Backward in time modeling to assess the effects of an afforestation process on plant species

Backward in time modeling to assess the effects of an afforestation process on plant species richness

Amici V. 1,*_{, Geri F.} 1_{, Rocchini D.} 2_{, Landi S.} 1_{, Chiarucci A.} 1

1 _{BIOCONNET, Biodiversity and Conservation Network, Department of Environmental Science}

“G.Sarfatti”, University of Siena, Via P.A. Mattioli 4, 53100 Siena, Italy

2 _{Fondazione Edmund Mach, Research and Innovation Centre, Department of Biodiversity and}

Molecular Ecology, GIS and Remote Sensing Unit, Via E. Mach 1, 38010 S. Michele all’Adige (TN), Italy

*

Corresponding author: e-mail address [email protected]; [email protected]

Effective conservation of biodiversity in the face of increasing human impacts and global environmental changes requires accurate measurement of key trends and alternative management actions at landscape scales. Since to better understand the present species diversity patterns we need to know the past, one key lies in extrapolate ecological data through the construction of ecological models that include the use of historical cartographic data. The aim of this work is to promote a method to predict backward in time plant species richness at a large spatial scale using present field data, historical land use maps and interpolation techniques. Although this is a weak method compared to direct field studies, it may serve as an approximation in the analysis of the effects of the establishment of forest in abandoned agricultural areas on forest plant species richness. The field data from an extensive monitoring program (Mo.Bi.SIC), were here used to model the temporal species richness change among the forest areas in the last 60 years. In order to rebuild a past species pool matrix using present field data and historical land use map, we applied a nearest neighbour selection using spatial

query in PostGIS environment. The obtained datasets have been interpolated using Inverse Distance Weighted algorithm implemented in GRASS software. Finally statical analysis were performed within the R-package in order to assess the accuracy of the interpolation maps. The results showed a high correlation between observed and interpolated values and a significant difference in terms of species richness loss between afforested and non forested areas. The results also emphasize that the proposed methodology can be used as an effective tool in addressing the problem of the lack of historical field data in the analysis of multi-temporal landscape change and its effects on biodiversity.