Understanding landscape-primary productivity and biodiversity relationship through graph metrics
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2020
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Abstract
Besides landscape ecological studies, the traditional approach to trying to understand
ecological processes that occur in a landscape is the use of spatial statistics. However,
this does not take into account that many of these processes cannot be observed without
considering the multiple interactions that occur between patches of different land use in
the landscape. The objective of this research was to explore the use of graph metrics in
understanding the processes at the landscape scale, specifically its productivity and plant
biodiversity, using three different landscapes. A bibliographic review of the graph metrics
was performed, which was separated into landscape and local scales, and into groups
within each scale. The usefulness and ecological significance of the metrics was
evaluated, the relationship between them and with productivity and biodiversity was
analysed. In total, 13 landscape scale metrics and 11 local scale metrics with ecological
significance were selected. It was found that a large part of the metrics were able to
identify differences between the three landscapes and between locations at local scale,
but with different variabilities over time. The metrics had a higher relationship with
productivity at both scales, achieving correlations over 70% between the predicted and
actual values of productivity, while the biodiversity models achieved a correlation over
45%. Several metrics at both scales were important for predicting both productivity and
biodiversity. This study highlights the utility and flexibility of graph theory to understand
processes in landscapes, in the context of biodiversity conservation in agricultural
landscapes and in landscape ecology.Besides landscape ecological studies, the traditional approach to trying to understand
ecological processes that occur in a landscape is the use of spatial statistics. However,
this does not take into account that many of these processes cannot be observed without
considering the multiple interactions that occur between patches of different land use in
the landscape. The objective of this research was to explore the use of graph metrics in
understanding the processes at the landscape scale, specifically its productivity and plant
biodiversity, using three different landscapes. A bibliographic review of the graph metrics
was performed, which was separated into landscape and local scales, and into groups
within each scale. The usefulness and ecological significance of the metrics was
evaluated, the relationship between them and with productivity and biodiversity was
analysed. In total, 13 landscape scale metrics and 11 local scale metrics with ecological
significance were selected. It was found that a large part of the metrics were able to
identify differences between the three landscapes and between locations at local scale,
but with different variabilities over time. The metrics had a higher relationship with
productivity at both scales, achieving correlations over 70% between the predicted and
actual values of productivity, while the biodiversity models achieved a correlation over
45%. Several metrics at both scales were important for predicting both productivity and
biodiversity. This study highlights the utility and flexibility of graph theory to understand
processes in landscapes, in the context of biodiversity conservation in agricultural
landscapes and in landscape ecology.Besides landscape ecological studies, the traditional approach to trying to understand
ecological processes that occur in a landscape is the use of spatial statistics. However,
this does not take into account that many of these processes cannot be observed without
considering the multiple interactions that occur between patches of different land use in
the landscape. The objective of this research was to explore the use of graph metrics in
understanding the processes at the landscape scale, specifically its productivity and plant
biodiversity, using three different landscapes. A bibliographic review of the graph metrics
was performed, which was separated into landscape and local scales, and into groups
within each scale. The usefulness and ecological significance of the metrics was
evaluated, the relationship between them and with productivity and biodiversity was
analysed. In total, 13 landscape scale metrics and 11 local scale metrics with ecological
significance were selected. It was found that a large part of the metrics were able to
identify differences between the three landscapes and between locations at local scale,
but with different variabilities over time. The metrics had a higher relationship with
productivity at both scales, achieving correlations over 70% between the predicted and
actual values of productivity, while the biodiversity models achieved a correlation over
45%. Several metrics at both scales were important for predicting both productivity and
biodiversity. This study highlights the utility and flexibility of graph theory to understand
processes in landscapes, in the context of biodiversity conservation in agricultural
landscapes and in landscape ecology.Besides landscape ecological studies, the traditional approach to trying to understand
ecological processes that occur in a landscape is the use of spatial statistics. However,
this does not take into account that many of these processes cannot be observed without
considering the multiple interactions that occur between patches of different land use in
the landscape. The objective of this research was to explore the use of graph metrics in
understanding the processes at the landscape scale, specifically its productivity and plant
biodiversity, using three different landscapes. A bibliographic review of the graph metrics
was performed, which was separated into landscape and local scales, and into groups
within each scale. The usefulness and ecological significance of the metrics was
evaluated, the relationship between them and with productivity and biodiversity was
analysed. In total, 13 landscape scale metrics and 11 local scale metrics with ecological
significance were selected. It was found that a large part of the metrics were able to
identify differences between the three landscapes and between locations at local scale,
but with different variabilities over time. The metrics had a higher relationship with
productivity at both scales, achieving correlations over 70% between the predicted and
actual values of productivity, while the biodiversity models achieved a correlation over
45%. Several metrics at both scales were important for predicting both productivity and
biodiversity. This study highlights the utility and flexibility of graph theory to understand
processes in landscapes, in the context of biodiversity conservation in agricultural
landscapes and in landscape ecology.Besides landscape ecological studies, the traditional approach to trying to understand
ecological processes that occur in a landscape is the use of spatial statistics. However,
this does not take into account that many of these processes cannot be observed without
considering the multiple interactions that occur between patches of different land use in
the landscape. The objective of this research was to explore the use of graph metrics in
understanding the processes at the landscape scale, specifically its productivity and plant
biodiversity, using three different landscapes. A bibliographic review of the graph metrics
was performed, which was separated into landscape and local scales, and into groups
within each scale. The usefulness and ecological significance of the metrics was
evaluated, the relationship between them and with productivity and biodiversity was
analysed. In total, 13 landscape scale metrics and 11 local scale metrics with ecological
significance were selected. It was found that a large part of the metrics were able to
identify differences between the three landscapes and between locations at local scale,
but with different variabilities over time. The metrics had a higher relationship with
productivity at both scales, achieving correlations over 70% between the predicted and
actual values of productivity, while the biodiversity models achieved a correlation over
45%. Several metrics at both scales were important for predicting both productivity and
biodiversity. This study highlights the utility and flexibility of graph theory to understand
processes in landscapes, in the context of biodiversity conservation in agricultural
landscapes and in landscape ecology.
Description
Tesis (Magíster en Recursos Naturales)--Pontificia Universidad Católica de Chile, 2020