ecological niche modeling

Abstract: A new study in Kenya has spatially quantified the paradigm shift in the current and future malaria vectors ecology by use of BIOCLIM True or False model with IPCC HADCM3 projected future climate under A2a scenario. The study was designed to correlate the effect of climate change as an explanatory variable, among other factors, on malaria vectors distribution in Kenya. There has been reported malaria cases in new Counties that were never malaria endemic zones, thus the study seeks to research on ecological changes that can be as a result of changing environmental factors to support malaria vectors to thrive in new areas. Predictive modeling was done to investigate the spatial-temporal vector distribution under different IPCC future climate projections. The period from 1950 – 2000 was treated as the current climate scenario to explain where malaria vectors are existing as per vector spatial presence data and the prediction of the ecological niches where the vectors would also be found to thrive. Ecological Niche Modeling demonstrated significant alteration in suitability of malaria vector habitats in Kenya from the current ecological zones by the years 2020, 2050 and 2080. Most of the current malaria ecological niches were found to remain suitable while others turned out to be unsuitable after projection although new malaria hotspots were found to emerge. Prediction by the year 2050 demonstrated an alarming expansion of suitable malaria ecologies. By 2080 the predictions showed that the suitable ecologies will start to revert similar to the earlier suitability as in the current climate. Therefore, climate change in Kenya will adversely affect the environment at an alarming rate by 2050, but beyond that there will be a level of stabilization, where further change will trigger reversal to the past climate. This change in unsuitable to suitable malaria habitats can negatively affect the national effort to fight malaria under the ongoing towards zero campaign if strategic planning overlooks the possible effect of climate change among other factors on future dynamics of malaria distribution in Kenya.

Due to the high diversity of mammalian taxa which interact with humans in these regions it can be very dif- ficult to determine particular taxa involved in disease transmission. For the purpose of this study we have as- sumed that primates are the likely reservoir based on laboratory findings that indicated only primates show evidence of productive infection after being experimen- tally inoculated [4]. Other taxa examined, which did not demonstrate Tanapox virus-induced illness, include calves, lambs, pigs, goats, rabbits, guinea-pigs, and mice [4]. Many species of primates exist in the locations where human disease occurs. Human-primate interactions are also common in these locations, where primates may be sold, kept as pets, or serve as agricultural pests [13]. The ecological niche modeling (ENM) technique known as the Genetic Algorithm for Rule-Set Production (GARP) relates ecological information from the landscape of disease to create a set of rules that define factors asso- ciated with disease presence [14]. The final result, an (ENM) can be rendered on a map depicting the poten- tial geographic distribution for the pathogen. This study

Background: Due to climate change, the geographical distribution of sand flies during the last decades has shifted northward from latitudes below 45°N in southern Europe to latitudes just above 50 ○ N. Recent studies show that some phlebotomine sand flies were recorded in several parts of Germany and Belgium. In central Europe, some autochthone leishmaniasis cases are being recorded in regions traditionally regarded as leishmaniasis-free. An important challenge is to predict the geographical distribution of leishmaniasis vectors under new climatic conditions. In this study, we attempted to predict the current distribution of six leishmaniasis vectors in the Mediterranean basin and forecast species ’ geographical shift under future climate scenarios using an ensemble ecological niche modeling approach. Species records were obtained from scientific surveys published in the research literature between 2006 and 2016. A series of climate metrics describing temperature and precipitation in the study area under two climatic scenarios were obtained from WorldClim database. A consensus model was derived from six varieties of modeling approaches (regression, machine learning and classification techniques) in order to ensure valid prediction of distribution of vectors under different climate scenarios.

