The idea that variations in climate exert a strong influence on organisms’ distribution is centuries old [1] [2] [3] . Climate has a direct influence on vegetation distribution as plants are unable to evade adverse climate by sheltering or migration, and are limited to areas with suitable climate [4] [5] . Animals respond to climate directly when they actively habituate specific climatic zones, or indirectly where an animal’s distribution positively correlates to vegetation that is only found within specific climatic zones [4] . Whereas literature on the effects of climate on organism’s distribution dates back to the 5^th century, methods for generating climatic grid surfaces for use in species distribution models (SDMs) were first published in the mid-80s [3] . During this period advances in computer science enabled the creation of specialist tools [6] to study species distribution based on geocoded species distribution data and interpolated environmental variables [3] . Though not often described as such, these early specialist tools were Geographic Information Systems (GIS), and many are now found as stand-alone software (e.g. ANUSPLIN), as extensions for popular GIS software packages (e.g. QGIS), or as spatial analysis tools in popular statistical software (e.g. CRAN R). The BIOCLIM package, conceived by Henry Nix [3] [7] [8] , generates organisms’ distribution maps by delineating areas suitable for habituation based on sampled species distribution data and interpolated climatic variables.

There have been numerous efforts to generate freely available global and continental climatic datasets for use in SDMs [7] [8] [9] . Many of these datasets are based on the climatic variables conceived by Nix [3] , the most popular being the global climatic dataset known as WorldClim [10] [11] . This dataset is free for download on the Internet (http://www.worldclim.org/), and the variables represented in the WorldClim dataset, known as bioclimatic variables, have been used in various ecological studies [12] [13] [14] . The availability of datasets such as WorldClim is invaluable to biological studies in areas where climatic records are sparse or non-existent. However, many users do not critically assess the suitability of these datasets for their applications [15] , and errors associated with global datasets are often assumed to be negligible. Whereas in the past large datasets were produced by governments or large organizations with well laid out data standards [16] , in recent years the Internet has enabled many non-traditional actors to publish and widely distribute spatial data, and the topic of uncertainty in these datasets has begun gaining prominence [17] [18] . Data quality is a very important factor in the processing of the spatial data, and the data needs of users vary from person to person, organization to organization, or from application to application. Because of this it is the ultimate responsibility of the user to check the quality and suitability of data for their specific application. Spatial data which is suitable for one application need not be necessarily suitable for another [19] . Even in cases where data standards have been published, understanding what a climate dataset can or cannot deliver requires the user to have a working knowledge of what the basic spatial climate-forcing factors are at the scale of his/her study in order to correctly interpret trends and uncertainty in the data [15] .

In GIS, uncertainty is often described by the degree of error (uncertainty), or degree of accuracy (certainty) in data [6] [17] [19] . Errors are inevitable in any in any spatial data [19] , and should be recognized as an inherent part of any spatial dataset. Errors may creep in at any stage of data acquisition, transformation, and/or analysis [19] . Furthermore, GIS often deals with different layers of spatial data from numerous sources, collected using different sampling technics, geocoded using different map projections, and composed at different scales by the original authors. The combination of different data is one of the strengths of GIS [19] , but for this very reason GIS models often have complex errors propagated from data collection through to analysis [19] , and species distribution models are no exception. The correct conceptualization of uncertainties in individual data as well as the eventual propagation of uncertainties when the data is combined for used in SDMs is a growing consideration when presenting results of modeling [19] .

Uncertainty has been the subject of much research in GIS and Remote Sensing, and is recognized as one of the priority areas in GIS research [17] . Various authors have looked at uncertainty as a result of processes used in generating spatial data [6] [17] [19] , propagation of errors and uncertainty in processes used in spatial analysis [18] [19] [20] , and uncertainty in the results of spatial models [21] [22] . Daly [15] , in a review on the assessment of climatic datasets, discusses climatic datasets available to scientists and highlights the importance of familiarization with climate-forcing factors in one’s study area, familiarization with methods of spatial interpolation used to generate climatic data for one’s study area, and familiarization with errors inherent in the generated data.

