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Biological Connectivity in the Mesoamerican Region

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Biological Connectivity in the Mesoamerican Region Biological Connectivity in the Mesoamerican Region Claire B. Paris1 and Laurent M. Chérubin2 University of Miami Figure 9- - CDM Plume extent on November 14, 2000 Rosenstiel School of Marine and Atmospheric Science Introduction: Most benthic species, including coral reef organisms, have pelagic larval stages. Thus, species that are typically sedentary are potentially able to travel large distances during the pelagic larval phase as currents transport them. Dispersal, or movement away from the birthplace during larval life, defines both the species biogeography (e.g. maximum dispersal distance) and the connectivity of geographically separated populations. In this latter case, sufficient number of larvae may arrive to replenish the ‘downstream’ population. In effect, neighbouring breeding populations may be connected by the exchange of larvae, forming a network of connected single species populations, or a Metapopulation. Some populations may act as major sources, some may only be sinks, and others may be important corridors (or stepping stones). It becomes evident that knowledge of the spatial structure of the network is essential for conservation issues, and that management approaches should be based on sound knowledge of the spatial scales at which local populations are interconnected. Measuring connectivity is technically difficult for several reasons: (1) marine organisms have a wide range of larval lives from short (e.g. a couple of days for brooding corals) to long (e.g. up 9 months for spiny lobsters), (2) larvae are too small to be acoustically tagged and tracked, and (3) transport is a complex product of dispersal by currents and active movement of individual larvae. Actually, transport is strongly influenced by patterns of water movement, but larvae are rarely passive and their behavior is 1 Division of Applied Marine Physics 2 Division of Meteorology and Physical Oceanography made more complicated by the fact that it changes during development and growth (ontogeny). Larvae can sense their surroundings and swim in responses to cues, sometimes surprisingly well. While hydrodynamic models have progressed greatly, they are still limited when it comes to modeling water flow in close proximity of complex topography, especially in shallow banks and on the reef break. Dispersing larvae begin and end their journey precisely in such complex coastal boundary layers. Therefore, measuring connectivity requires field observations that need to be done over relatively large regions to encompass the potential extent of larval transport, and timed to coincide with critical biological events such as spawning pulses. The precision of these measurements increases from the integration of various approaches (plankton hauls, population genetics, otolith trace-element chemistry, numerical modeling) that can give independent estimates of the dispersal distances for various species. This work is based solely on numerical simulations, using a coupled biological and physical model (Biophysical Offline Larval Tracking System, BOLTS) that we developed at RSMAS to estimate population connectivity (Cowen et al. 2006) and zoogeographical barriers to dispersal (Baums et al. 2006) in the MesoAmerican Region (MAR). In the first part of this report the ocean circulation and biophysical models are described. Then the biological connectivity results for a series of target species in are presented. Finally limitations to the interpretation of the model results are stated. Circulation Modeling The University of Miami adapted a circulation model to examine buoyant matter3 (BM) and population connectivity along the MAR. The Regional Ocean Modeling System (ROMS) provides the spatial and time evolution of circulation and passive transport of river discharge in the vicinity of the reef ecosystems along the coasts of Mexico, Belize, Guatemala, and Honduras. The model includes the barrier reef, reef lagoon, and adjacent oceanic waters, as well as bottom topography (bathymetry) at 1 km resolution. The horizontal resolution of the simulation is 2km (grid cell size). Both the state of the ocean (temperature, salinity, currents, tides) and the surface fluxes (wind, rain, solar, radiative heat fluxes) are accounted for in the model simulation of oceanic and coastal waters. They were taken from the Levitus ocean4 and atmospheric5 climatology, which provides monthly averages for a year. Therefore, the model produces a climatology of the circulation in the MAR. Four numerical simulations were done to understand the response of the ocean to the river run-off forcing (WRI-sponsored part of the project). The first simulation, forced by current year river run-off was designed to spin