainous areas that are less affected by human activities. The lowest values appear in the urban areas with river health values (0.97, 14.54, and 0.45, respectively) with high population density. For the river health scores, the values of the Shannon diversity index and IBI first increase and then decrease with the increase in the e‐flow meeting rates. The values of river health index continuously increase with the increase in the WP‐ and AEHRA‐calculated e‐flow meeting rate and fluctuate with the increase of Tennant‐calculated e‐flow meeting rate. We concluded that the adoption of WP‐ and AEHRA‐calculated e‐flow values can maintain the health of river ecosystems with a certain degree of pollution, whereas the Tennant‐calculated e‐flow can only ensure the health of river ecosystems with little pollution, the reason for which is the pollution‐affected relationships between river biota communities and stream flows, and the Tennant method uses historical stream flow records to calculate e‐flow. WP and AEHRA methods, on the other hand, do not rely on flow records. The methodologies and results can provide a scientific basis for the selection of suitable e‐flow methods and therefore facilitate the projects for ecological river restoration.

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Predictive mapping of the biotic condition of conterminous U.S. rivers and streams

Hill

Fox

Leibowitz

et al. 2017

Ecological Applications

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Understanding and mapping the spatial variation in stream biological condition could provide an important tool for conservation, assessment, and restoration of stream ecosystems. The USEPA's 2008-2009 National Rivers and Streams Assessment (NRSA) summarizes the percentage of stream lengths within the conterminous United States that are in good, fair, or poor biological condition based on a multimetric index of benthic invertebrate assemblages. However, condition is usually summarized at regional or national scales, and these assessments do not provide substantial insight into the spatial distribution of conditions at unsampled locations. We used random forests to model and predict the probable condition of several million kilometers of streams across the conterminous United States based on nearby and upstream landscape features, including human-related alterations to watersheds. To do so, we linked NRSA sample sites to the USEPA's StreamCat Dataset; a database of several hundred landscape metrics for all 1:100,000-scale streams and their associated watersheds within the conterminous United States. The StreamCat data provided geospatial indicators of nearby and upstream land use, land cover, climate, and other landscape features for modeling. Nationally, the model correctly predicted the biological condition class of 75% of NRSA sites. Although model evaluations suggested good discrimination among condition classes, we present maps as predicted probabilities of good condition, given upstream and nearby landscape settings. Inversely, the maps can be interpreted as the probability of a stream being in poor condition, given human-related watershed alterations. These predictions are available for download from the USEPA's StreamCat website. Finally, we illustrate how these predictions could be used to prioritize streams for conservation or restoration.

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Integrative assessment of Western Himalayas streams using multimeric index

Cited by 6 publications

References 29 publications

Assessing the causal relationships of ecological integrity: a re‐evaluation of Karr's iconic Index of Biotic Integrity

Assessing the causal relationships of ecological integrity: a re‐evaluation of Karr's iconic Index of Biotic Integrity

Standards for environmental flow verification

Predictive mapping of the biotic condition of conterminous U.S. rivers and streams

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