Here, we first asses the relationship between climate-forcing factors and the climate of southern Kenya by assessing the relationship between the distance of a weather station from the ocean, its altitude, and the climate recorded. It has long been reported that a correlation exists between the altitude of a station, the rainfall and temperatures recorded [15] [23] [24] . Reports also indicate a correlation between a weather station’s distance from large water bodied and the rainfall recorded [15] [25] . Second, we assess the thematic uncertainty, and finally the temporal uncertainty in the WorldClim data for southern Kenya. Thematic uncertainty refers to the uncertainty in measures made for each bioclimatic variable, whereas temporal uncertainty refers to the uncertainty in time period represented by each bioclimatic variable. Thematic uncertainty is normally represented by one or more statistical measures of error or accuracy such as the Standard Error of Estimates (SEE) [6] [17] . Whereas thematic uncertainty has received a lot of attention in literature [17] , uncertainty in temporal representation of data, a key aspect when considering climatic data, has not [16] . Time is an important ecological consideration, and the onset of different seasons is often tied to environmental conditions that affect availability of food. In semi-arid areas such as Amboseli National Park in Kenya stress levels of baboons have been are known to vary with seasons which occur in different times of the year [26] [27] . Migration of wildebeest also seasonal [28] , and migration routes are associated with different times of the year. Time is often not dealt with explicitly in geospatial databases, and temporal information is often omitted except in databases designed explicitly for historical or timelapse/time-series studies [16] .

Kenya, situated approximately between latitudes 4˚21'N and 4˚28'S and between longitudes 34˚ and 42˚E, shares her boarders with Tanzania, South Sudan, Somalia, Ethiopia and Uganda, with the Indian Ocean on the south-eastern edge (Figure 1). The country lies within the Inter Tropical Convergence Zone (ITCZ), which determines the main features of annual rainfall and its seasonal variations [29] [30] [31] . Because of the local influence from the different geographic features, Kenya has diverse bioclimatic zones that include tropical, temperate, arid, and desert. The Rift Valley, a low-lying area characterized by arid climate, is found between highlands characterized by tropical and temperate climate. Areas near large water bodies such as Lake Victoria and the Indian Ocean exhibit tropical monsoon climate. The country also has large areas that can be described as semi-arid, arid, and desert. The area adjacent to and south of the Equator, referred to here as southern Kenya, has numerous sites of interest to ecologists, and these include the Amboseli National Park, The Maasai Mara National Park, and Nairobi National Park among others. Many of these sites do not have weather stations within or nearby, and this means the best estimates for the climate of these areas are from interpolated data such as the WorldClim dataset.

To investigate the correlation between climate and the geography of southern Kenya, climatic data consisting of monthly precipitation, average monthly minimum temperature, and average monthly maximum temperature for the years 2001 to 2012 were collected from 11 weather stations (Mombasa, Malindi, Voi, Makindu, Machakos, Narok, Nakuru, Meru, Laikipia, Garissa and Kisumu), all monitored by the Kenya Meteorological Department (KMD) (Figure 1). Following the description by O’donnel and Ignizio [7] , nineteen core bioclimatic variables were calculated for each weather station. A raster with grid values determined by the distance of each grid cell from the Kenyan coastline was generated using the Distance tool in ArcGIS 9.2 (ESRI, USA). Altitude data were downloaded from the United States Geological Survey (USGS) website

(https://earthexplorer.usgs.gov/ accessed 25th July 2017) and imported into ArcGIS 9.2 as a raster dataset. By identifying the grid value where a weather station is located, the altitude of each weather station (ALT) and distance from the ocean of each weather station (DIST) were determined. Scatter analysis was then used to assess the relationship between the bioclimatic variables and the geography of each weather station (ALT and DIST).

