Informed stakeholder support for managing invasive <i>Hydrilla verticillata</i> linked to wildlife deaths in a Southeastern reservoir

<p>Fouts KL, Poudyal, NC, Moore R, Herrin J, Wilde, SB. 2017. Informed stakeholder support for managing invasive <i>Hydrilla verticillata</i> linked to wildlife deaths in a Southeastern reservoir. Lake Reserve Manage. 00:1–10.</p> <p>Public opinion surveys prior to implementing management actions provide managing agencies with a detailed understanding of stakeholders' attitudes and help inform the general public on the complexity of potential management actions. Like many other Southeastern U.S. reservoirs, J. Strom Thurmond (JST), on the border of Georgia and South Carolina, has been infested with nonnative hydrilla (<i>Hydrilla verticillata</i>). Avian Vacuolar Myelinopathy (AVM), a fatal wildlife disease linked to a neurotoxic cyanobacterial species growing on hydrilla, has been documented on this 28,733-ha reservoir since 1998, when the hydrilla acreage first exceeded 350 ha. As of 2016, 90 bald eagle (<i>Haliaeetus leucocephalus</i>) mortalities and hundreds of waterfowl deaths have been attributed to AVM disease on JST. To assess and compare the diverse stakeholders' attitudes toward aquatic vegetation, knowledge of AVM, and support for management actions to remove hydrilla, a mail survey was conducted targeting various JST user groups (anglers, boaters, campers, waterfowl hunters, and shoreline property owners). Generally, respondents were overwhelmingly in favor of reducing hydrilla density on JST, but shoreline permit holders (homeowners) were significantly more supportive of hydrilla management than boaters. Similarly, all user groups supported management actions to remove aquatic vegetation, including stocking triploid sterile grass carp (<i>Ctenopharyngodon idella</i>). Support for removing hydrilla was found to be significantly higher among users knowledgeable of AVM, suggesting that outreach activities educating the public on the effects and prevention of the disease would help enhance stakeholder support for hydrilla removal and management in public reservoirs.</p

Common Ancestry Is a Poor Predictor of Competitive Traits in Freshwater Green Algae

<div><p>Phytoplankton species traits have been used to successfully predict the outcome of competition, but these traits are notoriously laborious to measure. If these traits display a phylogenetic signal, phylogenetic distance (PD) can be used as a proxy for trait variation. We provide the first investigation of the degree of phylogenetic signal in traits related to competition in freshwater green phytoplankton. We measured 17 traits related to competition and tested whether they displayed a phylogenetic signal across a molecular phylogeny of 59 species of green algae. We also assessed the fit of five models of trait evolution to trait variation across the phylogeny. There was no significant phylogenetic signal for 13 out of 17 ecological traits. For 7 traits, a non-phylogenetic model provided the best fit. For another 7 traits, a phylogenetic model was selected, but parameter values indicated that trait variation evolved recently, diminishing the importance of common ancestry. This study suggests that traits related to competition in freshwater green algae are not generally well-predicted by patterns of common ancestry. We discuss the mechanisms by which the link between phylogenetic distance and phenotypic differentiation may be broken.</p></div

Traitgrams indicating the relationship between each species' trait value (vertical axis on the right of each panel), and its phylogenetic position on the left.

<p><b>a)</b> Distribution of I* across the phylogeny. For this trait a WN model was supported, indicating a random distribution of traits across the phylogeny; <b>b)</b> Distribution of % C across the phylogeny, a WN model was supported, again indicating a random trait distribution; <b>c)</b> Distribution of log-transformed algal biovolume across the phylogeny, a δ model was supported with a δ value of 7.971, suggesting a very weak influence of ancestral branches on trait variation; and <b>d)</b> Distribution of % P across the phylogeny, a λ model was supported with a λ value of 0.171, indicating that a tree-transformation to a nearly star-like phylogeny was the best fit. The phylogeny in this figure is the phylogeny published in Alexandrou et al. 2015 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137085#pone.0137085.ref049" target="_blank">49</a>]. We have smoothed the branches of the phylogeny only for purposes of the illustration.</p

Trait of interest, best model, Akaike (AIC) weights and parameter estimates for models of trait evolution.

<p>BM = Brownian Motion model, OU = Ornstein-Uhlenbeck model, WN = White Noise model. Models were fitted to the trait data using the Geiger package and the fitcontinuous function in R. Algal traits are the same as in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137085#pone.0137085.t001" target="_blank">Table 1</a></b>. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137085#pone.0137085.g002" target="_blank">Fig 2</a> for traitgrams. The α parameter of the OU model, the δ parameter of the δ model and the λ parameter of the λ model are all also shown to allow interpretation of the model selections. In cases where a δ Model or a λ model received the greatest support from AIC weights, we tested whether the estimated parameter value was significantly different from a BM expectation by simulating 1,000 random walks of evolution. For each simulated walk, we then estimated δ and λ, creating sampling distributions of the parameters to determine the estimated parameter value from our data was outside of the 95% confidence interval for a random walk. Parameter values that are in bold are outside of the 95% confidence interval for a random walk of evolution (α CI = 0.00–3.16 (one-tailed), δ CI = 0.52–4.00 (two-tailed), λ = 0.87–1.00 (one-tailed)).</p