The Urban Naturalist: Observations of Nature Along the US Gulf Coast, Vol 2
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This simulation time was selected based on preliminary comparative tests; it assumes a phytoplankton growth rate of one division per day and represents a good compromise to appropriately resolve the scales of both physical transport and biological processes. In order to classify the origin zones of the water masses reaching LTER-MC, we identified eight main sectors in the GoN that radiated from the LTER-MC site based on specific geographic and environmental features urban areas, presence of harbours, etc.
Based on these sectors, we categorized the VPPs arriving at LTER-MC as originating from either waters close to the GoN coastline, offshore waters, or from both the directions at the same time hereinafter, coastal, offshore and mixed origin, respectively. This information ultimately revealed whether a given plankton community developed in that particular place or was transported there from elsewhere. The above-mentioned descriptors of the surface dynamics were then compared with salinity, chl a and phytoplankton diversity data recorded at LTER-MC to: i inter-validate the identification of surface water origin based on VPPs modelling with the ecological characterization of the reference station and ii identify the distinct states of the plankton system based on both physical and ecological proxies.
In order to assess the relative importance of the two main drivers, i. For each sequence, different phytoplankton samples were aggregated based on mutual similarity Q-mode , whereas species were aggregated based on shared trends over time R-mode analysis. The examples were selected from among sequences of persistent coastal water conditions or hydrographic changes.
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The aim was to distinguish sharp changes in phytoplankton composition caused by physical drivers e. The annual and seasonal analyses of surface currents based on HFR data are presented in Fig. The surface current fields were primarily forced by the local wind regime 23 , as typical for the GoN 10 , 14 , 23 , 24 , During the spring and autumn seasons, the directions of origin were more variable.
Each lobe represents the frequency with which the current flows from a given direction, whereas the angular regions separate data into current speeds. Azimuthal bins indicate the direction from which the current is coming. Created with Grapher Ver. It displays the percentage of occurrences of each VPP coming from the pre-defined eight sectors: this elaboration indicates that, during the year , the surface water and the plankton within detected at LTER-MC mainly comes from the coastal dominion.
As a complement to this analysis, we plotted the spatial distribution of the VPPs origin zones cumulative annual percentage Fig. This plot emphasized that, on a yearly basis, VPPs mostly originated from the northern sub-basin of the GoN i. This observation was consistent with a latitude-dependent surface circulation pattern described in a previous study 23 and is associated with the presence of basin- and sub-basin-scale structures of the flow field. The total number of particles for each bin was calculated cumulated over the entire set of model runs , and the relative percentage contribution was then estimated.
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For a proper visual representation, data were gridded using a minimum curvature method to generate the smoothest possible surface while at the same time honouring data at best. While the former elaborations indicated the geographic origin of VPPs, the T A analysis provided the temporal scales characterizing the process of their transport. Plots in Fig. These findings are similar to previous studies that reported how surface dynamics driven by different local and remote forcing affect the residence times of particles released in the neighbourhood of LTER-MC 24 , Bars represent VPPs coming from the coastal areas green bars or from offshore areas blue bars.
Black dots and error bars inside the figure indicate the average and standard deviations values for each season.
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Results of the above described Lagrangian analyses were compared with the ecological characterization of water masses at the reference site LTER-MC based on cluster analyses using the values of salinity and chl a in accordance with an earlier report The mismatches observed were more frequent for waters recognized as of offshore origin. In seven cases, backtracking indicated a mixed origin of surface waters; in six of them, salinity and chl a grouped in a cluster with parameter values intermediate between those typically scored for coastal and offshore waters.
Comparison among backtrack Lagrangian reconstruction and ecological analysis based on salinity and chlorophyll a data obtained through weekly sampling at LTER-MC. We integrated the information on the zones of origin of water masses i. In order to enhance the comparability among the above-mentioned variables characterized by fairly different numerical ranges , standardized data were used and combined in radar plots Fig.
From this dataset integration, four main states of the system based on physical and ecological proxies were identified Fig. Green phases Fig.
Opposite conditions were present during the blue phases Fig. Different modes of coupled physical and ecological functioning in the GoN. Radar plots were derived from standardized data for each variable. In addition the 7 mixed cases, where VPPs came to LTER-MC partly from the coast and partly from offshore, were all events of intrusion of offshore waters in the midst of long-lasting coastal phases Fig.
biosparyzaxco.ga We distinguished between type 1 and 2 mixtures Fig. In type 1 mixture Fig.
