Posidonia oceanica (L.) Delile is a widespread slow-growing endemic seagrass of the Mediterranean Sea, colonizing the coastal environments from the surface down to 40-50 meters in depth. The meadows formed by P. oceanica constitute one of the most important Mediterranean marine ecosystems in terms of productivity and biodiversity, with associated communities representing climax assemblages on soft and shallow substrata (González-Correa et al., 2007). The meadows act as nurseries and refugia for several species, sustain diverse trophic webs, host endangered species, and are able to alter water circulation and sedimentation rate, preventing coastal erosion and shaping the seascape (Ruiz et al., 2001). Unfortunately, this habitat is dramatically shrinking across large sections of the Mediterranean coasts, due to anthropogenic pressures such as the mechanical damage by anchoring and trawling, as well as the coastal development (Boudouresque et al., 2009). The slow growth of P. oceanica means that countering meadow regression implies not only the local protection of this habitat, but also the restoration, whenever possible, of the already lost meadows. The latter is crucial in recovering the functioning and ecological integrity of coastal ecosystems, and the practice is gaining momentum with the development of increasingly efficient approaches. However, in spite of the widespread engagement in restoration programs, the success and outcomes of these actions, in terms of meadow evolution over time, are still hard to predict. In this context, ecological modeling can provide crucial support in evaluating the potential evolution of the meadows, especially through approaches focusing on describing the growth and the interactions with the environment of the planted propagules. The accuracy of these models, however, critically depends on the understanding of the biology and ecology of P. oceanica. To this end, the present research focuses on summarizing through an extensive meta-analysis the wealth of data and findings published on P. oceanica during the last 5 decades, using the derived information to parameterize individual based models of meadow evolution in time and space. Specifically, a spatial energy dynamic budget model (sDEB) was developed, including resource gathering and allocation processes, competitive interactions among shoots for light and space, as well as the effects of environmental factors such as light, nutrients, temperature and substrate type on species physiology. On the one hand, the model allows evaluating the potential evolution of planted meadows in time and space, on the other hand, meta-analysis results, provided in the form of a georeferenced database, highlight the current knowledge gaps toward which future researches should be oriented to improve our understanding of the ecology of this species and, thus, optimize the management of the unique ecosystem it forms.
From species ecology to ecosystem restoration – a modelling approach to successfully restore Posidonia oceanica meadows
Baldi V.;Bellino A.
;Baldantoni D.
2024-01-01
Abstract
Posidonia oceanica (L.) Delile is a widespread slow-growing endemic seagrass of the Mediterranean Sea, colonizing the coastal environments from the surface down to 40-50 meters in depth. The meadows formed by P. oceanica constitute one of the most important Mediterranean marine ecosystems in terms of productivity and biodiversity, with associated communities representing climax assemblages on soft and shallow substrata (González-Correa et al., 2007). The meadows act as nurseries and refugia for several species, sustain diverse trophic webs, host endangered species, and are able to alter water circulation and sedimentation rate, preventing coastal erosion and shaping the seascape (Ruiz et al., 2001). Unfortunately, this habitat is dramatically shrinking across large sections of the Mediterranean coasts, due to anthropogenic pressures such as the mechanical damage by anchoring and trawling, as well as the coastal development (Boudouresque et al., 2009). The slow growth of P. oceanica means that countering meadow regression implies not only the local protection of this habitat, but also the restoration, whenever possible, of the already lost meadows. The latter is crucial in recovering the functioning and ecological integrity of coastal ecosystems, and the practice is gaining momentum with the development of increasingly efficient approaches. However, in spite of the widespread engagement in restoration programs, the success and outcomes of these actions, in terms of meadow evolution over time, are still hard to predict. In this context, ecological modeling can provide crucial support in evaluating the potential evolution of the meadows, especially through approaches focusing on describing the growth and the interactions with the environment of the planted propagules. The accuracy of these models, however, critically depends on the understanding of the biology and ecology of P. oceanica. To this end, the present research focuses on summarizing through an extensive meta-analysis the wealth of data and findings published on P. oceanica during the last 5 decades, using the derived information to parameterize individual based models of meadow evolution in time and space. Specifically, a spatial energy dynamic budget model (sDEB) was developed, including resource gathering and allocation processes, competitive interactions among shoots for light and space, as well as the effects of environmental factors such as light, nutrients, temperature and substrate type on species physiology. On the one hand, the model allows evaluating the potential evolution of planted meadows in time and space, on the other hand, meta-analysis results, provided in the form of a georeferenced database, highlight the current knowledge gaps toward which future researches should be oriented to improve our understanding of the ecology of this species and, thus, optimize the management of the unique ecosystem it forms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
