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Using a climate classification to model the distribution of plants

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The results obtained from a climatic classification using biological-sense indexes are presented to model the distribution of plants. It is part of the results of the climatic studies carried out in the Southern Region of Ecuador, and in specific,
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  Using a climate classification to model the distribution of plants DOI: 10.13140/RG.2.2.19949.33765 Orlando Hilarión Álvarez Hernández Independent researcher. E-mail: orlando21alvarez@gmail.com. Orcid: No. 0000-0002-9798-8363 Abstract. The results obtained from a climatic classification using biological-sense indexes are presented to model the distribution of plants. It is part of the results of the climatic studies carried out in the Southern Region of Ecuador, and in specific, those related to the biotemperature and the aspects that have to do with the precipitations and the drought indices. Key words: Climatic classification, vegetation indices, Southern Region of Ecuador.  1.   Introduction Climate has a decisive influence on both crop viability and the floristic distribution and composition of natural ecosystems. In his article entitled "A Climate Classification Outline Using Biologically Meaningful Indices for Modeling Plant Distribution", Dr. Gregory John Schmidt (2018), of the United States Department of Agriculture, states that seven indexes were calculated to provide a basis for an alternative climate classification in order to be physiologically informative for modelling plant distribution. Indexes retain real-world units that can be directly related to climate change. Larger categories of classification approximate the main vegetation formations, but indices can be subdivided into smaller, regular increments without the presumption that any given threshold implies accuracy in the interpretation. This climate classification is applied in this study to model the distribution of plant species, based on the results of climate studies conducted in the Southern Region of Ecuador, especially those related to biotemperature and aspects that have to do with rainfall and drought rates. 2. Materials and methods Schmidt's work (2018) used a moisture index which is the ratio of potential precipitation/evapotranspiration (P/PTE) of annual totals (correlating with the dominant vegetation structure, i.e. desert to savannah to forest). This ranking follows the breakpoints used in Holdridge (1947) in a base 2 logarithm increment record. The PET was calculated using a hybrid between the Hamon method (using temperature and length of day) and a term of the Hargreaves-Samoni method (daily temperature range as indirect relative humidity indicator and cloud cover) (Lu et al., 2005). The final values were calibrated to match the average PET calculated for a known mid-latitude wet climate region (Eastern United States (United States)) using Holdridge's biotemperature method (Lugo et al., 1999). The surplus is the sum of each monthly precipitation that exceeds the PET. A surplus of more than 25 mm may be sufficient to overcome soil reabsorption in the upper 25 cm where half of  the root mass is located and allow seasonal growth of mesophytic vegetation in semi-arid climates. In true deserts, this can happen only stochastically in different years, excluding perennial mesophytes. The deficit is the sum of each monthly PET that exceeds precipitation. In most vegetation types, 95% of the root mass is within 150 cm of the surface, to which the depth is usually between 100 and 200 mm of available water retention capacity. A deficit of more than 150 mm could potentially use all available soil water, so plants that can remain inactive during these deficit periods are required (Schmidt, 2018). Climates can be classified, according to humidity zones (Schmidt, 2018): Humidity zones 1a. P/PET > 2 ……… .. …………………………………………………………………… .Perhumid 1b. P/PET <2 2a. P/PET >1,414 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . Moist Humid 2b. P/PET <1,414 3a. P/PET >1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dry Humid 3b. P/PET < 1 4a. P/PET >0,707 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . Moist Sub-humid 4b. P/PET <0,707 5a. P/PET >0,5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dry sub-humid 5b. P/PET < 0,5 . P/PET > 0,25 . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Semiarid 6b. P/PET < 0,25 7a. P/PET > 0,125. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . Arid 7b. P/PET < 0,125. . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . … . . . . . . Perarid And macro-ombroclimates are classified according to both the P/PET ratio and the total monthly precipitation deficit/surplus and maximum actual evapotranspiration (RET): 1a. P/PET > 1 and total monthly deficit < 150 mm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Isopluvial 1b. P/PET <1 and the total monthly deficit >- 150 mm 2a. P/PET > 0,5 or total monthly surplus >- 25 mm or maximum RET >- 75 mm 2b. P/PET <0,5 with total monthly surplus < 25 mm and maximum RET <75 mm . . . .. . . . . . Isoxeric 3a. Maximum RET >- 75 mm (monsoon: precipitation in the warm season). ………….  Pluviothermal 3b. Maximum RET <75 mm (Mediterranean: summer drought) . . . . . . . . . . . . . .. . . . . . . Xerothermal PET is calculated by: PET = 216,7*6,108*e^ (17,26939*T/ (T+237,3))/ (T+273,3)*0,1651*(dl/12)*dM*0,2606*TR^0,5   Where: PET - monthly potential evapotranspiration T - Monthly average temperature TR - daily temperature range Dl - length of day dM - Days per month Or, by a simple approximation: PET = 5 * BT   Where BT is the average monthly temperature when positive, or zero otherwise ("bio-temperature").   