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In type 2 mixture Fig. This condition was determined by a stronger water advection from the outer sector of the GoN i. Type 2 mixture showed relatively lower chl a levels than type 1, due to the relatively lower contribution of coastal waters. As for single species dynamics, the switches to the green states were generally characterised by an increase of diatoms, among which the small-sized and non-colonial ones e. The mixed and blue states coincided with the decrease of all these diatoms, which were still found in low concentration though, but also of other taxa i.
However, possible changes in the community of undetermined small flagellates and naked dinoflagellates would not have been captured by the counts on fixed material. In fact, an abrupt change in the protist community was revealed through molecular methods in the course of a type 2 mixture detected in August Both mixture types Fig. This aspect is relevant, since it highlights the role of physical factors in enhancing phytoplankton diversity.
The mechanisms behind diversity enhancement could be at least two: either i advection and mixing of the coastal phytoplankton with species different from the resident ones, or ii intermediate physical disturbance, which relaxes inter-specific competition by limiting the resource exploitation by fewer dominant species 32 , Biological factors uncoupled from physical ones, such as life-cycle transitions, species-specific physiological performances and inter-specific interactions, add further intricacy to phytoplankton community dynamics.
In the following sections, we provided some selected examples of temporal changes in phytoplankton communities due to either biological or physical drivers, or to their interplay. We first zoomed into phytoplankton dynamics in two cases Fig. In such a context, and in the absence of intense offshore intrusions, biological processes would be dominant in driving phytoplankton dynamics.
Hierarchical clustering analysis Q- and R-mode showed changes in the whole phytoplankton community in the investigated period Fig. In the lowermost scheme of each panel, the scaled black circles showed the change in the ratio between phytoplankton diversity and chl a , which characterizes the change in species dominance along the time-period considered. Phytoplankton dynamics during dominant coastal phases in the GoN. Heat maps represent phytoplankton species abundances recorded at LTER-MC during time-periods indicated by dates at the bottom of each panel.
The colour of these latter dates either green, blue or red indicate the origin of water sampled either coastal, offshore or mixed, respectively defined via backtracking analysis see also Fig. Q-mode clustering of phytoplankton data was used to group different phytoplankton samples at consecutive sampling dates; R-mode clustering was used to group species-trends.
Numbers at clustering nodes are bootstrap values. The first example refers to the time period between March 25 and May 5 Fig. R-mode clustering showed two main species-clusters over time: one including species with apparently short-lived blooms i. The first cluster included mainly dinoflagellates, coccolithophores and other flagellates, while the second included mainly planktonic diatoms mostly Skeletonema and Thalassiosira spp. In the presence of persisting coastal waters conditions, the alternation of different species over time can be driven by stochastic biological factors and inter-specific relationships 36 such as competition, grazing or pathogens.
Furthermore, the brisk increase of diatoms after April 15 can be confidently linked to spore germination coupled with increased cell growth rates due to spring conditions, particularly favourable to opportunistic growth in diatoms 4 , The rise of diatoms brought an increase in phytoplankton biomass along with a reduction in diversity black-circles in Fig.
The second example of a biologically regulated coastal phase refers to the autumnal time period between September 29 and October 20 Fig. A large number of species displayed a strong successional signal, while other species, mainly diatoms, underwent evident biomass decay.
The most marked of such decays were those of the diatoms Pseudo-nitzschia cf. Diatom collapse drove a decrease in the total phytoplankton biomass that was accompanied by a conspicuous increase in diversity black-circles in Fig. As for the horizontal scale, the absence of transport of offshore waters towards the coast as clearly indicated by backtracking, Fig. In the specific context described above, the sudden decay of L.
However, other biological processes such as sinking of the whole population due to physiological changes or selective grazing cannot be ruled out. In the case of Pseudo-nitzschia cf. That sexual event was followed by a brisk decrease in the population, which is consistent with the arrest of vegetative growth following gametogenesis as demonstrated through modelling and laboratory experiments for Pseudo-nitzschia species 29 , These flushing events can advect offshore species and mix different communities.
We examined the impact of this flushing on coastal communities by illustrating three cases Fig. Phytoplankton dynamics during alternations between green and blue phases in the GoN. No Q-mode clustering is presented for c since samples did not group according to time i. In the first case June 9—July 21 , physical and biological drivers of phytoplankton dynamics coexist Fig.
