assemblages [150–152]. One emerging strategy is in the use of prokaryote assemblages, which has historically been limited because many prokaryotes are not
readily cultured in the laboratory. However, emerging technologies that allow for
quantification of assemblage composition through DNA sequencing have largely
removed this limitation, making prokaryote assemblage assessments an emerging
new option for bioassessment of aquatic systems [152, 153]. Rapidly evolving
DNA sequencing methods have the potential to greatly enhance not only
bioassessments using prokaryotes but also those using assemblages that have
traditionally been evaluated by identification of specimens based on morphological
characteristics [154–157].
Regardless of the assemblage type chosen or the methods used for identifying
taxa in the assemblage, the most challenging aspect of bioassessments has been, and
remains, the difficulty in separating environmental effects on assemblages that are
the result of naturally varying factors such as climate and geology from those
caused by anthropogenic factors. The use of the RCA, coupled with advanced
predictive modeling methods such as machine learning techniques, has enhanced
our ability to predict how assemblages should vary based on natural environmental
factors. Such enhanced predictive power should ultimately allow for more accurate
determination of assemblage variation patterns that indicate impairment. Despite
these advancements, predictive modeling and the use of the RCA are greatly
confounded by the lack of suitable reference sites in many regions. To this end,
alternative strategies that employ both impaired and reference sites to derive
expected reference conditions have been proposed [118–121] and warrant further
evaluation to determine their widespread applicability. Because of the scarcity of
reference sites in many regions and the high potential for complex interactions
between natural environmental factors and stressors, the development of additional
data-efficient methods for predicting expected assemblages under unimpaired conditions and for quantifying deviations from these expectations is much needed.
An additional challenge for contemporary bioassessment programs is the
shifting baseline syndrome (sensu Hawkins et al. [43]) wherein future climate
change is likely to alter temperature and precipitation regimes globally, thus
changing the assemblage compositions that might reasonably be expected under
minimally impaired conditions. To meet this challenge, spatially and temporally
extensive monitoring is essential to derive realistic reference conditions. Several
large-scale assessment programs have been recently developed, such as the EU
Water Framework Directive, the US Geological Survey’s National Water Quality
Assessment program, the US EPA’s National Aquatic Resources Survey, the US
National Science Foundation’s National Ecological Observatory Network, and the
Canadian Biological Monitoring network. These programs include rigorous and
thoroughly documented bioassessment protocols focused on monitoring aquatic
assemblages over large spatial and long temporal scales. Data produced by these
important programs will enhance our ability to overcome the inherent challenges in
evaluating ecological integrity when least-impaired reference conditions are rare,
highly variable among regions, and changing in response to global climate change.
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A.L. Garey and L.A. Smock
readily cultured in the laboratory. However, emerging technologies that allow for
quantification of assemblage composition through DNA sequencing have largely
removed this limitation, making prokaryote assemblage assessments an emerging
new option for bioassessment of aquatic systems [152, 153]. Rapidly evolving
DNA sequencing methods have the potential to greatly enhance not only
bioassessments using prokaryotes but also those using assemblages that have
traditionally been evaluated by identification of specimens based on morphological
characteristics [154–157].
Regardless of the assemblage type chosen or the methods used for identifying
taxa in the assemblage, the most challenging aspect of bioassessments has been, and
remains, the difficulty in separating environmental effects on assemblages that are
the result of naturally varying factors such as climate and geology from those
caused by anthropogenic factors. The use of the RCA, coupled with advanced
predictive modeling methods such as machine learning techniques, has enhanced
our ability to predict how assemblages should vary based on natural environmental
factors. Such enhanced predictive power should ultimately allow for more accurate
determination of assemblage variation patterns that indicate impairment. Despite
these advancements, predictive modeling and the use of the RCA are greatly
confounded by the lack of suitable reference sites in many regions. To this end,
alternative strategies that employ both impaired and reference sites to derive
expected reference conditions have been proposed [118–121] and warrant further
evaluation to determine their widespread applicability. Because of the scarcity of
reference sites in many regions and the high potential for complex interactions
between natural environmental factors and stressors, the development of additional
data-efficient methods for predicting expected assemblages under unimpaired conditions and for quantifying deviations from these expectations is much needed.
An additional challenge for contemporary bioassessment programs is the
shifting baseline syndrome (sensu Hawkins et al. [43]) wherein future climate
change is likely to alter temperature and precipitation regimes globally, thus
changing the assemblage compositions that might reasonably be expected under
minimally impaired conditions. To meet this challenge, spatially and temporally
extensive monitoring is essential to derive realistic reference conditions. Several
large-scale assessment programs have been recently developed, such as the EU
Water Framework Directive, the US Geological Survey’s National Water Quality
Assessment program, the US EPA’s National Aquatic Resources Survey, the US
National Science Foundation’s National Ecological Observatory Network, and the
Canadian Biological Monitoring network. These programs include rigorous and
thoroughly documented bioassessment protocols focused on monitoring aquatic
assemblages over large spatial and long temporal scales. Data produced by these
important programs will enhance our ability to overcome the inherent challenges in
evaluating ecological integrity when least-impaired reference conditions are rare,
highly variable among regions, and changing in response to global climate change.
258
A.L. Garey and L.A. Smock