How to choose? A bioeconomic model for optimizing river barrier mitigation actions

King, Steve; O’Hanley, J.R.; Fraser, Iain

doi:10.1016/j.ecolecon.2020.106892

Cited by 4 publications

(2 citation statements)

References 56 publications

(73 reference statements)

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“…If left uncorrected, these efforts are unlikely to achieve free-flowing river targets. Incomplete barrier data can still be useful to plan river restoration programs, but only if the subjacent biases are known and can be accounted for explicitly [2,16,18,19].…”

Section: Introductionmentioning

confidence: 99%

Modelling remote barrier detection to achieve free-flowing river targets

Parks,

Garcia de Leaniz,

Jones

et al. 2024

Environ. Res. Lett.

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Fragmentation caused by artificial barriers is one of the main stressors of rivers worldwide. However, many barrier inventories only record large barriers, which underestimates barrier numbers, and hence fragmentation. Corrected barrier numbers can be obtained via river walkovers, but these are costly and time consuming. We assessed the performance of remote sensing as an alternative to river walkovers for barrier discovery by comparing the number and location of barriers detected in the field with those detected using Google Earth imagery. Only 56% of known barriers could be detected remotely, but machine learning models predicted the likelihood of remote detection with 62-65% accuracy. Barriers located downstream were twice as likely to be detected remotely than those in the headwaters, the probability of detection diminishing by 3-4% for every decrease in Strahler stream order and for every 10km increase in distance from the river mouth. Barriers located in forested reaches were 35% less likely to be detected than those in open reaches. Observer skills also affected the ability to locate barriers remotely and detection rate varied by 11% between experienced and less experienced observers, suggesting that training might improve barrier detection. Our findings have implications for estimates of river fragmentation because they show that the most under-represented structures in barrier inventories, i.e. small barriers located in forested headwaters, are unlikely to be detected remotely. Although remote sensing cannot fully replace ‘boots on the ground’ field surveys for filling barrier data gaps, it can reduce the field work necessary to improve barrier inventories and help inform optimal strategies for barrier removal under data-poor scenarios.

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Section: Introductionmentioning

confidence: 99%

Modelling remote barrier detection to achieve free-flowing river targets

Parks,

Garcia de Leaniz,

Jones

et al. 2024

Environ. Res. Lett.

View full text Add to dashboard Cite

show abstract

“…Approaches that use generic structural connectivity measures (i.e., total accessible stream network length) as proxies for habitat gain result in less efficient, more costly solutions (Sethi et al, 2017;Rodeles et al, 2020), prompting calls to move towards functional connectivity measures in dam prioritizations (Branco et al, 2014). Increasingly, prioritizations have utilized functional connectivity measures representing fish diversity, richness, populations, and life history traits (Erős et al, 2018;Ioannidou and O'Hanley, 2019;McManamay et al, 2019;Kemp et al, 2020, King et al, 2021, but attempts to address functional connectivity in dam prioritizations incorporating habitat suitability for individual species have often relied on species ranges. However, relying solely on species range data can result in large potential commission errors and inadequate estimates of habitat overlap with invasive species, as all stream habitat accessible within a species' range is treated as equally suitable (McKay et al, 2017) irrespective of differences in actual habitat quality of different stream segments.…”

Section: Introductionmentioning

confidence: 99%