During a long-lasting coastal phase June 9—30, Fig. On July 7, a flushing event Fig. The relatively small change in the community composition following the flushing event suggests that, during summer, a wide area of the GoN may be populated by one dominant phytoplankton community. As a consequence, the coastal phytoplankton community, previously transported offshore, may be retained in the outer GoN and eventually be transported back towards the coast, bearing a signal of dilution of the original coastal community.
Unlike in summer, during winter flushing waters even from closer offshore zones e. Surface water circulation in this season oscillates between two opposite scenarios, promoting a sharp alternation between the currents from coast and offshore sectors: SW winds sustain a coastward current field, thus favouring stagnation and retention of surface waters, while NE winds generate an intense coast-offshore jet, permitting a rapid renewal of coastal waters 23 , The situation is further complicated because these seasonally defined patterns are occasionally replaced by the formation of cyclonic and anti-cyclonic vortices induced by the Tyrrhenian Sea circulation 23 , Following a shift from the coastal to the offshore phase on December 21 Fig.
Unlike in summer, when a single community dominated the GoN, the shift between the green and blue phases during winter can mix at least two different communities with distinct features, one coastal and one offshore, the first showing higher biomass and lower diversity and the second showing the opposite features black circles in Fig. In some cases, phytoplankton community modifications induced by physical factors can be even more conspicuous than those described above. On the former date, offshore waters came from the nearest sector 7 causing a small change in the community composition and virtually no change in the ecological features like phytoplankton biomass and diversity black circles, Fig.
Conversely, flushing on August 11 determined a decrease in phytoplankton biomass due to strong dilution and an increase in diversity black circles in Fig.
A study based on high throughput sequencing and metabarcoding of protist communities revealed a similar case of sudden and complete replacement of the coastal community in August , which was also interpreted as the consequence of offshore water intrusion from the farthest sector of the GoN After these sudden changes, the community can however revert to a coastal phase that is more similar to the one preceding the flushing 31 , as also observed on August 18 in this study.
The approach adopted in this study allowed disentangling the respective roles of physical and biological forcings in driving phytoplankton dynamics in the GoN and lead to the following synthesis. In the latter cases, the farther the origin zone of offshore waters, the larger the modification induced in the phytoplankton community. Between these extremes, both biological and physical drivers interplay in cases of less intense horizontal mixing, involving waters originating from the offshore origin zone closer to the coast.
The alternation between coastal and offshore water masses promote phytoplankton diversity, because the dilution in the phytoplankton density may decrease the impact of the dominating species over the available resources, which is enhanced during prolonged coastal phases. Ultimately, such efforts may be useful in supporting decisions and solving problems in coastal management or in the study of harmful algal blooms.
An exhaustive technical description of the functioning of the HFR systems is available in previous publications 11 , The HF radar network in the GoN has been employed over the years to study both the surface currents 10 , 14 , 23 , 24 , 25 , 42 , 43 and the wave field Owing to the working frequency, the measured currents refer to a depth of 0. As HFR derived currents may be affected by different sources of error 45 , antenna pattern measurements were conducted routinely to correct currents measured in the GoN returning precise and accurate estimates of the surface fields 9 , Our analysis is focused on ; this year was selected as it presented a minimal number of gaps in terms of radar data density and associated weekly biological sampling.
In our study, the surface velocity fields detected by the HFR system between January 1 and December 31, provided the advective component of the Lagrangian model. The backward mode is indeed computationally advantageous to this extent when the number of receptors of VPPs is less than the number of sources considered 15 , as in our study site. No horizontal diffusion was considered, the irreversible nature of the random processes causing a negative diffusion equivalent to a physically inconsistent aggregation in the backward mode.
For all the weekly dates monitored at LTER-MC during 49 weeks , we simulated backward-in-time trajectories by releasing VPPs of 10, independent particles on a 1. By means of the backward trajectories, the VPP location up to 4 days i. The positions of the VPPs at the end of each backward simulation were used to evaluate the origin zones of phytoplankton organisms, distinguishing periods during which VPPs originated from the inner part of the basin coastal phases or from the open waters offshore phases.
To define the spatial distribution of the plankton origin zones, a binned cumulative probability p bin was calculated. The entire basin was divided into regular subsectors bins 0.