Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?

Amiri, Mohammad Javad; Tarkesh, Mostafa; Jafari, Reza; Jetschke, Gottfried

doi:10.1016/j.ecoinf.2020.101060

Cited by 43 publications

(38 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike remote sensing, which mainly leverages the spectral signature of vegetation, climate data can provide information about environmental factors affecting species. Biophysical properties of vegetation can provide basic information on vegetation (Amiri et al 2020;Deblauwe et al 2016). For example, invasive species (Acacia melanoxylon R., Gleditsia triacanthos L.) often spread more widely in the dry season due to their unique biophysical characteristics to cope with water limitations (Godoy et al 2011).…”

Section: Contribution Of Bioclimatic Data In Sdmmentioning

confidence: 99%

“…For example, invasive species (Acacia melanoxylon R., Gleditsia triacanthos L.) often spread more widely in the dry season due to their unique biophysical characteristics to cope with water limitations (Godoy et al 2011). Overlooking this will create inconsistencies in the prediction of invasive species distributions (Amiri et al 2020;Bellard et al 2018;Truong et al 2017).…”

Section: Contribution Of Bioclimatic Data In Sdmmentioning

confidence: 99%

“…This data is very popular to use for invasive SDM (Hijmans et al 2005;Vega et al 2017). However, there are some uncertainties in this product as it can be affected by closeness to weather stations, inter-annual variability, and topographic heterogeneity (Amiri et al 2020;Fernandez et al 2013). As a result, WorldClim as well as other station-based climate data is always uncertain for the prediction of invasive species distribution when ground weather stations are sparse (Amiri et al 2020;Deblauwe et al 2016).…”

Section: Contribution Of Bioclimatic Data In Sdmmentioning

confidence: 99%

“…Advancements in remote sensing technology and statistical modeling increasingly support the prediction efficiency of SDM by reducing SDM-inherent limitations (He et al 2015). Remotely sensed rainfall and temperature data are available at the different temporal and spatial resolutions but are not yet widely used (Amiri et al 2020;Deblauwe et al 2016;Fernandez et al 2013). Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Enhanced Vegetation Index (EVI), and other EO derived data are still under-used in SDM though their potential is unquestionable (Feilhauer et al 2012;He et al 2015;Klerk and Buchanan 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Besides the contributions of remotely sensed environmental variables, bioclimatic variables are equally important for predicting invasive species distribution (Amiri et al 2020;Bradley et al 2018;Deblauwe et al 2016;Fernandez et al 2013). Though it has the coarse resolution, climate variables alone have great potential to predict invasive species while the presence of remotely sensed environmental variables supports to provide spatial detail (Truong et al 2017;Zimmermann et al 2007).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

2020

View full text Add to dashboard Cite

Prediction and modeling using integrated datasets and expertise from various disciplines greatly improve the management of invasive species. So far several attempts have been made to predict, handle, and mitigate invasive alien species impacts using specific efforts from various disciplines. Yet, the most persuasive approach is to better control its invasion and subsequent expansion by making use of cross-disciplinary knowledge and principles. However, the information in this regard is limited and experts from several disciplines have sometimes difficulties understanding well each other. In this respect, the focus of this review was to overview challenges and opportunities in integrating bioclimatic, remote sensing variables, and species distribution models (SDM) for predicting invasive species in data-poor regions. Google Scholar search engine was used to collect relevant papers, published between 2005–2020 (15 years), using keywords such as SDM, remote sensing of invasive species, and contribution of remote sensing in SDM, bioclimatic variables, invasive species distribution in data-poor regions, and invasive species distribution in Ethiopia. Information on the sole contribution of remote sensing and bioclimatic datasets for SDM, major challenges, and opportunities for integration of both datasets are systematically collected, analyzed, and discussed in table and figure formats. Several major challenges such as quality of remotely sensed data and its poor interpretation, inappropriate methods, poor selection of variables, and models were identified. Besides, the availability of Earth Observation (EO) data with high spatial and temporal resolution and their capacity to cover large and inaccessible areas at a reasonable cost, as well as progress in remote sensing data integration techniques and analysis are among the opportunities. Also, the impacts of important sensor characteristics such as spatial and temporal resolution are crucial for future research prospects. Similarly important are studies analyzing the impacts of interannual variability of vegetation and land use patterns on invasive SDM. Urgently needed are clearly defined working principles for the selection of variables and the most appropriate SDM.

