Evaluating landscape characteristics of predicted hotspots for plant invasions

2020 ◽  
Vol 13 (3) ◽  
pp. 163-175
Author(s):  
Adrián Lázaro-Lobo ◽  
Kristine O. Evans ◽  
Gary N. Ervin
Keyword(s):  
Land Use ◽  
Invasive Species ◽  
Propagule Pressure ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Plant Invasions ◽  
Landscape Composition ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Landscape Characteristics

AbstractInvasive species are widely recognized as a major threat to global diversity and an important factor associated with global change. Species distribution models (SDMs) have been widely applied to determine the range that invasive species could potentially occupy, but most examples focus on predictive variables at a single spatial scale. In this study, we simultaneously considered a broad range of variables related to climate, topography, land cover, land use, and propagule pressure to predict what areas in the southeastern United States are more susceptible to invasion by 45 invasive terrestrial plant species. Using expert-verified occurrence points from EDDMapS, we modeled invasion susceptibility at 30-m resolution for each species using a maximum entropy (MaxEnt) modeling approach. We then analyzed how environmental predictors affected susceptibility to invasion at different spatial scales. Climatic and land-use variables, especially minimum temperature of coldest month and distance to developed areas, were good predictors of landscape susceptibility to invasion. For most of the species tested, human-disturbed systems such as developed areas and barren lands were more prone to be invaded than areas that experienced minimal human interference. As expected, we found that landscape heterogeneity and the presence of corridors for propagule dispersal significantly increased landscape susceptibility to invasion for most species. However, we also found a number of species for which the susceptibility to invasion increased in landscapes with large core areas and/or less-aggregated patches. These exceptions suggest that even though we found the expected general patterns for susceptibility to invasion among most species, the influence of landscape composition and configuration on invasion risk is species specific.

scholarly journals Bee responses to landscape composition on coupled and decoupled multiscale approaches

2021 ◽  
Author(s):  
Lazaro Carneiro ◽  
Milton Cezar Ribeiro ◽  
Willian Moura de Aguiar ◽  
Camila de Fátima Priante ◽  
Wilson Frantine-Silva ◽  
...  
Keyword(s):  
Forest Cover ◽  
Linear Models ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Landscape Composition ◽  
Landscape Context ◽  
Multiscale Approach ◽  
Ecological Processes ◽  
Positive Effects ◽  
Scale Of Effect

Abstract ContextMultiscale approaches are essential for understanding ecological processes and detecting the scale of effect. However, nested multiscale approaches retain the effect of the landscape attributes from the smaller spatial scales into the larger ones. Thus, decoupling local vs. regional scales can reveal detailed ecological responses to landscape context, but this multiscale approach is poorly explored. ObjectivesWe evaluated the scale of effect of the forest cover (%) and landscape heterogeneity on Euglossini bees combining coupled and decoupled multiscale approaches. MethodsThe Euglossini males were sampled in forest patches from 15 landscapes within the Atlantic Forest, southeast Brazil. For simplicity, we defined that the coupled approaches represented the local scales and decoupled approaches the regional scales. We decoupled the scales by cutting out the smaller scales inserted into larger ones. We estimated the relationship of the bee community attributes with forest cover (%) and landscape heterogeneity in local and regional scales using Generalized Linear Models. ResultsWe found positive effects of landscape heterogeneity on species richness for regional scales. Forest cover and landscape heterogeneity in local scales showed positive effects on the euglossine abundances. The scale of effect for euglossine richness was higher than species abundances. ConclusionsCombining coupled and decoupled multiscale approaches showed adequate capture of the scale of effect of the landscape composition on bee communities. Therefore, it is of paramount importance to measure the influence of the landscape context on biodiversity. Maintaining landscapes with larger forest cover and spatial heterogeneity is essential to keep euglossine species requirements.

scholarly journals Predicting species distributions and community composition using satellite remote sensing predictors

2021 ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Satellite Remote Sensing ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Next Generation ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Conservation Actions

Abstract Biodiversity is rapidly changing due to changes in the climate and human related activities; thus, the accurate predictions of species composition and diversity are critical to developing conservation actions and management strategies. In this paper, using oak assemblages distributed across the continental United States obtained from the National Ecological Observatory Network (NEON), we assessed the performance of stacked species distribution models (S-SDMs), constructed using satellite remote sensing as covariates and under a Bayesian framework, in order to build the next-generation of biodiversity models. This study represents an attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from satellite remote sensing represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, not necessarily results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus pS-SDM versus cS-SDM), this kind of biodiversity models do not accurately recover the observed species composition at plot level or ecological scales (NEON plots), however, they do return reasonable predictions at macroecological scales, i.e., mid to high correct assignment of species identities at the scale of NEON sites. Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.

