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

The spread of invasive alien species promotes ecosystem structure and functioning changes, with detrimental effects on native biodiversity and ecosystem services, raising challenges for local management authorities. Predictions of invasion dynamics derived from modeling tools are often spatially coarse and therefore unsuitable for guiding local management. Accurate information on the occurrence of invasive plants and on the main factors that promote their spread is critical to define successful control strategies. For addressing this challenge, we developed a dual framework combining satellite image classification with predictive ecological modeling. By combining data from georeferenced invaded areas with multispectral imagery with 10-meter resolution from Sentinel-2 satellites, a map of areas invaded by the woody invasive Acacia longifolia in a municipality of northern Portugal was devised. Classifier fusion techniques were implemented through which eight statistical and machine-learning algorithms were ensembled to produce accurate maps of invaded areas. Through a Random Forest (RF) model, these maps were then used to explore the factors driving the landscape-level abundance of A. longifolia. RF models were based on explanatory variables describing hypothesized environmental drivers, including climate, topography/geomorphology, soil properties, fire disturbance, landscape composition, linear structures, and landscape spatial configuration. Satellite-based maps synoptically described the spatial patterns of invaded areas, with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.895, Area Under the Receiver Operating Curve, ROC: 0.988, Kappa: 0.857). The predictive RF models highlighted the primary role of climate, followed by landscape composition and configuration, as the most important drivers explaining the species abundance at the landscape level. Our innovative dual framework—combining image classification and predictive ecological modeling—can guide decision-making processes regarding effective management of invasions by prioritizing the invaded areas and tackling the primary environmental and anthropogenic drivers of the species’ abundance and spread.

O uso de modelagem de distribuição de espécies para restauração florestal: Uma revisão sistemática

O objetivo deste trabalho foi realizar uma revisão sistemática da produção científica do uso da modelagem de distribuição de espécies para restauração florestal. As buscas de artigos científicos nas bases de dados Scopus e Web of Science para os últimos 15 anos foram realizadas no mês de dezembro de 2020 utilizando os termos: “ecological modeling” OR “biodiversity modeling” OR “predictive models” OR “niche modeling" OR "habitat models" AND “species distribution” OR "geographic distribution" OR “potential distribution” AND “forest restoration” OR “restoration ecology”. Para as análises estatísticas e gráficos dos dados brutos foi utilizado o pacote Bibliometrix do software R. Os dados brutos foram refinados por meio da seleção dos estudos que atenderam aos seguintes critérios: (i) estudos publicados em revistas científicas com fator de impacto igual ou superior a 2,0; (ii) estudos em que o título ou resumo mencionasse as palavras restauração florestal ou restauração ecológica; (iii) estudos que avaliaram o uso de modelagem de distribuição de espécies como auxílio aos projetos e programas de restauração florestal ou restauração ecológica. Foram encontrados 44 documentos publicados em 30 periódicos científicos com média de 3,91 publicações por ano; 18,55 citações por documento; 197 autores, sendo 3 documentos com autoria única. Assim pode-se concluir que o uso de modelagem de distribuição de espécies para restauração florestal no mundo é muito recente, e no Brasil é incipiente com baixos números de artigos publicados, mas apresenta tendência de crescimento por conta da sua significativa contribuição para melhorar as taxas de sucesso dos projetos de restauração.

Invasive Dreissenid Mussel Effects on Phosphorus Dynamics in Lake Ontario: Insights from Integrated Hydrodynamic-Ecological Modeling

Invasive dreissenid mussels have a profound effect on the total phosphorus (TP) budget in Lake Ontario, which in turn influences ecological processes such as the resurgence of the benthic alga Cladophora. A validated three-dimensional integrated hydrodynamic and ecological modeling framework is applied to quantify the impact that dreissenids have on the spatial and species distribution of TP in the lake. Model results for April to September 2013 show that dreissenids decrease TP in the water column by about 1812 metric tons (MT), which is about 60% of the tributary TP loading to the lake. This reduction in TP affects other processes controlling its distribution. Physical transport of TP from nearshore to offshore waters is reduced, and the amount of TP involved in chemical reactions is reduced, while TP processed by biological transformations is increased. This study provides the first attempt to quantify the TP budget changes in Lake Ontario by dreissenids using numerical modeling, and findings of this study can be generalized to other lakes with similar conditions.

Is New Always Better? Frontiers in Global Climate Datasets for Modeling Treeline Species in the Himalayas

Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.

The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.