Interannual climate variability data improves niche estimates in species distribution models

Aim: Climate is an essential element of species' niche estimates in many current ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values. Yet, climate can also be described as spatial or temporal variability for variables like temperature or precipitation. Such variability, spatial or temporal, offers additional insights into niche properties. Here, we test to what degree spatial variability and long-term temporal variability in temperature and precipitation improve SDM predictions globally. Location: Global. Time period: 1979-2013. Major taxa studies: Mammal, Amphibians, Reptiles. Methods: We use three different SDM algorithms, and a set of 833 amphibian, 779 reptile, and 2211 mammal species to quantify the effect of spatial and temporal climate variability in SDMs. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). Results: Mean performance of SDMs with climatic means as predictors was TSS=0.71 and AUC=0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS=0.74, mean AUC=0.92), as does the inclusion of temporal variability (mean TSS=0.80, mean AUC=0.94). Including both spatial and temporal variability in SDMs shows similarly high TSS and AUC scores. Main conclusions: Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in exotherm groups such as amphibians and reptiles, while for endotermic mammals no such improvement was observed. These results indicate that more detailed information about temporal climate variability offers a highly promising avenue for improving niche estimates and calls for a new set of standard bioclimatic predictors in SDM research.

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Using species distribution models to assess the long‐term impacts of changing oceanographic conditions on abalone density in south east Australia

Ecography ◽

10.1111/ecog.05181 ◽

2020 ◽

Vol 43 (7) ◽

pp. 1052-1064 ◽

Cited By ~ 1

Author(s):

Mary A. Young ◽

Eric A. Treml ◽

Jutta Beher ◽

Molly Fredle ◽

Harry Gorfine ◽

...

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Distribution Models ◽

Oceanographic Conditions

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A hydrochemical modelling framework for combined assessment of spatial and temporal variability in stream chemistry: application to Plynlimon, Wales

Hydrology and Earth System Sciences ◽

10.5194/hess-5-49-2001 ◽

2001 ◽

Vol 5 (1) ◽

pp. 49-58 ◽

Cited By ~ 10

Author(s):

H.J. Foster ◽

M.J. Lees ◽

H.S. Wheater ◽

C. Neal ◽

B. Reynolds

Keyword(s):

Land Use ◽

Land Use Change ◽

Spatial Variability ◽

Temporal Variability ◽

Biological Diversity ◽

Spatial And Temporal Variability ◽

Stream Chemistry ◽

Two Component ◽

End Members

Abstract. Recent concern about the risk to biota from acidification in upland areas, due to air pollution and land-use change (such as the planting of coniferous forests), has generated a need to model catchment hydro-chemistry to assess environmental risk and define protection strategies. Previous approaches have tended to concentrate on quantifying either spatial variability at a regional scale or temporal variability at a given location. However, to protect biota from ‘acid episodes’, an assessment of both temporal and spatial variability of stream chemistry is required at a catchment scale. In addition, quantification of temporal variability needs to represent both episodic event response and long term variability caused by deposition and/or land-use change. Both spatial and temporal variability in streamwater chemistry are considered in a new modelling methodology based on application to the Plynlimon catchments, central Wales. A two-component End-Member Mixing Analysis (EMMA) is used whereby low and high flow chemistry are taken to represent ‘groundwater’ and ‘soil water’ end-members. The conventional EMMA method is extended to incorporate spatial variability in the two end-members across the catchments by quantifying the Acid Neutralisation Capacity (ANC) of each in terms of a statistical distribution. These are then input as stochastic variables to a two-component mixing model, thereby accounting for variability of ANC both spatially and temporally. The model is coupled to a long-term acidification model (MAGIC) to predict the evolution of the end members and, hence, the response to future scenarios. The results can be plotted as a function of time and space, which enables better assessment of the likely effects of pollution deposition or land-use changes in the future on the stream chemistry than current methods which use catchment average values. The model is also a useful basis for further research into linkage between hydrochemistry and intra-catchment biological diversity. Keywords: hydrochemistry, End-Member Mixing Analysis (EMMA), uplands, acidification

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Analysis of Long-Term Solar Resource Variability Using NSRDB Version 3

10.5194/ems2021-193 ◽

2021 ◽

Author(s):

Manajit Sengupta ◽

Aron Habte

Keyword(s):

