Individual, population, and ecosystem effects of hypoxia on a dominant benthic bivalve in Chesapeake Bay

Hypoxia is an environmental stressor that affects abundance, biomass,diversity, and ecosystem function of benthic assemblages worldwide, yet its collective impact at individual, population, and ecosystem levels has rarely been investigated. We examined the effects of hypoxia on the biomass-dominant clam,Macoma balthica, in the York and Rappahannock Rivers (Chesapeake Bay, USA). We (1) surveyed the M. balthica populationsin both rivers in 2003 and 2004, (2) determined the effects of low dissolved oxygen (DO) on M.balthica fecundity in a laboratory experiment, and (3) employed a predator-exclusion fieldexperiment to establish the effects of hypoxia and prey density on predation upon M. balthica.The resultant data were used to parameterize a matrix model, which was analyzed to define potential effects of hypoxia at the population level. In both rivers, hypoxia decreased individual clam growth and caused local extinction of populations. Hypoxia reduced egg production of M. balthicaby 40%and increased protein investment per egg. In the predator-exclusion field experiment, hypoxia magnified predation rates threefold and altered the functional response of predators toM. balthicafrom a stabilizing type III functional response to a destabilizing type II functional response. In a density-independent matrix model, hypoxia resulted in coupled source–sink metapopulation dynamics, with hypoxic areas acting as black-hole sinks. Increases in the spatial and temporal extent of hypoxia caused the populations to decline toward extinction. In a second model that incorporated density dependence, under mild hypoxic conditions trophic transfer from M. balthica to predators increased, but at increased spatial or temporal extent of hypoxia trophic transfer decreased. The major declinein trophic transfer to predators under severe hypoxia resulted from diversion of M. balthica biomass into the microbial loop. Our model predicted that there are multiple stable states forM. balthic apopulations (high and very low densities), such that the saddle point (threshold at which the population switches from one state to the other) increased and resilience decreased with the spatial extent of hypoxia. We underscore how effects of a stressor at the individual level can combine to have substantial population and ecosystem-level effect

Nitrogen reductions have decreased hypoxia in the Chesapeake Bay: Evidence from empirical and numerical modeling

Seasonal hypoxia is a characteristic feature of the Chesapeake Bay due to anthropogenic nutrient input from agriculture and urbanization throughout the watershed. Although coordinated management efforts since 1985 have reduced nutrient inputs to the Bay, oxygen concentrations at depth in the summer still frequently fail to meet water quality standards that have been set to protect critical estuarine living resources. To quantify the impact of watershed nitrogen reductions on Bay hypoxia during a recent period including both average discharge and extremely wet years (2016–2019), this study employed both statistical and three-dimensional (3-D) numerical modeling analyses. Numerical model results suggest that if the nitrogen reductions since 1985 had not occurred, annual hypoxic volumes (O2 \u3c 3 mg L−1) would have been ~50–120% greater during the average discharge years of 2016–2017 and ~20–50% greater during the wet years of 2018–2019. The effect was even greater for O2 \u3c 1 mg L−1, where annual volumes would have been ~80–280% greater in 2016–2017 and ~30–100% greater in 2018–2019. These results were supported by statistical analysis of empirical data, though the magnitude of improvement due to nitrogen reductions was greater in the numerical modeling results than in the statistical analysis. This discrepancy is largely accounted for by warming in the Bay that has exacerbated hypoxia and offset roughly 6–34% of the improvement from nitrogen reductions. Although these results may reassure policymakers and stakeholders that their efforts to reduce hypoxia have improved ecosystem health in the Bay, they also indicate that greater reductions are needed to counteract the ever-increasing impacts of climate change

Mass migration of juvenile Queen Conch (Strombus gigas) in the Bahamas

We summarize the available information for a mass migration of juvenile queen conch in the Bahamas. The migration was observed from April through June, 1987 over a large seagrass meadow and adjacent sand-algal plain at 1-5 m depths. Component aggregations ranged from 40-190 m in length and 1-6 m in width, with a maximum density of 319 conch/m2. Migrants averaged 101 mm in shell length, which ranged from 67-145 mm, and were of a similar size as non-migrants in the area The migration was directional towards ebb tidal flow and moved approximately 250 m, at rates of 2.7 - 4.8 m/d, from April until its dissociation in June. Given the characteristics of the migrants and aggregations, we conclude that a key function of the mass migration was as a dispersal mechanism for asynchronously emerged l+ year class of juvenile queen conch; other potential functions include reduction of predation-induced mortality and efficient utilization of food resources

Valuing Ecosystem Services: Oysters, Denitrification, and Nutrient Trading Programs

Maryland, Pennsylvania, Virginia, and West Virginia have all developed nutrient trading programs to defray the cost of achieving mandated nitrogen load reductions in Chesapeake Bay, and there is increasing interest in the role oysters can play in generating credits. A number of bioeconomic models highlight the impact these credits have in optimizing oyster harvest rates, but all overlook a major limiting factor in oyster population dynamics: oyster shell is an oyster’s preferred settling medium. Harvest thus impacts oyster productivity through the removal of both extant oysters and the future shell habitat. This is extremely important given that the removal of shell and oyster meat is a major channel by which nutrient credits could be generated. Further, recent research suggests that multiple oyster reef equilibria exist, and reef height determines the trajectory of oyster population change. In this research we couple a biological model of an oyster population, including shell dynamics, to a value function and analyze optimal oyster harvest regimes. The value function incorporates both the oyster harvesting profits and the value of an oyster reefs’ nutrient sequestration and denitrification. We then maximize the net present value of the oyster reef, using numerical dynamic programming and simulation techniques for a reasonable range of biological and economic parameters, to provide policy guidance on the trade-off between harvest, sequestration and denitrification services. Results indicate that optimal harvest rates are more sensitive to variability in the biological rather than economic parameters, although some level of harvest is almost always optimal.KEYWORDS: Multiple Uses, Fisheries economics, Ecosystem Service

Supplement 1. MATLAB function files for modeling Macoma balthica populations, and trophic transfer under normoxic and hypoxic conditions.