The bioclimatic variables envisaged by Nix [3] highlight average annual climatic patterns, extreme climatic monthly patterns, as well as extreme climatic seasonal patterns. The maximum temperature of the hottest month (BIO5), minimum temperature of the coldest month (BIO6), precipitation of the wettest month (BIO13), and precipitation of the driest month (BIO14) represent monthly climatic extremes. Seasonal bioclimatic variables described by Hijmans et al. [10] are calculated on a quarterly basis (i.e. the coldest three consecutive months, the hottest three consecutive months, the wettest three consecutive months, and the driest three consecutive months). The mean temperature of the wettest quarter (BIO8), mean temperature of the driest quarter (BIO9), mean temperature of the warmest quarter (BIO10), mean temperature of the coldest quarter (BIO11), precipitation of the wettest quarter (BIO16), precipitation of the driest quarter (BIO17), precipitation of the warmest quarter (BIO18) and precipitation of the coldest quarter (BIO19) represent seasonal climatic extremes.

To investigate the temporal uncertainty in the WorldClim dataset, we focused on generating time charts of the respective monthly and seasonal bioclimatic variables. Literature indicates that Kenya experiences two wet seasons, with precipitation experienced during the ‘‘long rains’’ (March-May) and also during the ‘‘short rains’’ (October-December), with two dry seasons in between [29] , [32] . To highlight the distribution of climatic extremes experienced at each weather station and provide a comparison with patterns in literature, the time charts were based on a hydrological calendar which starts in October and ends in September.

To investigate thematic uncertainty in the WorldClim dataset, data from WorldClim version 2.0 [11] consisting of nineteen core bioclimatic variables were downloaded from the WorldClim repository (worldclim.org), and the score of each bioclimatic variable at each respective weather station was noted. As WorldClim 2.0 is generated from data collected between the years 1970 and 2000 [11] , we used the bioclimatic variables calculated from each KMD weather station (years 2001 to 2012) as test data and calculated the Standard Error of Estimates, and the correlation (R²) between the WorldClim dataset and the KMD data.

In summary, we can conclude that the variations in climate observed in southern Kenya were influenced by the altitude the distance from the Indian Ocean, and that the influence of these climate forcing factors was well captured in the WorldClim dataset. Whereas the patterns in temperature, mainly influenced by altitude, showed strong local variation, patterns in precipitation showed global trends and were mainly influenced by the distance of an area from the ocean. The study concluded that the WorldClim dataset closely estimated average climatic conditions found in southern Kenya, and that thematic uncertainty was not a major concern when using the WorldClim dataset. However, the temporal uncertainty in the dataset, more so for bioclimatic variables that measure different aspects of precipitation, would be a concern for some. Scientists hoping to use the WorldClim dataset for species distribution models should carefully consider both the temporal characteristics of their study species and the temporal uncertainty of the WorldClim dataset before using the WorldClim dataset in their respective studies. Activities that are closely coupled with seasonality in organisms include migration and dispersal, and these activities often have an influence on the distribution of different species. Migration, describes as a seasonal long distance movement of individual or groups, is often tied to seasonal triggers. Dispersal in animals is described as the movement from one area to a breeding site, and is often tied to seasonal triggers. In plants seed dispersal and germination are closely related to seasonality, and only occur when climatic conditions are favorable. When studying a species that is found in a wide geographic area, the different patterns observed in precipitation patterns indicate that key behavior that is triggered by seasonal changes may occur at different times in different areas, and this may not be clear when using the WorldClim dataset.

This research was supported by the Mwangaza Foundation. I would like to thank the Amboseli Baboon Re-search Project for their continued support. I would also like to thank Prof. Jeanne Altmann, Prof. Susan Alberts, Prof. Elizabeth Archie, and Vivian Atieno Oudu for their encouragement and support.

The authors declare no conflicts of interest regarding the publication of this paper.

Wango, T.J.L., Musiega, D. and Mundia, C.N. (2018) Assessing the Suitability of the WorldClim Dataset for Ecological Studies in Southern Kenya. Journal of Geographic Information System, 10, 643-658. https://doi.org/10.4236/jgis.2018.106033