Corresponding author: Marco Malavasi ( malavasi@fzp.czu.cz ) Academic editor: Michele Freppaz
© 2019 Flavio Marzialetti, Manuele Bazzichetto, Silvia Giulio, Alicia T.R. Acosta, Angela Stanisci, Marco Malavasi, Maria Laura Carranza.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Marzialetti F, Bazzichetto M, Giulio S, Acosta ATR, Stanisci A, Malavasi M, Carranza ML (2019) Modelling Acacia saligna invasion on the Adriatic coastal landscape: An integrative approach using LTER data. In: Mazzocchi MG, Capotondi L, Freppaz M, Lugliè A, Campanaro A (Eds) Italian Long-Term Ecological Research for understanding ecosystem diversity and functioning. Case studies from aquatic, terrestrial and transitional domains. Nature Conservation 34: 127-144. https://doi.org/10.3897/natureconservation.34.29575
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Invasive Alien Species (IAS) pose a major threat to biodiversity and ecosystem services worldwide. Even if preventing biological invasions should be the most cost-effective way to minimise the impact of IAS on biodiversity, new efforts are necessary to identify early signs of invasion and to assess invasion risk. In this context, the implementation of invasive Species Distribution Models (iSDMs) could represent a sound instrument that merits further research. Acacia saligna is an Australian vascular plant introduced into Europe during the last half century and is one of the most aggressive IAS in the Mediterranean basin.
In this work, we model the occurrence of A. saligna in the coastal landscapes of central Italy (Adriatic coast) while accounting for the simultaneous effect of multiple factors (propagule pressure, abiotic, biotic factors). The iSDM for A. saligna was implemented on a representative tract of the Adriatic coast in central Italy (Molise region), largely included in two Long-Term Ecological Research (LTER) sites which actively contribute to the description of the considered ecosystem status and possible future trends. By using a Generalised Linear Model (GLM) with a binomial distribution of errors based on field and cartographic geo-referenced data, we examined the statistical relationship between the occurrence of A. saligna and a comprehensive set of environmental factors. The iSDM effectively captured the role of the different variables in determining the occurrence of A. saligna in the coastal dunes. Its occurrence is primarily related to Wooded dunes with Pinus pinea and/or P. pinaster (EU Habitat 2270) and distance from the sea and, to a lesser extent, with distance from roads and rivers. This research provides a first exploratory analysis of the environmental characteristics that promote the rapid growth and development of A. saligna in Italian dune ecosystems, identifying the habitats that are mainly affected by the invasive process in coastal areas and, by doing so, contributing to filling the gap between theory and practice in conservation decision-making. Finally, the LTER network benefitted from this research, confirming its relevance in providing useful information for modelling and monitoring invasion processes.
Abiotic factors, Biotic factors, Invasive species distribution model, Propagule pressure, LTER
Biological invasions are one of the major global drivers of biodiversity loss, often resulting in economic damage and public health care problems (
The establishment, growth and expansion of invasive alien species depend on a combination of mechanisms related to both ecology of the species and the assembly of environmental factors (
In order to deal with biological invasions and for preventing their negative effects on ecosystem biodiversity and functioning (
Despite all these efforts, further research is still necessary, orientated towards implementing invasive species distribution models for supporting IAS management. A taxon for which important research efforts have been undertaken, but still requiring multivariate analysis of the different invasion drivers, is the Australian genus Acacia. Acacia sp. is a highly aggressive genus and one of the major invaders in the world (
In light of this, the present study sets out to model the occurrence of A. saligna in coastal landscapes of central Italy (Adriatic coast) while accounting for the simultaneous effect of propagule pressure, abiotic and biotic factors. By implementing an iSDM, based on field and cartographic geo-referenced data, we explored the relationship between the presence of A. saligna and PAB factors. We assumed that the invasion by A. saligna across the dune mosaic is not homogeneous, but varies through space, according to the distribution of the main PAB factors.
By identifying the factors related to higher occurrence values of the alien taxa and by mapping the areas with different probabilities of occurrence, we can identify new tools able to contribute to the prioritisation of conservation actions in coastal ecosystems, as required by the EU Alien regulation.