The distribution of these mosquito vectors, like other exophilic mosquitoes, are influenced by surrounding cli- mate and ecological variables. In addition, this distribution gives deep insights on the virus circulation between the bridging and main mosquito vector, reservoir bird and hu- man hosts. The findings of some previous models showed great potential in understanding the biology and ecology of WNV mosquito vectors in state of Florida [9, 16, 22], however, they encountered some limitations in emphasizing the interaction between ecological, climate, DEM variables and their overall influence on mosquito vector distribution at a local scale. Other models concluded the association between WNV transmission was either with forested and urban land [11, 44] or socioeconomic status [45]. However, their findings may not be applicable in other landscapes that encounter WNV transmission due to the heterogeneity in climate and landscape variables [46]. Moreover, some of the previous models predicted the spatial distribution risk of virus circulation based on sampling points rather than the flight range area of these mosquito vectors.

An alternative explanation, and one that we favor, is that the information essential to the different vegetation indices is – in the end – derived from the same remotely sensed images, and in many cases from the same actual bands of data. In this sense, while the different vegeta- tion indices (NDVI, EVI, LSWI, etc.) are not equivalent, the key information in one may overlap very broadly with that in another. NDVI, EVI and LSWI are related to leaf area index, canopy chlorophyll content, and water content, respectively [16]; these vegetation proper- ties are closely related to each other. In the case of large-scale modeling at relatively coarse spatial resolu- tion (e.g., 5-km gridcell in this study) we suspect that the important information in these vegetation indices may be highly correlated, such that one does not offer significant and consistent advantages over any other. Finally, in spite of known complications in EVI calcula- tions for images in which snow cover is present [16], we found no marked improvement of models built in Correlation by Summary Statistic

ENM, similar in many respects to species distribution modeling (SDM), is an innovative approach that associ- ates the occurrence locations of infectious diseases (i.e., presence data) with a set of environmental variables resulting in a geographic range prediction for disease cases and agents. Maxent, used in this study, is a max- imum entropy approach to presence-only distribution modeling that has shown a high predictive power even for small sample sizes [43,44]. However, the output from Maxent for evaluating the relative contribution of each independent variable to the model can be affected by high correlation amongst variables [45]. Prior to model- ing experiments, variable correlations were examined to eliminate issues of high multicollinearity. The Maxent modeling procedure assesses the importance of the indi- vidual and collective contributions that each variable makes to the distribution of human Brucellosis cases through 1) jackknife analysis, 2) average area under the curve (AUC) values for 10 model iterations and 3) average percentage contribution of each variable to the model. The prediction was projected outside of Inner Mongolia

In future work this model and template should be tested and applied further with independent data, ideally data that is less biased and not dependent on human access and human exposure. We also believe that a wider macro- ecology view and model prediction for rabies overall, and its niche is warranted, assuming that other rabies strains from Canada or more southern regions will enter Alaska sooner or later. This pathogen transport has been seen in other disease system with influenza being a prominent example of pathogen transport to high latitudes [52]. A wider socio-economic perspective to public health and rabies across scales is required. Such an approach will clarify how the findings of our model can be extended beyond the risk of human exposure to start to explain and manage the distribution of rabies in Alaskan wildlife.

Study on the mosquitoes of Iran has a long history and is conducted on different aspects such as fauna, distribution, parasitic infection, resistance to insecticides and modeling distri- bution (5, 6, 17–20). Culex theileri is reported from 27 out of 31 provinces of Iran including Ardabil, West Azarbaijan, East Azarbaijan, Bushehr, Charmahal and Bakhtiari, Fars, Gui- lan, Hamadan, Hormozgan, Ilam, Isfahan, Ker- man, Kermanshah, South Khorassan, North Khorassan, Khorassan-e Razavi, Khuzestan, Kohgiluyeh and Boyerahmad, Kordestan, Lorestan, Markazi, Mazandaran, Qom, Sistan and Baluchestan, Tehran, Yazd, and Zanjan (21).