show abstract

Section: Contribution Of Bioclimatic Data In Sdmmentioning

confidence: 99%

Section: Contribution Of Bioclimatic Data In Sdmmentioning

confidence: 99%

Section: Contribution Of Bioclimatic Data In Sdmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

2020

View full text Add to dashboard Cite

show abstract

Shifts in bioclimatic zones mirror climate change signals in a tropical agriculture‐dominated Bharathapuzha River basin of southern Western Ghats (India)

Nizar,

Thomas,

Jainet

et al. 2024

Intl Journal of Climatology

View full text Add to dashboard Cite

Assessing anthropogenic climate change in a regional context is challenging due to the spatial heterogeneity of climatic variables and is more complicated than at the global scale. Especially in the Tropics, such spatial variations are expected to increase, warranting the identification of homogeneous climatic zones for assessing regional climate change. The present study explores the ability of bioclimatic variables in defining regional climatic zones, and the detection of climate change therein. We hypothesize that the identification of homogeneous climatic zones based on bioclimatic variables could be an effective approach rather than the conventional extreme climate‐based indices to identify climate change signals. To demonstrate the hypothesis, bioclimatic variables representing the generalized climatic characteristics of a tropical river basin were derived from observed gridded datasets of rainfall and temperature. Clusters of homogeneous climatic zones were identified, and their temporal variations were analysed to examine the existence of climate change. The results indicate that despite the spatial heterogeneity in extreme climate‐based indices, the bioclimatic variables‐based approach renders a meaningful representation of the regional climatic pattern. Investigation of bioclimatic zones of the study area helped to identify a shift in its climatic zones with a slant towards drier conditions. Further, future changes in climatic zones were identified from 13 different GCMs that participated in the CMIP6, projecting drier conditions over the basin, with varying spatial extend based on future emission scenarios. The study significantly contributes towards the identification of climatologically fragile regions in changing climate, which is an essential component in developing any regional climate change adaptation and mitigation strategy.

show abstract

Mainstreaming remotely sensed ecosystem functioning in ecological niche models

Regos

Gonçalves

Arenas‐Castro

et al. 2022

Remote Sens Ecol Conserv

View full text Add to dashboard Cite

Biodiversity is declining globally at unprecedented rates. Ecological niche models (ENMs) are one of the most widely used toolsets to appraise global change impacts on biodiversity. Here, we identify a variety of advantages of incorporating remotely sensed ecosystem functioning attributes (EFAs) into ENMs. The development of ENMs that explicitly incorporate ecosystem functioning will allow a more holistic and integrative perspective of the habitat dynamics. The synergies between the increasingly available open‐access satellite images and cloud‐based platforms for planetary‐scale geospatial analysis offer an unprecedented opportunity to incorporate ecosystem processes and disturbances (such as fires, insect outbreaks or droughts) that have been so far largely neglected in ecological niche characterization and modelling. The most paradigmatic example of EFAs is the application of time series of spectral vegetation indices related to primary productivity and carbon cycle. EFAs related to surface energy balance and water cycles derived from remote sensing products such as land surface temperature or soil moisture enable a fine‐scale characterization of the species' niche—eventually improving the predictive performance of ENMs. All these advantages confirm that a new generation of ENMs based on such EFAs would offer great perspectives to increase our ability to monitor habitat suitability trends and population dynamics. However, despite the technical advances and increasing effort of remote sensing community to develop integrative EFAs, ENMs have yet to make full profit of the most recent developments by integrating them in ENMs. A coordinated agenda for remote sensing experts and ecological modellers will be essential over the coming years to bridge the gap between remote sensing and ecology disciplines and to take full (and timely) advantage of the fast‐growing body of Earth observation data and remote sensing technologies—with special emphasis on the development and testing of new variables related to key processes driving ecosystem functioning.

show abstract

Bioclimatic variables from precipitation and temperature records vs. remote sensing-based bioclimatic variables: Which side can perform better in species distribution modeling?

Cited by 43 publications

References 40 publications

Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities

Shifts in bioclimatic zones mirror climate change signals in a tropical agriculture‐dominated Bharathapuzha River basin of southern Western Ghats (India)

Mainstreaming remotely sensed ecosystem functioning in ecological niche models

Contact Info

Product

Resources

About