Species distribution models and local ecological knowledge in marine protected areas: The case of Os Miñarzos (Spain)

2016 ◽  
Vol 124 ◽  
pp. 66-77 ◽  
Author(s):  
Noela Sánchez-Carnero ◽  
Daniel Rodríguez-Pérez ◽  
Elena Couñago ◽  
Frank Le Barzik ◽  
Juan Freire
Author(s):  
Marina Jankovic ◽  
Marija Milicic ◽  
Dimitrije Radisic ◽  
Dubravka Milic ◽  
Ante Vujic

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.


2009 ◽  
Vol 52 (3-4) ◽  
pp. 154-165 ◽  
Author(s):  
Leopoldo C. Gerhardinger ◽  
Eduardo A.S. Godoy ◽  
Peter J.S. Jones

2021 ◽  
Author(s):  
Angel Delso ◽  
Jesús Muñoz ◽  
Javier Fajardo

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.


2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang

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.


Sign in / Sign up

Export Citation Format

Share Document