ModEco incorporates a range of environmental niche models in dealing with presence-only, presence and absence, and abundance data. Specifically, as the observation data of many species contain presence-only data, ModEco provides three types of model solution: a) presenceonly model, such as Bioclim, Domain, and one-class SVMs; b) pseudo- absence model, such as two-class SVMs, maximum entropy, generalized linear model (GLM), artificial neural networks (ANN), classification tree, rough sets, and naive Bayes classification. In addition to commonly used pseudo-absence models, an iterative approach was also implemented in ModEco, i.e. after model prediction based on pseudo- absence data, ModEco generates the pseudo-absence data from the absences area of the prediction map and re-runs the model until the final prediction results are stabilized (Yu 2005). The third model solution is the implementation of background vs presence data models. Recent studies have demonstrated that background-based models are pro- mising in dealing with presence-only data (Elith et al. 2006, Phillips et al. 2009). In ModEco, we implemented max- imum entropy, support vector regression, GLM, and ANN as background-based models. Instead of using conventional pseudo-absence data generated from regions outside the presence data, the background-based models sample the ‘‘pseudo-absence data’’ from the whole study area, which results in certain types of conditional probability depending on the models used (Phillips and Dudı´k 2008). Moreover, ModEco also implements sample selection bias that allows users to provide the biased background to improve model performance (Phillips et al. 2009). Thresholds need to be applied in order to generate the binary output (i.e. presence and absence). Two methods are implemented in ModEco with respect to threshold selection (Fig. 3): one method is based on the empirical accumulative probability (e.g. 95%) and the other is derived from a statistically proven theory (Elkan and Noto 2008). Both methods need a validation dataset, which normally is the subset of the training data (e.g. 25%). The empirical accumulative probability method seeks to find the threshold that corresponds to a certain percentage of accumulative probability (e.g. 95%), while

Several limitations exist for this type of modeling. To generate the ENM reported here, the centroids of munic- ipalities, which have experienced a VACV outbreak, not the actual farm, were used. Significant environmental het- erogeneity across the municipality would reduce the pre- cision of the final model. Further, the outbreaks used to make the model are a result of a literature review, which do not represent reports from active surveillance of dis- ease in humans or cattle; therefore, there is an inherent bias that could over-represent the geographic areas that routinely report and test for VACV. Several measures have been taken to minimize the potential effects of these biases, including the use of subsets, limiting geographic extents, and the use of a 10% threshold to make conserv- ative estimates. Improvements in either the specificity of coordinates used and in data collection methods would likely improve model prediction. Implementing a surveil- lance program for VACV would improve the precision and number of cases and outbreaks. This would, in turn, improve model accuracy and predictive capability. Addi- tionally, overcoming barriers for reporting cases (i.e., fear of closing farms) would aid in surveillance efforts. High- quality occurrence data would also allow the use of rel- evant non-climatic factors such as land use, trade data, milker travel records, and other sources of environmental data (i.e., satellite imagery) at higher spatial and temporal resolutions to refine the models and broaden our under- standing of the ecology of this pathogen.

Wildlife conservation is essential, especially for countries like Kenya which rely on tourism as a major earner of foreign exchange. Conservation of species with minimal ecological information such as Grevy’s zebra, though a chal- lenge, is critical to enable the future survival of such species. Grevy’s and Plains zebra have been classified as endangered and near-threatened by Inter- national Union for Conservation of Nature and Natural Resources (IUCN) respectively, with Grevy’s zebra found mostly in Northern Kenya and Ethi- opia. This has been due to habitat degradation from livestock grazing, local hunting and development of resorts. Six prediction variables i.e. rainfall, tem- perature, land use, population, NDVI and cattle occurrence were used in Maxent algorithm to produce a habitat prediction map for both species. Both prediction maps had an AUC > 0.75, which is adequate for conservation plan- ning. Niche similarity based on Warren’s I index (I = 0.78) indicates that both zebra species are identical based on their occupied niche environments, sug- gesting that similar conversation strategies can be adopted for both species.

