A data-integration approach to correct sampling bias in species distribution models using multiple datasets of breeding birds in the Swiss Alps

2021 ◽  
pp. 101501
Author(s):  
Nasrin Amini Tehrani ◽  
Babak Naimi ◽  
Michel Jaboyedoff
Keyword(s):  
Data Integration ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Breeding Birds ◽  
Swiss Alps ◽  
Distribution Models ◽  
Multiple Datasets ◽  
Integration Approach

Very high resolution environmental predictors in species distribution models

2013 ◽  
Vol 38 (1) ◽  
pp. 79-96 ◽  
Author(s):  
Jean-Nicolas Pradervand ◽  
Anne Dubuis ◽  
Loïc Pellissier ◽  
Antoine Guisan ◽  
Christophe Randin
Keyword(s):  
High Resolution ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Environmental Data ◽  
Swiss Alps ◽  
Fine Scale ◽  
Distribution Models ◽  
Model Quality ◽  
Vegetation Height ◽  
Very High

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

scholarly journals Sampling bias correction in species distribution models by quasi-linear Poisson point process

2020 ◽  
Vol 55 ◽  
pp. 101015 ◽  
Author(s):  
Osamu Komori ◽  
Shinto Eguchi ◽  
Yusuke Saigusa ◽  
Buntarou Kusumoto ◽  
Yasuhiro Kubota
Keyword(s):  
Point Process ◽  
Species Distribution ◽  
Bias Correction ◽  
Poisson Point Process ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Distribution Models

Sampling bias in geographic and environmental space and its effect on the predictive power of species distribution models

2012 ◽  
Vol 10 (3) ◽  
pp. 305-315 ◽  
Author(s):  
Nadia Bystriakova ◽  
Mykyta Peregrym ◽  
Roy H.J. Erkens ◽  
Olesya Bezsmertna ◽  
Harald Schneider
Keyword(s):  
Species Distribution ◽  
Predictive Power ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Distribution Models ◽  
Environmental Space

scholarly journals The importance of correcting for sampling bias in MaxEnt species distribution models

2013 ◽  
Vol 19 (11) ◽  
pp. 1366-1379 ◽  
Author(s):  
Stephanie Kramer-Schadt ◽  
Jürgen Niedballa ◽  
John D. Pilgrim ◽  
Boris Schröder ◽  
Jana Lindenborn ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Distribution Models

scholarly journals Novel methods to correct for observer and sampling bias in presence‐only species distribution models

2021 ◽  
Vol 30 (11) ◽  
pp. 2312-2325
Author(s):  
Yohann Chauvier ◽  
Niklaus E. Zimmermann ◽  
Giovanni Poggiato ◽  
Daria Bystrova ◽  
Philipp Brun ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Distribution Models ◽  
Novel Methods

scholarly journals Inhomogeneous Poisson Point Process for Species Distribution Models: Relative Performance of Methods Accounting for Sampling Bias and Imperfect Detection

2021 ◽  
Author(s):  
Yannick MUGUMAARHAHAMA ◽  
Adandé Belarmain FANDOHAN ◽  
Arsene Ciza MUSHAGALUSA ◽  
Idelphonse Akoeugnigan SODE ◽  
Romain GLELE KAKAÏ
Keyword(s):  
High Precision ◽  
Hierarchical Model ◽  
Species Distribution ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Abundant Species ◽  
Distribution Models ◽  
Imperfect Detection ◽  
Point Counts ◽  
Virtual Species

Species distribution models have become tools of great importance in ecology since the advanced knowledge of suitable habitat of species is needed in the process of the world's biodiversity conservation. Models that use presence-only data are of great interests and are widely used in ecology due to their easy access. However, these models do not estimate accurately the true spatial species distribution based solely on presence-only data since they do not account for biases induced by the sampling techniques used and imperfect detection. To address this gap, Hierarchical integrated models have been recently introduced. Through this study, we assessed the relative performance of these new SDMs models using simulated data. The performance of the models was tested by comparing the estimates of parameters of the distribution models they provide with parameters used to simulate the distribution of the virtual species. The best model was the one whose estimates were close to the true distribution parameters of the virtual species. Results showed that analyzing Presence-only data in conjunction with Point-counts data through the Dorazio's Hierarchical model produced estimates of the coecients of the species intensity models with high precision and less bias while the Koshkina integrated model showed poor performance. Site-occupancy data, being not informative of species abundance, did not allow reducing biases in Presence-only data. The Dorazio's Hierarchical model produced estimates with high precision even with low detection probability. We have also found that the species rarity tends to in ate the variability of the models' estimates making modelling abundant species to be more accurate than modelling less abundant species. Hence, to model the species distribution with high precision based on Presence-only data, additional Point-counts data are required to account for sampling bias and imperfect detection.

scholarly journals Dynamic Species Distribution Models in the Marine Realm: Predicting Year-Round Habitat Suitability of Baleen Whales in the Southern Ocean

2021 ◽  
Vol 8 ◽  
Author(s):  
Ahmed El-Gabbas ◽  
Ilse Van Opzeeland ◽  
Elke Burkhardt ◽  
Olaf Boebel
Keyword(s):  
Species Distribution ◽  
Environmental Conditions ◽  
Habitat Suitability ◽  
Species Distribution Models ◽  
Sampling Bias ◽  
Background Information ◽  
Baleen Whale ◽  
Distribution Models ◽  
Baleen Whales ◽  
Marine Realm

Species distribution models (SDMs) relate species information to environmental conditions to predict potential species distributions. The majority of SDMs are static, relating species presence information to long-term average environmental conditions. The resulting temporal mismatch between species information and environmental conditions can increase model inference’s uncertainty. For SDMs to capture the dynamic species-environment relationships and predict near-real-time habitat suitability, species information needs to be spatiotemporally matched with environmental conditions contemporaneous to the species’ presence (dynamic SDMs). Implementing dynamic SDMs in the marine realm is highly challenging, particularly due to species and environmental data paucity and spatiotemporally biases. Here, we implemented presence-only dynamic SDMs for four migratory baleen whale species in the Southern Ocean (SO): Antarctic minke, Antarctic blue, fin, and humpback whales. Sightings were spatiotemporally matched with their respective daily environmental predictors. Background information was sampled daily to describe the dynamic environmental conditions in the highly dynamic SO. We corrected for spatial sampling bias by sampling background information respective to the seasonal research efforts. Independent model evaluation was performed on spatial and temporal cross-validation. We predicted the circumantarctic year-round habitat suitability of each species. Daily predictions were also summarized into bi-weekly and monthly habitat suitability. We identified important predictors and species suitability responses to environmental changes. Our results support the propitious use of dynamic SDMs to fill species information gaps and improve conservation planning strategies. Near-real-time predictions can be used for dynamic ocean management, e.g., to examine the overlap between habitat suitability and human activities. Nevertheless, the inevitable spatiotemporal biases in sighting data from the SO call for the need for improving sampling effort in the SO and using alternative data sources (e.g., passive acoustic monitoring) in future SDMs. We further discuss challenges of calibrating dynamic SDMs on baleen whale species in the SO, with a particular focus on spatiotemporal sampling bias issues and how background information should be sampled in presence-only dynamic SDMs. We also highlight the need to integrate visual and acoustic data in future SDMs on baleen whales for better coverage of environmental conditions suitable for the species and avoid constraints of using either data type alone.

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