Solar Radiation ◽

Spatial Variability ◽

Temporal Variability ◽

Radiative Transfer Model ◽

Transfer Model ◽

Spatial And Temporal Variability ◽

Cloud Optical Depth ◽

Resource Variability ◽

Solar Resource

<p>Understanding long-term solar resource variability is essential for planning and deployment of solar energy systems. These variabilities occur due to deterministic effects such as sun cycle and nondeterministic such as complex weather patterns. The NREL&#8217;s National Solar Radiation Database (NSRDB) provides long term solar resource data covering 1998- 2019 containing more than 2 million pixels over the Americas and gets updated on an annual basis. This dataset is satellite-based and uses a two-step physical model for it&#8217;s development. In the first step we retrieve cloud properties such as cloud mask, cloud type, cloud optical depth and effective radius. The second step uses a fast radiative transfer model to compute solar radiation. &#160;This dataset is ideal for studying solar resource variability. For this study, NSRDB version 3 which contains data from 1998-2017 on a half hourly and 4x4 km temporal and spatial resolution was used. The study analyzed the spatial and temporal trend of solar resource of global horizontal irradiance (GHI) and direct normal irradiance (DNI) using long-term 20-years NSRDB data. The coefficient of variation (COV) was used to analyze the spatio-temporal interannual and seasonal variabilities. The spatial variability was analyzed by comparing the center pixel to neighboring pixels. The spatial variability result showed higher COV as the number of neighboring pixels increased. Similarly, the temporal variability for the NSRDB domain ranges on average from &#177;10% for GHI and &#177;20% for DNI. Furthermore, the long-term variabilities were also analyzed using the K&#246;ppen-Geiger climate classification. This assisted in the interpretation of the result by reducing the originally large number of pixels into a smaller number of groups. This presentation will provided a unique look at long-term spatial and temporal variability of solar radiation using high-resolution satellite-based datasets.</p>

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Integrating Gap Analysis and Corridor Design with Less Used Species Distribution Models to Improve Conservation Network for Two Rare Mammal Species (Gazella bennettii and Vulpes cana) in Central Iran

Contemporary Problems of Ecology ◽

10.1134/s1995425521050103 ◽

2021 ◽

Vol 14 (5) ◽

pp. 550-563

Author(s):

Shiva Torabian ◽

Mehrdad Ranaie ◽

Hossein Akbari Feizabadi ◽

Laurie Chisholm

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Gap Analysis ◽

Central Iran ◽

Distribution Models ◽

Mammal Species ◽

Conservation Network

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Presence-only species distribution models to predict suitability over a long-term study for a species with a growing population

Wildlife Society Bulletin ◽

10.1002/wsb.497 ◽

2014 ◽

Vol 39 (1) ◽

pp. 218-224 ◽

Cited By ~ 3

Author(s):

Tiffany M. MCfarland ◽

Joseph A. Grzybowski ◽

Heather A. Mathewson ◽

Michael L. Morrison

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Distribution Models ◽

Term Study ◽

Long Term Study ◽

Growing Population

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Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Ecological Indicators ◽

10.1016/j.ecolind.2015.08.008 ◽

2016 ◽

Vol 65 ◽

pp. 122-132 ◽

Cited By ~ 13

Author(s):

Mathias Kuemmerlen ◽

Stefan Stoll ◽

Andrea Sundermann ◽

Peter Haase

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Monitoring Data ◽

Freshwater Species ◽

Distribution Models ◽

Long Term Monitoring ◽

Term Monitoring

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Improving performance and transferability of small mammal species distribution models

Transactions of the Royal Society of South Australia ◽

10.1080/03721426.2018.1513770 ◽

2018 ◽

Vol 142 (2) ◽

pp. 143-161

Author(s):

Nerissa A. Haby ◽

Steven Delean ◽

Barry W. Brook

Keyword(s):

Species Distribution ◽

Small Mammal ◽

Species Distribution Models ◽

Distribution Models ◽

Mammal Species ◽

Small Mammal Species

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Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

Agronomy ◽

10.3390/agronomy8120298 ◽

2018 ◽

Vol 8 (12) ◽

pp. 298 ◽

Cited By ~ 2

Author(s):

Wenxuan Guo

Keyword(s):

Spatial Variability ◽

Temporal Variability ◽

Yield Variability ◽

High Plains ◽

Spatial And Temporal Variability ◽

Cotton Yield ◽

Site Specific ◽

Growing Seasons ◽

Specific Management

Understanding spatial and temporal variability patterns of crop yield and their relationship with soil properties can provide decision support to optimize crop management. The objectives of this study were to (1) determine the spatial and temporal variability of cotton (Gossypium hirsutum L.) lint yield over different growing seasons; (2) evaluate the relationship between spatial and temporal yield patterns and apparent soil electrical conductivity (ECa). This study was conducted in eight production fields, six with 50 ha and two with 25 ha, on the Southern High Plains (SHP) from 2000 to 2003. Cotton yield and ECa data were collected using a yield monitor and an ECa mapping system, respectively. The amount and pattern of spatial and temporal yield variability varied with the field. Fields with high variability in ECa exhibited a stronger association between spatial and temporal yield patterns and ECa, indicating that soil properties related to ECa were major factors influencing yield variability. The application of ECa for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and ECa variation patterns. For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons. Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs. For unstable yield, however, long-term management practices are difficult to implement. For these fields with unstable yield patterns, within season site-specific management can be a better choice. Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times.

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