Using patch and landscape variables to model bird abundance in a naturally heterogeneous landscape

2003 ◽  
Vol 81 (3) ◽  
pp. 441-452 ◽  
Author(s):  
Gaea E Crozier ◽  
Gerald J Niemi
Keyword(s):  
Spatial Scales ◽  
Remote Sensing Data ◽  
Landscape Composition ◽  
Training Data ◽  
Aerial Photographs ◽  
Habitat Patch ◽  
Bird Abundance ◽  
Landscape Characteristics ◽  
Heterogeneous Landscape ◽  
Study Sites

Regression models were developed to predict relative bird abundance in a naturally heterogeneous landscape using patch and landscape spatial scales. Breeding birds were surveyed with point counts on 140 study sites in 1997 and 1998. Aerial photographs were digitized to obtain habitat patch information, such as area, shape, and edge contrast. Classified remote-sensing data were gathered to provide information on landscape composition and configuration within a 1-km2 area around the study sites. Stepwise multiple linear regression was used to develop 40 species-specific models within specific habitat types using patch and landscape characteristics. In 38 out of the 40 models, area of the habitat patch was first selected as the most important predictor of relative bird abundance. Variables related to the landscape were retained in 6 of the 40 models. In this naturally heterogeneous region, the landscape surrounding the patch contributed little to explaining relative bird abundance. The models were evaluated by examining how well they predicted relative bird abundance in a test set not included in the original analyses. The results of the test data were reasonable: >79% of the test observations were within the prediction intervals established by the training data.

scholarly journals Simple is sometimes better: a test of the transferability of species distribution models

2020 ◽  
Vol 77 (5) ◽  
pp. 1752-1761
Author(s):  
Danielle E Haulsee ◽  
Matthew W Breece ◽  
Dewayne A Fox ◽  
Matthew J Oliver
Keyword(s):  
Species Distribution ◽  
Mixed Model ◽  
Species Distribution Models ◽  
Autonomous Underwater Vehicle ◽  
Spatial Scales ◽  
Test Model ◽  
Atlantic Sturgeon ◽  
Environmental Predictors ◽  
Distribution Models ◽  
New Locations

Abstract Species distribution models (SDMs) are often empirically developed on spatially and temporally biased samples and then applied over much larger spatial scales to test ecological hypotheses or to inform management. Underlying this approach is the assumption that the statistical relationships between species observations and environmental predictors are applicable to other locations and times. However, testing and quantifying the transferability of these models to new locations and times can be a challenge for resource managers because of the technical difficulty in obtaining species observations in new locations in a dynamic environment. Here, we apply two SDMs developed in the Mid-Atlantic Bight for Atlantic sturgeon (Acipenser oxyrhynchus oxyrhynchus) to the South Atlantic Bight and use an autonomous underwater vehicle to test model predictions. We compare Atlantic sturgeon occurrence to two SDMs: one associating sturgeon occurrence with simple seascapes and one developed through coupling occurrences with environmental predictors in a generalized additive mixed model (GAMM). Our analysis showed that the seascape model was transferable across these disparate regions; however, the complex GAMM was not. The association of the imperilled Atlantic sturgeon with simple seascapes allows managers to easily integrate this remotely sensed dynamic oceanographic product into future ecosystem-based management strategies.

Forecasting the Vulnerability of Lakes to Aquatic Plant Invasions

2014 ◽  
Vol 7 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Mariana Tamayo ◽  
Julian D. Olden
Keyword(s):  
Invasive Species ◽  
Invasive Plant ◽  
Classification Tree ◽  
Plant Invasions ◽  
Urban Land Use ◽  
Lake Area ◽  
Native Plant ◽  
Potamogeton Crispus ◽  
Distribution Models ◽  
Lake Ecosystems

AbstractPrevention is an integral component of many management strategies for aquatic invasive species, yet this represents a formidable task when the landscapes to be managed include multiple invasive species, thousands of waterbodies, and limited resources to implement action. Species distributional modeling can facilitate prevention efforts by identifying locations that are most vulnerable to future invasion based on the likelihood of introduction and environmental suitability for establishment. We used a classification tree approach to predict the vulnerability of lakes in Washington State (United States) to three noxious invasive plants: Eurasian watermilfoil (Myriophyllum spicatum), Brazilian egeria (Egeria densa), and curlyleaf pondweed (Potamogeton crispus). Overall, the distribution models predicted that approximately one-fifth (54 out of 319 study lakes) of lakes were at risk of being invaded by at least one aquatic invasive plant, and many of these predicted vulnerable lakes currently support high native plant diversity and endemism. Highly vulnerable lakes are concentrated in western Washington in areas with the highest human population densities, and in eastern Washington along the Columbia Basin Irrigation Project and the Okanogan River Basin that boast hundreds of lakes subject to recreational use. Overall, invasion potential for the three species was highly predictable as a function of lake attributes describing human accessibility (e.g., public boat launch, urban land use) and physical–chemical conditions (e.g., lake area, elevation, productivity, total phosphorous). By identifying highly vulnerable lake ecosystems, our study offers a strategy for prioritizing on-the-ground management action and informing the most efficient allocation of resources to minimize future plant invasions in vast freshwater networks.

