scholarly journals Predicting future invaders and future invasions

2019 ◽  
Vol 116 (16) ◽  
pp. 7905-7910 ◽  
Author(s):  
Alice Fournier ◽  
Caterina Penone ◽  
Maria Grazia Pennino ◽  
Franck Courchamp
Keyword(s):  
Papua New Guinea ◽  
Ecosystem Functioning ◽  
Species Distribution Models ◽  
Invasive Alien Species ◽  
Management Strategies ◽  
Central Africa ◽  
Invasive Ants ◽  
Distribution Models ◽  
Proactive Management ◽  
Local Extinctions

Invasive alien species are a great threat to biodiversity and human livelihoods worldwide. The most effective way to limit their impacts and costs is to prevent their introduction into new areas. Identifying invaders and invasions before their occurrence would arguably be the most efficient strategy. Here, we provide a profiling method to predict which species—with which particular ecological characteristics—will invade, and where they could invade. We illustrate our approach with ants, which are among the most detrimental invasive species, as they are responsible for declines of numerous taxa, are involved in local extinctions, disturb ecosystem functioning, and impact multiple human activities. Based on statistical profiling of 1,002 ant species from an extensive trait database, we identify 13 native ant species with an ecological profile that matches that of known invasive ants. Even though they are not currently described as such, these species are likely to become the next global invaders. We couple these predictions with species distribution models to identify the regions most at risk from the invasion of these species: Florida and Central America, Brazil, Central Africa and Madagascar, Southeast Asia, Papua New Guinea Northeast Australia, and many islands worldwide. This framework, applicable to any other taxa, represents a remarkable opportunity to implement timely and specifically shaped proactive management strategies against biological invasions.

scholarly journals Extinction debts and colonization credits of non-forest plants in the European Alps

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabine B. Rumpf ◽  
Karl Hülber ◽  
Johannes Wessely ◽  
Wolfgang Willner ◽  
Dietmar Moser ◽  
...  
Keyword(s):  
Climate Change ◽  
Species Distribution Models ◽  
European Alps ◽  
Extinction Debt ◽  
Distribution Models ◽  
Realized Niche ◽  
Dispersal Capability ◽  
Local Extinctions ◽  
Forest Plants ◽  
Mountain Plant

Abstract Mountain plant species shift their elevational ranges in response to climate change. However, to what degree these shifts lag behind current climate change, and to what extent delayed extinctions and colonizations contribute to these shifts, are under debate. Here, we calculate extinction debt and colonization credit of 135 species from the European Alps by comparing species distribution models with 1576 re-surveyed plots. We find extinction debt in 60% and colonization credit in 38% of the species, and at least one of the two in 93%. This suggests that the realized niche of very few of the 135 species fully tracks climate change. As expected, extinction debts occur below and colonization credits occur above the optimum elevation of species. Colonization credits are more frequent in warmth-demanding species from lower elevations with lower dispersal capability, and extinction debts are more frequent in cold-adapted species from the highest elevations. Local extinctions hence appear to be already pending for those species which have the least opportunity to escape climate warming.

scholarly journals Community Confounding In Joint Species Distribution Models

2021 ◽  
Author(s):  
Justin J. Van Ee ◽  
Jacob S. Ivan ◽  
Mevin B. Hooten
Keyword(s):  
Bark Beetle ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Management Strategies ◽  
Single Species ◽  
Distribution Model ◽  
Subalpine Forest ◽  
Community Effects ◽  
Distribution Models

Abstract Joint species distribution models have become ubiquitous for studying species-habitat relationships and dependence among species. Accounting for community structure often improves predictive power, but can also alter inference on species-habitat relationships. Modulated species-habitat relationships are indicative of community confounding: The situation in which interspecies dependence and habitat effects compete to explain species distributions. We discuss community confounding in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the inference from independent single species distribution models and a joint species distribution model. We present a method for measuring community confounding and develop a restricted version of our hierarchical model that orthogonalizes the habitat and species random effects. Our results indicate that variables associated with the severity and duration of the bark beetle epidemic suffer from community confounding. This implies that mammalian responses to the bark beetle epidemic are governed by interconnected habitat and community effects. Disentangling habitat and community effects can improve our understanding of the ecological system and possible management strategies. We evaluate restricted regression as a method for alleviating community confounding and distinguish it from other inferential methods for confounded models.