series of fourteen Maxent models. Fifty sites for mos- quito collection were selected using a blocked stratified random sampling design and mosquitoes in these sites were then collected using human landing collections. Fifty-two environmental data layers representing land cover, Normalized Difference Vegetation Index (NDVI), bioclimatic and topographic variables were derived from three sources (Landsat 5 TM, MOD13Q1, WorldClim). Using a two-step variable selection process, 11 of these variables were identified for Maxent modeling. Maxent models were developed for seven mosquito species and three time frames: the rainy seasons of 2009 and 2010 combined; November 2009; and November 2010. The importance of environmental variables influencing mosquito distributions was evaluated using percent con- tributions and permutation importance. Model perform- ance was assessed using the area under the receiver operating characteristic curve (AUROC). AUROC values range from 0 to 1; an AUROC value of 0.5 indicates that the model performs no better than random, an AUROC value of 1 would indicate perfect accuracy, and an AUROC value > 0.8 indicates robust performance of the model [37]. Maxent models are generated using presence-only rather than abundance data; however, be- cause we had abundance data available, we thought it important to test the correlation of Maxent model outputs with both species abundance and CHIKV abundance. Each of these steps is described in greater depth below.

Traditional epidemiological studies of disease in animal populations often focus on directly transmitted pathogens. One reason pathogens with complex lifecycles are understudied could be due to challenges associated with detection in vectors and the environment. Ecological niche modeling (ENM) is a methodological approach that overcomes some of the detection challenges often seen with vector or environmentally dependent pathogens. We test this approach using a unique dataset of two pathogens in wild felids across North America: Toxoplasma gondii and Bartonella spp. in bobcats (Lynx rufus) and puma (Puma concolor). We found three main patterns. First, T. gon- dii showed a broader use of environmental conditions than did Bartonella spp. Also, ecological niche models, and Normalized Difference Vegetation Index satellite imagery, were useful even when applied to wide-ranging hosts. Finally, ENM results from one region could be applied to other regions, thus transferring information across different landscapes. With this research, we detail the uncertainty of epidemiological risk models across novel environments, thereby advancing tools available for epidemiological deci- sion-making. We propose that ENM could be a valuable tool for enabling understanding of transmission risk, contributing to more focused prevention and control options for infectious diseases.

With the recent and rapid spread of ecological niche modeling (ENM) and geographic information systems (GIS), the need for a detailed dataset of environmental characterization has increased substantially. The creation of the WorldClim dataset (Hijmans et al., 2005, available at http://www.worldclim.org) is a considerable advance in terms of global environmental characterization as it provides high resolution (i.e. nearly 1 km) climatic surfaces derived from historical records from a number of weather stations across the globe. With this dataset, several analyses by means of GIS can be performed. WorldClim provides high resolution monthly maximum (tmax), minimum (tmin), and mean temperatures (tmean), and monthly precipitation (prec); and from those, a set of 19 bioclimatic variables can be derived (Busby, 1991).

Abstract: Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to study the geographically suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, and then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows were observed related to the months of the year, which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence—January to March and September to December—which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

The ecological niche modeling may have several purposes, including analyzing the effect of global climate change on biodiversity; guiding selection of priority areas for conservation; making prediction of ideal areas for planting; guiding surveys to detect new species (rare or endangered); identifying new patterns of vegetation distribution; guiding surveys to detect the different populations of a species; detecting areas at risk for the invasion of exotic species and study possible routes for the spread of pests and diseases (Muñoz et al., 2009) [24].

Abstract: Leishmaniasis is the third most common vector-borne disease and a very important protozoan infection. Cutaneous leishmaniasis is one of the most common types of leishmaniasis infectious diseases with up to 1.2 million occurrences of new cases each year worldwide. A dynamic transmission multivariate time series model was applied to the data to account for overdispersion and evaluate the effects of three environmental layers as well as seasonality in the data. Furthermore, ecological niche modeling was used to study the geographically suitable conditions for cutaneous leishmaniasis using temperature, precipitation and altitude as environmental layers, together with the leishmaniasis presence data. A retrospective analysis of the cutaneous leishmaniasis spatial data in Afghanistan between 2003 and 2009 indicates a steady increase from 2003 to 2007, a small decrease in 2008, then another increase in 2009. An upward trend and regularly repeating patterns of highs and lows were observed related to the months of the year which suggests seasonality effect in the data. Two peaks were observed in the disease occurrence—January to March and September to December—which coincide with the cold period. Ecological niche modelling indicates that precipitation has the greatest contribution to the potential distribution of leishmaniasis.

