Application of species distribution models for protected areas threatened by invasive plants

2016 ◽  
Vol 34 ◽  
pp. 1-7 ◽  
Author(s):  
Jana Pěknicová ◽  
Kateřina Berchová-Bímová
Keyword(s):  
Protected Areas ◽  
Invasive Plants ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Distribution Models

scholarly journals New findings on protected and strictly protected species confirm the value of the prime hoverfly area network

2018 ◽  
pp. 63-71
Author(s):  
Marina Jankovic ◽  
Marija Milicic ◽  
Dimitrije Radisic ◽  
Dubravka Milic ◽  
Ante Vujic
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Small Sample Size ◽  
Small Sample ◽  
Area Network ◽  
Distribution Models ◽  
Environmental Pressures ◽  
New Findings ◽  
Using Data

With environmental pressures on the rise, the establishment of pro?tected areas is a key strategy for preserving biodiversity. The fact that many species are losing their battle against extinction despite being within protected areas raises the question of their effectiveness. The aim of this study was to evaluate established Priority Hoverfly Areas (PHAs) and areas that are not yet but could potentially be included in the PHA network, using data from new field surveys. Additionally, species distribution models have been created for two new species recognized as important and added to the list of key hoverfly species. Maps of potential distribution of these species were superimposed on maps of protected areas and PHAs to quantify percentages of overlap. The results of this study are not statisti?cally significant, which could be influenced by a small sample size. However, the results of species distribution models and the extent of overlap with PHAs confirm the utility of these expert-generated designations.

scholarly journals Niche conservatism in a plant with long invasion history: the case of the Peruvian peppertree (Schinus molle, Anacardiaceae) in Mexico

2020 ◽  
Vol 153 (1) ◽  
pp. 3-11
Author(s):  
Jorge E. Ramírez-Albores ◽  
Gustavo Bizama ◽  
Ramiro O. Bustamante ◽  
Ernesto I. Badano
Keyword(s):  
Invasive Plants ◽  
Species Distribution ◽  
Environmental Conditions ◽  
Species Distribution Models ◽  
Large Fraction ◽  
Niche Conservatism ◽  
Invasion History ◽  
Distribution Models ◽  
Schinus Molle ◽  
Suitable Habitats

Background and aim – Invasive plants should only colonize habitats meeting the environmental conditions included in their native niches. However, if they invade habitats with novel environmental conditions, this can induce shifts in their niches. This may occur in plants with long invasion histories because they interacted with the environmental conditions of invaded regions over long periods of time. We focused on this issue and evaluated whether the niche of the oldest plant invader reported in Mexico, the Peruvian peppertree, is still conserved after almost 500 years of invasion history. Methods – We compared climatic niches of the species between the native and invaded region. We later used species distribution models (SDM) to visualize the geographical expression of both niches in Mexico. Results – The invasive niche of the Peruvian peppertree is fully nested within the native niche. Although this suggests that the niche is conserved, this also indicates that a large fraction of the native niche is empty in the invaded region. The SDM from the native region indicated that Mexico contains habitats meeting the conditions included in this empty fraction of the native niche and, thus, this invasion should continue expanding. Nevertheless, the SDM calibrated with data from the invaded region indicated that peppertrees have colonized all suitable habitats indicated by its invasive niche and, thus, their populations should no longer expand. Conclusion – Our results suggests that the niche of the Peruvian peppertree is partially conserved in Mexico. This may have occurred because individuals introduced into Mexico constituted a small, nonrepresentative sample of the full niche of the species.

scholarly journals Protected Area Networks Do Not Represent Unseen Diversity

2021 ◽  
Author(s):  
Angel Delso ◽  
Jesús Muñoz ◽  
Javier Fajardo
Keyword(s):  
Protected Areas ◽  
Species Distribution ◽  
Conservation Planning ◽  
Protected Area ◽  
Species Distribution Models ◽  
Systematic Conservation Planning ◽  
Knowledge Gaps ◽  
Distribution Models ◽  
Current Area ◽  
Planning Approaches

Abstract Most existing protected area networks are usually biased to protect charismatic species or showy landscapes. We hypothesized that conservation networks designed including unseen diversity –groups usually species-rich but consisting of inconspicuous taxa, or affected by knowledge gaps– would be more efficient than networks ignoring those groups. To test this hypothesis, we created species distribution models for 3,006 species of arthropods and determined which were represented in three networks of different size and biogeographic origin. We assessed the efficiency of each network using spatial prioritization to measure its completeness –increment needed to achieve conservation targets– and specificity –how much overlap the priority areas based on unseen diversity with existing networks. We find that representativeness of unseen diversity in existing protected areas –extrinsic representativeness– was low, as ~40% of unseen diversity species were unprotected. We also find that existing networks should be expanded by an additional ~26-46% of their current area to complete targets, and that specificity can be as low as 8.8%, meaning that existing networks are not efficient to conserve unseen diversity. We conclude that information on unseen diversity must be included in systematic conservation planning approaches to design more efficient and ecologically representative protected areas.

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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