scholarly journals Pollinator guilds respond contrastingly at different scales to landscape parameters of land-use intensity

2021 ◽  
Author(s):  
Kolja Bergholz ◽  
Lara-Pauline Sittel ◽  
Michael Ristow ◽  
Florian Jeltsch ◽  
Lina Weiss
Keyword(s):  
Land Use ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Wild Bees ◽  
Dry Grassland ◽  
Land Use Intensification ◽  
Ne Germany ◽  
Local Plant ◽  
Species Richness And Abundance ◽  
Positive Effect

Land-use intensification is the main factor for the catastrophic decline of insect pollinators. However, land-use intensification includes multiple processes that act across various scales and should affect pollinator guilds differently depending on their ecology. We aimed to reveal how two main pollinator guilds, wild bees (specialists) and hoverflies (generalists), respond to different land-use intensification measures, i.e. arable field cover (AFC), landscape heterogeneity (LH) and functional flower composition of local plant communities as a measure of habitat quality. We sampled wild bees and hoverflies on 22 dry grassland sites within a highly intensified landscape (NE Germany) within three campaigns using pan traps. We estimated AFC and LH on consecutive radii (60-3000m) around the dry grassland sites and estimated the local functional flower composition. Wild bee species richness and abundance was positively affected by LH and negatively by AFC at small scales (140-400m). In contrast, hoverflies were positively affected by AFC and negatively by LH at larger scales (500-3000m), where both landscape parameters were negatively correlated to each other. At small spatial scales, though, LH had a positive effect on hoverflies abundance. Functional flower diversity had no positive effect on pollinators, but conspicuous flowers seem to attract abundance of both guilds. In conclusion, landscape parameters contrarily affect two pollinator guilds at different scales. The correlation of landscape parameters may influence the observed relationships between landscape parameters and pollinators. Hence, effects of land-use intensification seems to be highly landscape-specific.

scholarly journals Land-use change and propagule pressure promote plant invasions in tropical rainforest remnants

2020 ◽  
Vol 35 (9) ◽  
pp. 1891-1906
Author(s):  
Emily H. Waddell ◽  
Lindsay F. Banin ◽  
Susannah Fleiss ◽  
Jane K. Hill ◽  
Mark Hughes ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Tropical Rainforest ◽  
Propagule Pressure ◽  
Plant Invasions

scholarly journals Predicting species distributions and community composition using remote sensing products

2020 ◽  
Author(s):  
Jesús N. Pinto-Ledezma ◽  
Jeannine Cavender-Bares
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Direct Detection ◽  
Spatial Scales ◽  
Management Strategies ◽  
Next Generation ◽  
Environmental Predictors ◽  
Distribution Models ◽  
Conservation Actions ◽  
New Generation

AbstractAccurate predictions of species composition and diversity are critical to the development of conservation actions and management strategies. In this paper using oak assemblages distributed across the conterminous United States as study model, we assessed the performance of stacked species distribution models (S-SDMs) and remote sensing products in building the next-generation of biodiversity models. This study represents the first attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from remote sensing products represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus cS-SDM), biodiversity models do not recover the observed species composition with high spatial resolution, i.e., correct species identities at the scale of individual plots. However, they do return reasonable predictions at macroecological scales (1 km). Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.

scholarly journals Complex community responses underpin biodiversity change following invasion

2021 ◽  
Author(s):  
Alessandra R. Kortz ◽  
Anne E. Magurran
Keyword(s):  
Invasive Species ◽  
Species Richness ◽  
Spatial Scales ◽  
Species Abundance ◽  
Ecological Systems ◽  
Multi Scale ◽  
Mechanistic Approach ◽  
Α Diversity ◽  
Biodiversity Change ◽  
Insight Into

AbstractHow do invasive species change native biodiversity? One reason why this long-standing question remains challenging to answer could be because the main focus of the invasion literature has been on shifts in species richness (a measure of α-diversity). As the underlying components of community structure—intraspecific aggregation, interspecific density and the species abundance distribution (SAD)—are potentially impacted in different ways during invasion, trends in species richness provide only limited insight into the mechanisms leading to biodiversity change. In addition, these impacts can be manifested in distinct ways at different spatial scales. Here we take advantage of the new Measurement of Biodiversity (MoB) framework to reanalyse data collected in an invasion front in the Brazilian Cerrado biodiversity hotspot. We show that, by using the MoB multi-scale approach, we are able to link reductions in species richness in invaded sites to restructuring in the SAD. This restructuring takes the form of lower evenness in sites invaded by pines relative to sites without pines. Shifts in aggregation also occur. There is a clear signature of spatial scale in biodiversity change linked to the presence of an invasive species. These results demonstrate how the MoB approach can play an important role in helping invasion ecologists, field biologists and conservation managers move towards a more mechanistic approach to detecting and interpreting changes in ecological systems following invasion.

Sign in / Sign up

Export Citation Format

Share Document