Ecological modelling of habitat suitability for RVF occurrence was implemented using the MaxEnt software version 3.3.3k [42]. There has been no systematic surveillance of RVF in Tan- zania and therefore, the spatial range of its occurrence was not explicitly known. Prior to con- ducting our study, we could not differentiate whether more RVF cases were confirmed in the northern Tanzania because those locations were suitable for disease occurrence or rather because they received the largest surveillance efforts. Our presence dataset was therefore con- sidered small and biased because of the fact that most of past surveillance efforts have been conducted in the northern Tanzania. We assumed that the un-sampled locations of the country could be suitable for RVF occurrence. For this reason, the MaxEnt default setting seemed more appropriate because it assumes that the species/disease being modelled is equally likely to be anywhere in the geographical space of the study area [70]. In addition, the regularization multi- plier was set to 1 to limit over-fitting of the model and prevent prediction from being inade- quately large [48]. Regularization multiplier is a parameter that leads to smoothening of the regression line to minimizing the error function and thus prevents over-fitting of the model. It does so by penalizing the values of the features that tries to closely match the noisy data points resulting to balanced optimal solution to avoid making the model complex. The model con- taining the optimal combination of predictor variables was run with ten replicates and 500 iter- ations at a convergence threshold of 0.00001, with cross validation replicate type. The output was set to logistic format, so that the predictions of habitat suitability would assume probability scores between 0 and 1 [42].

determined by van Apeldoorn et al. (2006) as those gener- ally used by badger groups in a Dutch population. This may imply that the spatial resolution of this study might not allow for determination of sett-site characteristics alone, but more generally for characteristics of the landscape proper- ties of badger home range. Generally, SDM at large scales allows for characterising the target species’ Eltonian niche, which is mainly determined by scenopoetic variables, and relevant for understanding the large-scale ecological and geo- graphic species requirements (Elton 1927, Soberón 2007), such was the aim of this study. On the contrary, finer scale modelling, together with mechanistic modelling approaches, allow for the characterisation of the Grinnellian, which is determined by biotic and trophic interactions (Grinnell 1917, Soberón 2007, Peterson et al. 2011). In order to gain further insight into the Grinnellian niche of badgers in the Netherlands, future studies should be performed using finer spatial resolution and detailed knowledge of badgers’ home ranges. Moreover, these studies should use badger locations or tracking data, rather than sett locations.

Nasal colonization with pathogenic bacteria continues to present challenges for patients undergoing surgical procedures, and for the physicians that treat them. Even as molecular medicine produces ever faster and improved data sets for cli- nicians, it would benefit all medical personnel attempting to decolonize the nose to better understand the historical nasal decolonization data with specific refer- ence to the ecological niche for these bacteria, as it has been recorded for more than a century. Much of the historical data points to the largest ecological niche for nasal Staphylococcus aureus as the vibrissae of the vestibulum nasi. A careful study shows that any topical antimicrobial preparation needs to successfully penetrate the deepest recesses of these specialized nasal hair follicles, if decolo- nization is to be adequately accomplished. This review highlights the most rele- vant data of the last 140 years concerning the staphylococcal ecological niche of the vibrissae. Also to be discussed will be a historical review of topical Mupiro- cin. Almost thirty years after its FDA approval, Mupirocin is still the most widely used topical antibiotic for decolonization therapy around the word. Cor- respondingly, new experimental in vitro data will be presented showing the dif- fering efficacy of Mupirocin against multiple strains of HA-MRSA and CA- MRSA, based solely on the commercial topical formulation (non Mupirocin in- gredients) that acts synergistically with the Mupirocin. Finally, the review will discuss why an understanding of these historical data is a vital component to integrate into any new or augmented nasal decolonization therapy.