It is also worth mentioning that such iSDM implementation benefitted from the presence within the study area of two Long-Term Ecological Research sites (LTER) (http://www.lter-europe.net/), which actively contributed to the description of the considered ecosystem status and its possible future trends. Indeed, the LTER network in which ecosystem experts monitor a wide range of environmental variables may offer a rich overview of alien species distribution and invasion drivers across different ecosystems and geographical areas.
A. saligna is one of the most invasive taxa of the genus Acacia (
A. saligna has several ecological features that favour its expansion in non-native environments. The clonal and sexual reproduction, high rate of growth, short juvenile period and high tolerance to environmental stress (
The study was carried out on a representative tract of the Adriatic coast of central Italy (Molise region). The coast is mainly composed of recent sandy dunes (Holocene), which occupy a narrow strip along the seashore. The dune system has a simple structure, being usually characterised by a single dune ridge with low elevation (less than 10 metres) (
The intense and rapid land use change (
To implement the analysis, we used presence data of A. saligna mostly collected inside LTER sites during the years 2013-15 (
In an ArcGIS environment (ArcGIS 10.2.2), a set of environmental variables was computed related to propagule pressure (P), abiotic (A) and biotic factors (B) and which were expected to influence the expansion of A. saligna. We selected road distance as a proxy of propagule pressure (
Predictors analysed. Propagule pressure (P), abiotic (A) and biotic (B) factors along with the corresponding proxy variables (predictors) used for implementing the iSDM.
Factor | Proxy variables (predictors) | Description |
P | Road distance (m) | Euclidean nearest distance (m) from paved roads and secondary pathways |
A | Sea distance (m) | Euclidean distance (m) from shoreline |
River distance (m) | Euclidean distance (m) from nearest river (main streams, river courses) | |
B | % Pinus sp. wooded dunes | Percentage of Pinus sp. wooded dunes within a 30 m radius window. Includes EU Habitat: 2270 – Wooded dunes with Pinus pinea and/or Pinus pinaster |
% Herbaceous dune vegetation | Percentage of herbaceous vegetation within a 30 m radius window. Includes embryonic shifting dunes (EC-2110), shifting dunes along the shoreline with Ammophila arenaria (EC – 2120) and Malcolmietalia dune grasslands (EC –2230). |
The iSDM was based on a binomial Generalised Linear Model (GLM) aimed at analysing the relationship between the occurrence of A. saligna and the PAB variables (
Model performance was evaluated using two measures: the McFadden’s R squared (
According to the GLM model, the different PAB factors showed specific and significant relationships with the occurrence of A. saligna. (Table
GLM model outcome. Response variable: Acacia saligna presence/absence; predictors: Propagule pressure, abiotic and biotic factors. For a detailed description of the predictors and the land cover types see Table
Predictors | Estimate | Std. Error | Z value | p-value |
Intercept | 2.08 | 1.24 | 1.68 | p>0.05 |
P (Propagule pressure) | ||||
Road distance | -0.004 | 0.002 | -2.059 | * |
A (Abiotic) | ||||
Sea distance | -0.025 | 0.010 | -2.639 | ** |
River distance | -0.001 | 0.000 | -2.289 | * |
B (Biotic) | ||||
Pinus sp. dune wood | 0.073 | 0.019 | 3.884 | *** |
Herbaceous dune vegetation | 0.003 | 0.016 | 0.202 | p>0.05 |
The variables, considered for modelling species occurrence, were independent with no significant Spearman’s rank correlation coefficient and VIF values (see Appendix
The occurrences of A. saligna were associated with propagule pressure (P) (Table
Regression curves. Relationship between A. saligna occurrence and the PAB predictors a Road distance b Sea distance c River distance d % of Pinus sp. dune wood). On the x-axis: predictors with the corresponding unit of measurement. On the y-axis: the residual values for each predictor.
The fitted GLM explained 0.70 of the variability (McFadden’s R squared= 0.70) and good predictive power (AUC mean = 0.96).