Predicting the potential invasion suitability of regions to cassava lacebug pests (Heteroptera: Tingidae: Vatiga spp.)

2014 ◽  
Vol 105 (2) ◽  
pp. 173-181
Author(s):  
S.I. Montemayor ◽  
P.M. Dellapé ◽  
M.C. Melo
Keyword(s):  
Central America ◽  
Species Distribution Models ◽  
Central Africa ◽  
Neotropical Region ◽  
Pest Species ◽  
Distribution Models ◽  
Bioclimatic Variables ◽  
The World ◽  
Dispersal Routes ◽  
The Tropics

AbstractCassava (Manihot esculenta Crantz) is one of the most important staple crops for small farmers in the tropics, feeding about 800 million people worldwide. It is currently cultivated in South and Central America, Africa and Asia. The genus Vatiga is widespread throughout the Neotropical region. Its species are sympatric and feed exclusively on cassava. The main objectives of this paper are: (1) to assess the potential distribution of Vatiga, one of the most relevant pests of cassava; (2) to project the resulting models onto the world; (3) to recognize areas with suitable and optimal climates (and thus, high probability) for future colonization, and (4) to compare this model with the harvested area of cassava analyzing the climatic variables required by both the host and the pest species. Species distribution models were built using Maxent (v3.3.3k) with bioclimatic variables from the WorldClim database in 2.5 arc min resolution across the globe. Our model shows that Vatiga has the potential to expand its current distribution into other suitable areas, and could invade other regions where cassava is already cultivated, e.g., Central Africa and Asia. Considering the results and the high host specificity of Vatiga, its recent appearance in Réunion Island (Africa) poses a serious threat, as nearby areas are potentially suitable for invasion and could serve as dispersal routes enabling Vatiga to reach the continent. The present work may help prevention or early detection of Vatiga spp. in areas where cassava is grown.

scholarly journals Climate-Driven Range Shifts Are Rapid Yet Variable Among Recreationally Important Coastal-Pelagic Fishes

2021 ◽  
Vol 8 ◽  
Author(s):  
Curtis Champion ◽  
Stephanie Brodie ◽  
Melinda A. Coleman
Keyword(s):  
Species Distribution Models ◽  
Management Strategies ◽  
Range Shifts ◽  
Ecosystem Structure ◽  
Distribution Models ◽  
Spanish Mackerel ◽  
Pelagic Fishes ◽  
Identification Of Species ◽  
Environmental Responses ◽  
Key Resources

Shifts in species distributions are occurring globally in response to climate change, but robust comparisons of redistribution rates among species are often prevented by methodological inconsistencies, challenging the identification of species that are most rapidly undergoing range shifts. In particular, comparable assessments of redistributions among harvested species are essential for identifying climate-driven changes in fishing opportunities and prioritising the development of management strategies. Here we utilise consistent datasets and methodologies to comparably analyse rates of climate-driven range shifts over 21 years for four recreationally important coastal-pelagic fishes (Australian bonito, Australian spotted mackerel, narrow-barred Spanish mackerel, and common dolphinfish) from the eastern Australian ocean warming hotspot. Latitudinal values corresponding to the poleward edge of species’ core oceanographic habitats were extracted from species distribution models (SDMs). Rates of poleward shifts in core oceanographic habitats ranged between 148.7 (i.e., common dolphinfish) and 278.6 (i.e., narrow-barred Spanish mackerel) km per decade over the study period. However, rates of redistribution varied by approximately 130 km per decade among species, demonstrating that subtle differences in species’ environmental responses can manifest in highly variable rates of climate-driven range shifts. These findings highlight the capacity for coastal-pelagic species to undergo rapid, yet variable, poleward range shifts, which have implications for ecosystem structure and the changing availability of key resources to fisheries.

scholarly journals Combining Satellite Remote Sensing and Climate Data in Species Distribution Models to Improve the Conservation of Iberian White Oaks (Quercus L.)

