Different Conceptualizations of River Basins to Inform Management of Environmental Flows

Environmental flows are a critical tool for addressing ecological degradation of river systems brought about by increasing demand for limited water resources. The importance of basin scale management of environmental flows has long been recognized as necessary if managers are to achieve social, economic, and environmental objectives. The challenges in managing environmental flows are now emerging and include the time taken for changes to become manifest, uncertainty around large-scale responses to environmental flows and that most interventions take place at smaller scales. The purpose of this paper is to describe how conceptual models can be used to inform the development, and subsequent evaluation of ecological objectives for environmental flows at the basin scale. Objective setting is the key initial step in environmental flow planning and subsequently provides a foundation for effective adaptive management. We use the implementation of the Basin Plan in Australia's Murray-Darling Basin (MDB) as an example of the role of conceptual models in the development of environmental flow objectives and subsequent development of intervention monitoring and evaluation, key steps in the adaptive management of environmental flows. The implementation of the Basin Plan was based on the best science available at the time, however, this was focused on ecosystem responses to environmental flows. The monitoring has started to reveal that limitations in our conceptualization of the basin may reduce the likelihood of achieving of basin scale objectives. One of the strengths of the Basin Plan approach was that it included multiple conceptual models informing environmental flow management. The experience in the MDB suggests that the development of multiple conceptual models at the basin scale will help increase the likelihood that basin-scale objectives will be achieved

Physiological Trade-Offs Along a Fast-Slow Lifestyle Continuum in Fishes: What Do They Tell Us about Resistance and Resilience to Hypoxia?

<div><p>It has recently been suggested that general rules of change in ecological communities might be found through the development of functional relationships between species traits and performance. The physiological, behavioural and life-history traits of fishes are often organised along a fast-slow lifestyle continuum (FSLC). With respect to resistance (capacity for population to resist change) and resilience (capacity for population to recover from change) to environmental hypoxia, the literature suggests that traits enhancing resilience may come at the expense of traits promoting resistance to hypoxia; a trade-off may exist. Here I test whether three fishes occupying different positions along the FSLC trade-off resistance and resilience to environmental hypoxia. Static respirometry experiments were used to determine resistance, as measured by critical oxygen tension (P_crit), and capacity for (RC) and magnitude of metabolic reduction (RM). Swimming respirometry experiments were used to determine aspects of resilience: critical (<i>U</i>_crit) and optimal swimming speed (<i>U</i>_opt), and optimal cost of transport (COT_opt). Results pertaining to metabolic reduction suggest a resistance gradient across species described by the inequality <i>Melanotaenia fluviatilis</i> (fast lifestyle) < <i>Hypseleotris</i> sp. (intermediate lifestyle) < <i>Mogurnda adspersa</i> (slow lifestyle). The U_crit and COT_opt data suggest a resilience gradient described by the reverse inequality, and so the experiments generally indicate that three fishes occupying different positions on the FSLC trade-off resistance and resilience to hypoxia. However, the scope of inferences that can be drawn from an individual study is narrow, and so steps towards general, trait-based rules of fish community change along environmental gradients are discussed.</p></div

Physiological resistance to hypoxia for three fishes occupying different positions along the fast-slow lifestyle continuum.

<p><i>Melanotaenia fluviatilis</i>, <i>Hypseleotris</i> sp. and <i>Mogurnda adspersa</i> have, respectively, a fast, intermediate and slow lifestyle (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.t001" target="_blank">Table 1</a>). (A) The gradient in standard (SMR), routine (RMR) and maximum (MMR) metabolic rates, as well as aerobic scope (AS). (B) Critical oxygen tensions (P_crits). (C) Magnitude of metabolic reduction. (D) Capacity for metabolic reduction (RC) is the logarithm of the ratio of two areas (<i>ln</i>(A_r/A_e)), where A_r is the area between either SMR (RC_SMR) or RMR (RC_RMR) and the depressed metabolic rate curves, during gradual hypoxia, and A_e is EPHOC. Means and single standard errors are presented in all plots. Sample sizes for <i>M</i>. <i>fluviatilis</i>, <i>Hypseleotris</i> and <i>M</i>. <i>adspersa</i> were, respectively: A: N = 16, 16, 12; B: N = 8, 9 and 8; C and D: N = 6, 3 and 3.</p

Illustrations of how P_crit and metabolic reduction capacity were calculated.

