Estimating Invasion Success by Non-Native Trees in a National Park Combining WorldView-2 Very High Resolution Satellite Data and Species Distribution Models

Abstract

Invasion by non-native tree species is an environmental and societal challenge requiring predictive tools to assess invasion dynamics. The frequent scale mismatch between such tools and on-ground conservation is currently limiting invasion management. This study aimed to reduce these scale mismatches, assess the success of non-native tree invasion and determine the environmental factors associated to it. A hierarchical scaling approach combining species distribution models (SDMs) and satellite mapping at very high resolution (VHR) was developed to assess invasion by Acacia dealbata in Peneda-Gerês National Park, the only national park in Portugal. SDMs were first used to predict the climatically suitable areas for A. dealdata and satellite mapping with the random-forests classifier was then applied to WorldView-2 very-high resolution imagery to determine whether A. dealdata had actually colonized the predicted areas (invasion success). Environmental attributes (topographic, disturbance and canopy-related) differing between invaded and non-invaded vegetated areas were then analyzed. The SDM results indicated that most (67%) of the study area was climatically suitable for A. dealbata invasion. The onset of invasion was documented to 1905 and satellite mapping highlighted that 12.6% of study area was colonized. However, this species had only colonized 62.5% of the maximum potential range, although was registered within 55.6% of grid cells that were considerable unsuitable. Across these areas, the specific success rate of invasion was mostly below 40%, indicating that A. dealbata invasion was not dominant and effective management may still be possible. Environmental attributes related to topography (slope), canopy (normalized difference vegetation index (ndvi), land surface albedo) and disturbance (historical burnt area) differed between invaded and non-invaded vegetated area, suggesting that landscape attributes may alter at specific locations with Acacia invasion. Fine-scale spatial-explicit estimation of invasion success combining SDM predictions with VHR invasion mapping allowed the scale mismatch between predictions of invasion dynamics and on-ground conservation decision making for invasion management to be reduced. Locations with greater potential to suppress invasions could also be defined. Uncertainty in the invasion mapping needs to be accounted for in the interpretation of the results.

Evaluation of Species Invasiveness: A Case Study with Acacia dealbata Link. on the Slopes of Cabeça (Seia-Portugal)

“…Although other studies propose the calculation of the risk of invasion, answering a set of questions [6,54], the linear advance of invasive plant populations is in fact crucial to calculate the time traveled to cover a specific area or to some physical limit, as is a property or a water line. However, the expansion of acacia populations seems to be favored by the proximity to water lines, as has been identified in other studies [21,55]. This capacity for growth and space occupation is clearly visible in the present (Figure 2).…”

“…calculate the time traveled to cover a specific area or to some physical limit, as is a property or a water line. However, the expansion of acacia populations seems to be favored by the proximity to water lines, as has been identified in other studies [21,55]. This capacity for growth and space occupation is clearly visible in the present (Figure 2).…”

“…In this sense, invasion rate prediction, modeling and management of acacia-dominated ecosystems are challenging tasks that need further investigation and intervention [17,18]. Several studies present results on the prediction of the development of invasive species; however, the models available do not use a stain invasiveness rate, nor an annual linear increment [19][20][21]. Morphological, chorological and ecological characterization data for this species are well known, as are the most efficient control methods [22,23].…”

‘The Best of Two Worlds’—Combining Classifier Fusion and Ecological Models to Map and Explain Landscape Invasion by an Alien Shrub

“…Overall, these advances manifest in managers' and stakeholders' ability to profit from the availability of data and analytical routines to make better assessments, monitor, and devise strategies to control invasive species. Despite these advances, the combination of both satellite image classification (for invasive species mapping/detection) and predictive modeling (for inferring invasion drivers) has been seldom explored (but see, e.g., [27]). As such, researchers miss the ability to profit from synergistic advances of both fields and obtain more insights regarding species invasions.…”

Prunus lusitanica L.: An Endangered Plant Species Relict in the Central Region of Mainland Portugal

“…However, due to the permanent threats that menace these habitats, a significant reduction in the number of individuals occurred [6]. Among all the threats identified in the Habitat File, the invasion of exotic plants that progressively colonize new areas and the recurrence and severity of rural fires can be highlighted [12][13][14]. In fact, at this moment, the impact of fires is, probably, the major concern for the reduction in the number of individuals and loss of the area occupied by this habitat, particularly in the central region of mainland Portugal [15].…”