In this study, we modelled the occurrence of A. saligna along the Adriatic coast in central Italy and identified the specific role of propagule pressure, abiotic and biotic factors in determining the presence of the species. The model showed a good power of prediction, as highlighted by the explained variability of the McFadden’s R squared and predictive accuracy of the mean AUC, obtained through cross-validation.
Our results highlighted that the invasion by A. saligna was not spatially homogeneous, but varied across the coastal landscape, following the spatial distribution of the different PAB factors. A. saligna preferentially occurred close to the coastal pine forest, at an intermediate distance from the coastline, preferably 50-100 metres from sea and its presence was also related to distance from roads and rivers. Specifically, A. saligna occurrence is promoted by propagule pressure, which along the Mediterranean coasts can be related to distance from roads (
Moreover, the model also highlighted that abiotic factors regulated A. saligna invasion. Coastal dune ecosystems are characterised by a mosaic of habitats in which the gradual change in abiotic conditions shapes the growth of the species, thus determining the typical sea-inland ecological gradient (
Similar behaviour was observed in other ecosystems characterised by dry sandy soils, (e.g. South-African fynbos, coastal sand dunes of Israel) in which A. saligna invades areas with open or patchy vegetation (
In confirmation of this, river distance (a proxy of soil moisture) seems to favour A. saligna growth in this Adriatic sector. The observed correlation highlighted the tendency of the species to grow and develop in the most humid areas of arid coasts of the Mediterranean climatic region (
Biotic conditions also affected the distribution of A. saligna, which preferentially invaded areas close to pine forests. The preference of the invader for pine forests on Mediterranean coasts is particularly worrisome because this formation, with high historical and social value for the territory (
Similarly to that observed by
This integrative analysis of the occurrence of the non-native species A. saligna in coastal landscapes, including a single model using the simultaneous effect of propagule pressure, abiotic and biotic factors, allowed us to effectively depict those critical drivers for determining the presence of this highly invasive plant in the Mediterranean dunes and to define areas with different probabilities of invasion. Invasion by A. saligna was not homogeneous but varied across the coastal landscape, following the spatial distribution of different factors. Indeed, invasion preferentially occurred on coastal fixed dunes close to pine forests. The implemented iSDM provided valuable insights into the invasion process and it supplied an efficient prediction of the invasion processes in this stretch of the Adriatic coast, providing the necessary instruments for the assessment of invasion risks claimed by the EU Alien regulation (EEC 2014).
Finally, as the presence of a valuable amount of data collected across a network of LTER sites supported the implementation of a more effective iSDM, it is also true that the LTER network benefitted from such research, confirming its relevance in providing useful information for modelling and monitoring invasion processes. Furthermore, by monitoring variations in environmental conditions, it is possible to identify, through multi-temporal iSDMs, the factors and phenomena that allow alien species expansion over time. With this in mind, we hope that other LTER network-based case studies could be further carried out to provide integrated information across a wide range of monitored ecosystems and for increasingly larger areas.
The authors are grateful the LIFE WATCH “Patterns of ecosystem fragility to alien and invasive species in Europe” for providing contribution to alien species knowledge in Italy.
The variables considered for modelling species occurrence are lower than the threshold values of both Spearman’s rank correlation coefficient and VIF values (Table A1). Sea distance and herbaceous dune vegetation are the variables with greater correlation score (-0.6); instead the lowest correlation was found between herbaceous dune vegetation and road distance (0.00). Pinus dune wood in the multi-collinearity test has the higher VIF (2.56), while road distance has the lowest (1.12).
Correlation analysis. Spearman's rank correlation coefficient and Variance Inflation factor (VIF) for all predictors.
Predictors | Spearman’s rank correlation | VIF | ||||
Pinus sp. dune wood | Road distance | Herbaceous dune vegetation | Sea distance | River distance | ||
Pinus dune wood | 2.56 | |||||
Road distance | 0.15 | 1.12 | ||||
Herbaceous dune vegetation | 0.01 | 0.00 | 1.22 | |||
Sea distance | -0.35 | -0.05 | -0.60 | 1.43 | ||
River distance | -0.07 | 0.17 | -0.04 | 0.06 | 1.19 |