<p>A, B and C: Examples of changes in </p><p></p><p></p><p></p><p></p><p><mi>M</mi><mo>˙</mo></p><p></p><p>O<mn>2</mn></p><p></p><p></p><p></p><p></p><p></p> as a function of oxygen tension for individual <i>M</i>. <i>fluviatilis</i>, <i>Hypseleotris</i> and <i>M</i>. <i>adspersa</i> (respectively), demonstrating the abrupt decline in <p></p><p></p><p></p><p></p><p><mi>M</mi><mo>˙</mo></p><p></p><p>O<mn>2</mn></p><p></p><p></p><p></p><p></p><p></p> that defines P_crit. On each plot three lines are presented. The red line is the regression defining the oxyconformation zone of gradual hypoxia, while the orange line is the regression defining the oxyregulation zone during gradual hypoxia. Both the red and orange lines were determined using the algorithm of Yeager and Ultsch, and their point of intersection is one way of calculating P_crit (P_crit,YU). An alternative estimator of P_crit (P_crit,SMR) is the point of intersection of SMR (blue line) and the regression defining oxyconformation (red line). D. Reduction Capacity (RC_SMR in this case), was calculated as the logarithm of the ratio of two areas (<i>ln</i>(A_r/A_e)), where A_r is the area between the SMR and depressed metabolic rate curves, during gradual hypoxia, and A_e is excess post-hypoxic oxygen consumption (EPHOC). Two points of intersection between the SMR curve and the fitted spline curve, and two points of intersection between the RMR curve and the spline, define four times critical for determination of the integrals defining A_r and A_e: <i>t</i>_startR, <i>t</i>_finishR<i>t</i>_startE and <i>t</i>_finishE are, respectively, the times at which (a) metabolic reduction below SMR began; (b) metabolic reduction ceases; (c) EPHOC begins; (d) EPHOC finishes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#sec002" target="_blank">Materials and Methods</a>). (Data presented in D is from a 4.67 g <i>M</i>. <i>fluviatilis</i>).<p></p

Key statistics associated with the gross cost of transport function.

<p>Presented are the 95% confidence intervals of the fixed population parameters of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.e039" target="_blank">Eq 9</a>, as well as the coefficients of determination (R²) for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.e039" target="_blank">Eq 9</a> fitted to each of the three species tested. Statistics obtained using nonlinear mixed-effects regression to accommodate repeated measures of </p><p></p><p></p><p></p><p></p><p></p><p>M</p><mo>˙</mo><p></p><p></p><p></p><p></p><p>O</p><p>2</p><p></p><p></p><p></p><p></p><p></p> on individuals across velocities [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.ref074" target="_blank">74</a>]. Sample sizes for <i>M</i>. <i>fluviatilis</i>, <i>Hypseleotris</i> and <i>M</i>. <i>adspersa</i> were, respectively: N = 6, 7 and 6.<p></p><p>Key statistics associated with the gross cost of transport function.</p

Endurance swimming capacity of three fishes with different lifestyles.

<p><i>Melanotaenia fluviatilis</i>, <i>Hypseleotris</i> sp. and <i>Mogurnda adspersa</i> have, respectively, a fast, intermediate and slow lifestyle (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.t001" target="_blank">Table 1</a>). A. Critical swim velocities (U_crits). B. Mean optimal swimming speed (<i>U</i>_opt) in body lengths per second. (C) Mean gross cost of transport at <i>U</i>_opt. D. Gross cost of transport functions. Lines are modelled relationships between velocity and COT_gross using fixed effects estimates of parameters in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.e039" target="_blank">Eq 9</a> (estimates in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130303#pone.0130303.t002" target="_blank">Table 2</a>). Relationships between COT_gross and velocity are presented for each species up to their respective mean <i>U</i>_crit values. All error bars are a single standard error. Sample sizes for <i>M</i>. <i>fluviatilis</i>, <i>Hypseleotris</i> and <i>M</i>. <i>adspersa</i> were, respectively: N = 6, 7 and 6.</p

Contrasting fundamental and realized niches: two fishes with similar thermal performance curves occupy different thermal habitats