2020 ◽  
Vol 9 (12) ◽  
pp. 735
Author(s):  
Carlos Vila-Viçosa ◽  
Salvador Arenas-Castro ◽  
Bruno Marcos ◽  
João Honrado ◽  
Cristina García ◽  
...  
Keyword(s):  
Species Distribution ◽  
Ecosystem Functioning ◽  
Species Distribution Models ◽  
Hot Spot ◽  
Critical Role ◽  
Climate Variables ◽  
White Oak ◽  
Distribution Models ◽  
Narrow Endemic ◽  
Combined Models

The Iberian Peninsula hosts a high diversity of oak species, being a hot-spot for the conservation of European White Oaks (Quercus) due to their environmental heterogeneity and its critical role as a phylogeographic refugium. Identifying and ranking the drivers that shape the distribution of White Oaks in Iberia requires that environmental variables operating at distinct scales are considered. These include climate, but also ecosystem functioning attributes (EFAs) related to energy–matter exchanges that characterize land cover types under various environmental settings, at finer scales. Here, we used satellite-based EFAs and climate variables in species distribution models (SDMs) to assess how variables related to ecosystem functioning improve our understanding of current distributions and the identification of suitable areas for White Oak species in Iberia. We developed consensus ensemble SDMs targeting a set of thirteen oaks, including both narrow endemic and widespread taxa. Models combining EFAs and climate variables obtained a higher performance and predictive ability (true-skill statistic (TSS): 0.88, sensitivity: 99.6, specificity: 96.3), in comparison to the climate-only models (TSS: 0.86, sens.: 96.1, spec.: 90.3) and EFA-only models (TSS: 0.73, sens.: 91.2, spec.: 82.1). Overall, narrow endemic species obtained higher predictive performance using combined models (TSS: 0.96, sens.: 99.6, spec.: 96.3) in comparison to widespread oaks (TSS: 0.80, sens.: 92.6, spec.: 87.7). The Iberian White Oaks show a high dependence on precipitation and the inter-quartile range of Normalized Difference Water Index (NDWI) (i.e., seasonal water availability) which appears to be the most important EFA variable. Spatial projections of climate–EFA combined models contribute to identify the major diversity hotspots for White Oaks in Iberia, holding higher values of cumulative habitat suitability and species richness. We discuss the implications of these findings for guiding the long-term conservation of Iberian White Oaks and provide spatially explicit geospatial information about each oak species (or set of species) relevant for developing biogeographic conservation frameworks.

Distribution modelling of four scelionid egg parasitoids of green stink bugs (Hemiptera: Pentatomidae)

Biologia ◽  
2017 ◽  
Vol 72 (1) ◽  
Author(s):  
Tahmineh Tavanpour ◽  
M. Reza Mehrnejad ◽  
Alimorad Sarafrazi ◽  
Sohrab Imani
Keyword(s):  
Conservation Biology ◽  
Potential Distribution ◽  
Species Distribution Models ◽  
Management Strategies ◽  
Egg Parasitoids ◽  
Stink Bugs ◽  
Maximum Entropy Model ◽  
Distribution Models ◽  
Entropy Model ◽  
Distribution Modelling

AbstractSpecies distribution models are increasingly used in regional biodiversity assessments, pest management strategies, conservation biology, ecology and evolution. The Maximum Entropy model was applied to predict the potential distribution of four egg parasitoids, e.g.,

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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