up the model both from a circulation point of view and from a sediment point of view. The model reached its buoyant transport equilibrium in winter of the second and third year. Therefore all the other simulations were started at the end of that second year. Further simulation addressed land-use sustainability scenario 3 Refer to the definition given in the Technical notes 4 http://ingrid.ldeo.columbia.edu/SOURCES/.LEVITUS94/ 5 http://icoads.noaa.gov/status.html Table 1: Time table of the numerical model runs Run 09/Y1-09/Y2 10/Y2-04/Y3 05/Y3-12/Y3 1- Regular X X X 2 - Sustainability X X 3 - Keith X 4 - Mitch X and the changes following hurricanes Keith and Mitch landfall during the second model year (Table 1). Results of the circulation in the MAR are detailed in Cherubin et al. (2008). Biophysical Coupled Connectivity Model Aim: The nature Conservancy has data on fish spawning sites for the Mesoamerican Region (Mexico, Belize, Honduras, and Guatemala). The aim of this study is to examine larval dispersal and map the larval connectivity between breeding populations, under undisturbed conditions (i.e. without taking into account exploitation and habitat health), using a range of physical (ocean circulation, geomorphology), and biological (adult spawning strategy, larval traits, coral cover etc) factors. Management factors such as presence of MPA are unaccounted for. We use a Bio-physical Offline Larval Tracking System (BOLTS), composed of several standalone code units coupled in a parallel environment (Paris et al. 2007). BOLTS inputs offline Eulerian fields and statistics from the hydrodynamic data at each time-step to gain computational speed for high throughput and allow iterative runs for efficient simulations of dispersing larvae. This model produces probabilistic simulations of both larval trajectories and transition probability matrices (or connectivity matrices) using an iterative stochastic approach to ensure accuracy of the predictions. The general algorithm for the BOLTS code consists of several steps: (1) initialization reads the grid coordinate of an Ocean General Circulation Model (OGCM, e.g. ROMS or HYCOM) and spatially explicit habitat (e.g. GIS module or reef polygons, Fig.1) and population information (e.g. population size, spawning frequency and seasonality); (2) advancing individual particles using the OGCM output frequency, advection due to mean currents, and dispersion due to turbulence; and (3) imposing individual larval behavior (e.g. ontogenetic vertical migration, OVM) and mortality rate as stochastic processes. Finally, if a larva is active (e.g. depending on the pre- competence period input parameter) its location is checked to verify if it falls within one of the reef polygon. If a larva falls within a reef polygon, it is assumed to have successfully recruited and the time and day and location of recruitment are saved. Otherwise, the larval dispersion is continued until it is recruited or the model is integrated for the number of days specified by the pelagic larval duration (PLD) or maximum competency period. Complex, multi-stage modeling should be validated at every possible stage of the analysis. This analysis serves to integrate a wide range of data, and adapt modeling tools for an innovative, region wide analysis. Where possible, data from published sources or proxy indicators derived from remote sensing were used to calibrate and validate model results (Cherubin et al. 2008). An important aspect of the project, however, is to provide these modeling tools to partners in the MAR region, so that they might apply to tools at higher resolution and then use local data to initially calibrate, and later validate model results. The region-wide results presented in this report should be considered preliminary and indicative of overall patterns of biological connectivity across the region. Figure 1. GIS Module: (A) Satellite image of fish spawning aggregations in Belize (TNC) and (B) Satellite-derived reef polygons for the MAR – polygons are 10 km reef segments, buffered by a 5-18 km sensory zone, depending on the species modeled. The buffer represents the distance from which larvae can sense and swim towards their preferred settlement habitat. Data: Connectivity is measured by the probability that particles (larvae) arrive within a given area (polygon) after a simultaneous release from all areas within the region. This value will include particles that are self-recruited (originating from the same area) and subsidies (originating from other areas). These components will be represented separately to indicate source and sink areas. Hence for each polygon we will generate two values for each larval strategy – number of self-recruited particles and number of subsidised particles. The origin of the source of the larvae to each of the reef is estimated by the use of a
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