Human alteration of thermal regimes of freshwater ecosystems is creating an urgent need to understand how freshwater ectotherms will fare under different thermal futures. Two key questions are: 1) how well do the fundamental thermal niches of ectotherms map to their realized thermal niches, and 2) which axes of the fundamental thermal niche must be modeled to predict temperature-dependent fitness in real ecosystems? The first question is particularly challenging in riverine systems, where gradients in temperature are strongly confounded by gradients in other biotic and abiotic drivers. To address these questions, we compared the realized and fundamental thermal niches of 2 congeneric riverine fish: Gadopsis marmoratus and Gadopsis bispinosus. We characterized their realized thermal niches by examining their distributions in relation to environmental temperature at multiple scales. We characterized their fundamental thermal niches by doing laboratory experiments on the thermal sensitivity of swimming performance and metabolic rates, particularly aerobic scope. The distributions of the 2 species supported the idea that they have different realized thermal niches, with G. bispinosus occupying cooler habitats than G. marmoratus. Despite this, we detected no significant differences in the shapes of thermal performance curves defining 2 axes of their fundamental niches: swimming performance and aerobic scope. Our results suggest that either the distributions of these 2 species are driven by factors other than temperature or that swimming performance and aerobic scope were not suitable proxies of their fundamental thermal niches. Our study shows that modeling the thermal niches of ectotherms along the river continuum is not straightforward. If we are to forecast effects of thermal futures effectively and efficiently, we must do more to decipher the relative influence of temperature and other abiotic drivers on the fitness of riverine ectotherms

Effects of current and future climates on the growth dynamics and distributions of two riverine fishes

1. To facilitate conservation planning, there is a need for improved confidence in forecasts of climate change impacts on species distributions. Towards that end, there have been calls for the development of process‐based models to test hypotheses concerning the mechanisms by which temperature shapes distribution and to corroborate forecasts of correlative models. 2. Models of temperature‐dependent growth (TDG) were developed for two Australian riverine blackfishes with disjunct longitudinal distributions: Gadopsis marmoratus (occupies lower, warmer elevations) and Gadopsis bispinosus (occupies higher, cooler elevations). The models were used to (a) predict blackfish monthly and annual growth dynamics under current and future climate scenarios within different elevation bands of their current distribution, and (b) test the hypothesis that, under the current climate, the distributions of each species would be positively correlated with predicted TDG. 3. Increases in mean annual growth were forecast for both species under all warming scenarios, across all elevation bands. Both species currently occupy annual habitat temperatures below those optimal for growth. Under certain warming scenarios, the predicted increases in annual growth belie forecasts of within‐year dynamics that may interact with the phenology of blackfish to impair recruitment. 4. There was not a significant positive linear relationship between predicted TDG and observed abundance among river segments for either species. Both species were strongly under‐represented where annual growth rates were forecast to be optimal and over‐represented where growth rates were forecast to be intermediate. 5. Confidence in forecasts of climate change impacts based on correlative models will increase when those forecasts are consistent with a mechanistic understanding of how specific drivers (e.g. water temperature) affect processes (e.g. growth). This process‐based study revealed surprises concerning how future climates may affect fish growth dynamics, showing that although the blackfish distributions are correlated with temperature the temperature‐dependent mechanisms underpinning that correlation require further investigation

An Asymmetric Model of Heterozygote Advantage at Major Histocompatibility Complex Genes: Degenerate Pathogen Recognition and Intersection Advantage

We characterize the function of MHC molecules by the sets of pathogens that they recognize, which we call their “recognition sets.” Two features of the MHC–pathogen interaction may be important to the theory of polymorphism construction at MHC loci: First, there may be a large degree of overlap, or degeneracy, among the recognition sets of MHC molecules. Second, when infected with a pathogen, an MHC genotype may have a higher fitness if that pathogen belongs to the overlapping portion, or intersection, of the two recognition sets of the host, when compared with a genotype that contains that pathogen in only one of its recognition sets. We call this benefit “intersection advantage,” γ, and incorporate it, as well as the degree of recognition degeneracy, m, into a model of heterozygote advantage that utilizes a set-theoretic definition of fitness. Counterintuitively, we show that levels of polymorphism are positively related to m and that a high level of recognition degeneracy is necessary for polymorphism at MHC loci under heterozygote advantage. Increasing γ reduces levels of polymorphism considerably. Hence, if intersection advantage is significant for MHC genotypes, then heterozygote advantage may not explain the very high levels of polymorphism observed at MHC genes

Length-mass models for some common New Zealand littoral-benthic macroinvertebrates, with a note on within-taxon variability in parameter values among published models

Regression models are developed and presented to predict dry mass (mg) from two linear dimensions (mm) for 17 benthic macroinvertebrate taxa common to littoral zones of New Zealand lakes. We also provide regression models to predict body length from head capsule width for the major insect taxa. Dry mass was best explained as a power function of all linear dimensions: M = aL b .Parameters are presented in the log10‐transformed linear form of this power function. Body length was a simple linear function of head capsule width for all insect taxa, hence parameters for these models are presented as untransformed values. We also provide family level models for the Chironomidae, and compare our chironomid body length‐mass model with other published Chrionomidae length‐mass models. There was a very high degree of variability in parameter values among published length‐mass models for the family Chironomidae (mean coefficient of variation for mass at length = 148%). We discuss the potential causes and implications of this variability