<h2>File List</h2><div> <p><a href="MbPopModel.m">MbPopModel.m</a> (MD5: fbf47bb585a7f041100da16138dc8ab0)</p> <p><a href="Pop_and_Pred.m">Pop_and_Pred.m</a> (MD5: 435f0942c819b03520e41bea6ec7756e)</p> <p><a href="dA.m">dA.m</a> (MD5: 67e503d79e2373c3756d7eb322ae4a84)</p> <p><a href="typeIII.m">typeIII.m</a> (MD5: 3664dfa10b84d28960b8bd952e66e058)</p> <p><a href="typeIIIH.m">typeIIIH.m</a> (MD5: 7cc47c1e7de06bbf7fb824bcdb03b6e8)</p> <p><a href="typeIII_P.m">typeIII_P.m</a> (MD5: dc445d6b130c070b8f73ffbb9779b08f)</p> <p><a href="typeIIIH_P.m">typeIIIH_P.m</a> (MD5: f5cf34d4a88340c688394a1be50f9ad7)</p> </div><h2>Description</h2><div> <p><b>MbPopModel.m-</b> This MATLAB function will model the population of <i>Macoma balthica</i> over a user specified length of time. Required sub-functions that are called within this function are dA.m, which creates a population projection matrix using input parameters and tyepIII.m and typeIIIH.m which are a series of linked ordinary differential equations describing the population dynamics during the summer in normoxic and hypoxic areas of the river.</p> <p>The three inputs to the function are:<br> param- A vector with five parameters: <i>MN</i>, <i>MH</i>, <i>RH</i>, <i>RN</i>, <i>d</i><br> Where <i>M</i> is the proportion of the juvenile population that reproduces in their first year, and <i>R</i> is the number of recruits produced by each female, and d is the areal proportion of the river that remains normoxic.</p> <p>Inits(<i>JN</i>, <i>AN</i>, <i>JH</i>, <i>AH</i>)- A vector with the initial population density of the Juveniles (<i>J</i>), and Adults (<i>A</i>) in the normoxic (subscript <i>N</i>) and hypoxic (subscript <i>H</i>).</p> <p>J- the number of years the model is to be run for.</p> <p>Base parameter estimates used in the paper are:</p> <blockquote> -- TABLE: Please see in attached file. -- </blockquote> <p> </p> <p><b>Pop_and_Pred.m</b>- This MATLAB function will model the population (output <i>pop</i>) of <i>Macoma balthica</i> and provide an annual estimate of the biomass (output <i>B</i>) and number (output <i>N</i>) of clams consumed by blue crabs (<i>Callinectes sapidus</i>) and those that suffer non-predatory mortality. Required sub-functions that are called within this function are dA.m, which creates a population projection matrix using input parameters and tyepIIIP.m and typeIIIH_P.m which are a series of linked ordinary differential equations describing the population dynamics during the summer in normoxic and hypoxic areas of the river.</p> <p>The four inputs to the function are:<br> Mparam- A vector with five parameters: <i>MN</i>, <i>MH</i>, <i>RH</i>, <i>RN</i>, <i>d</i><br> Where <i>M</i> is the proportion of the juvenile population that reproduces in their first year, and <i>R</i> is the number of recruits produced by each female, and d is the areal proportion of the river that remains normoxic.</p> <p>Param- A vector with three parameters: <i>tH</i>, <i>mH</i>,<i> P</i><br> Where <i>tH</i> is the duration of hypoxia in days, <i>mH</i> is the non-predatory mortality rate in hypoxic areas and <i>P</i> is the proportional increase in predation under hypoxic conditions.</p> p>Inits(<i>JN</i>, <i>AN</i>, <i>JH</i>, <i>AH</i>)- A vector with the initial population density of the Juveniles (<i>J</i>), and Adults (<i>A</i>) in the normoxic (subscript <i>N</i>) and hypoxic (subscript <i>H</i>).<p></p> <p>J- the number of years the model is to be run for.</p> <p>Base parameter estimates used in the paper are:</p> <blockquote> -- TABLE: Please see in attached file. -- </blockquote> <p> </p> <p><b>dA.m</b>- Sub-function of MbPopModel and Pop_and_Pred that creates a population projection matrix using input parameters.</p> <p><b>typeIII</b>- Sub-function of MbPopModel that is a series of linked ordinary differential equations describing the population dynamics during the summer in normoxic areas of the river.</p> <p><b>typeIIIH</b>- Sub-function of MbPopModel that is a series of linked ordinary differential equations describing the population dynamics during the summer in hypoxic areas of the river.</p> <p><b>typeIII_P</b>- Sub-function of Pop_and_Pred that is a series of linked ordinary differential equations describing the population dynamics and predation during the summer in normoxic areas of the river.</p> <p><b>typeIIIH_P</b>- Sub-function of Pop_and_Pred that is a series of linked ordinary differential equations describing the population dynamics and predation during the summer in hypoxic areas of the river.</p> </div