In the case of the Southern Region of Ecuador (SRE), Holdridge's biotemperature method was used from the average temperature values per month and annual. It is considered that the main energy that can be used on the planet comes from solar radiation, so the calculation of it is performed for the southern region, using the Hottel method as recommended by Passamai (2000, cited by Alvarez et al ., 2013), for which it was necessary to prepare, first, the numerical altitude model (NAM) of the study region. The data was used provided by the Shuttle Radar Topography Mission, with elevation points every 90 meters. A linear Krigging analysis was performed for the study region with a grid step of 0,001   (Figure 1). Fig. 1. Numerical altitude model with the station network used Starting with NAM, points were taken every 0,001   to calculate the values of solar radiation with clear skies for months, using the RSE SOL software (Molina et al , 2015), to complete the tasks of the 754 SENESCYT-UNL project while obtaining, monthly and annual maps of solar radiation with clear sky. With Envi 4.5 software was obtained orographic shadow values for Azimuth’s  main elevations and angles, calculating an average to obtain the map of orographic shadows (Fig. 2) of the southern region of Ecuador, considering the same network. Shadow maps obtained from the numerical altitude model were used for the following combinations of elevation and azimuth angles: (15, 45); (15, 135); (15, 225); (15, 315); (30,  45); (30, 135); (30, 225); (30, 315); (45, 45); (45, 135); (45, 225); (45, 315); (60, 45); (60, 135); (60, 225); (60, 315). From these maps, average maps were made according to the North and South azimuths, and finally an average was made to obtain the shadows of the entire project area. Subsequently, and using albedo and cloud data provided by NASA (NASA, 2016), albedo and cloudy maps were drawn up for months and annually with the same characteristics as used for the mapping of solar radiation, with the aim of performing operations between maps. Radiation maps were finally obtained taking into account cloud coverage and orographic shadows Fig. 2. Map of Orographic Shadows of the SRE The information provided by INAMHI was used in the climate summaries from 1991 to 2011, corresponding to the weather stations of the southern region of Ecuador, whose network can be seen in Figure 1. Of the total of 29 stations that appear in the INAMHI yearbooks, only 10 of them could be obtained. Stations and type data are presented in Table 1 (Alvarez and Montaño, 2017). As you can see from Table 1, there is only one main climatological station, seven climatological and two agro-meteorological. According to the information contained in the INAMHI yearbooks, monthly statistical values published in the Yearbooks have been obtained on the basis of three daily observations (07, 13 and 19 hours), regardless of the type of station. The object of this processing is to provide the user with homogeneous statistics information at the national level. The data series have many gaps, so the individual years were filled, whenever feasible, using polynomial equations from the previous and successive monthly data. The all stations annual data were later introduced in Statgraphic Centurion X software ® , to obtain single or multiple regression equations in order to complete the missing data.    Table 1. Relationship of existing INAMHI stations in the southern region of Ecuador (AP-agro-meteorological; PV - precipitation; Co - climatological; PG - rain; CP - main climatological) LONG   LAT   ALT   CODE   NAME OF STATION   TYPE   -79.2   -4.04   2160   M033   LA ARGELIA-LOJA   AP   -79.78   -3.33   40   M040   PASAJE   PV   -79.23   -3.62   2525   M142   SARAGURO   CO   -79.27   -4.22   1453   M143   MALACATOS   CO   -79.24   -4.31   1560   M145   QUINARA INAMHI   CO   -79.55   -4.33   1950   M146   CARIAMANGA   AP   -79.17   -4.37   1835   M147   YANGANA   CO   -79.95   -4.1   1984   M148   CELICA   CO   -79.43   -4.23   2042   M149   GONZANAMA   CO   -79.43   -4.58   1672   M150   AMALUZA INAMHI   CO   -80.24   -4.38   223   M151   ZAPOTILLO   CO   -80.06   -3.56   60   M179   ARENILLAS   CO   -79.62   -3.7   1100   M180   ZARUMA   CO   -79.26   -3.73   2525   M432   SAN LUCAS INAMHI   PV   -79.79   -4.32   1510   M434   SOZORANGA INAMHI   PG   -80.03   -4.02   1250   M435   ALAMOR   PV   -80.2   -4.28   328   M437   SAUCILLO(ALAMOREN)   PV   -79.46   -4.63   2094   M438   JIMBURA   PV   -79.81   -4.36   734   M439   SABIANGO INAMHI   PV   -79.58   -3.32   290   M481   USHCURRUMI   PV   -80.2   -3.54   60   M482   CHACRAS   CO   -78.57   -3.65   820   M502   EL PANGUI   PV   -79.07   -3.96   1620   M503   SAN FRANCISCO -SAN RAMON   PV   -78.64   -3.91   650   M506   PAQUISHA   PV   -79.65   -4.05   1808   M515   CATACOCHA   PV   -79.69   -4.32   2410   M544   COLAISACA   PV   -79.7   -3.68   1126   M773   PINNAS   PV   -80.06   -4.12   1739   MB87   POZUL-COLEGIO AGROP. RODRIGUEZ   CO   -79.43   -4.14   1835   MB88   NAMBACOLA-COLEGIO AGROP. CUEVA   CP   The average temperatures, maximum and minimum, were taken from the values reported by each station, and were applied an increase by height to take them to sea level, using a thermal
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