Scene of a changing climate

For the last 500,000 years, the world climate has been in transition from warm to cold and vice versa. However, recent human-caused climate change has increased the rate of change in extreme and average climate conditions. Globally, people are facing higher than average temperatures as well as accelerated rates of drought and flooding. According to the Intergovernmental Panel on Climate Change (IPCC,) natural systems around the globe are being affected by regional climate change, mainly temperature increases. They IPCC found that 20-30% of plant and animal species are likely to be at increased risk of extinction if global average temperatures rise by more than 1.5-2.5°C. In recent years, scientific studies have provided valuable information that helps understand the effects of climate change on natural systems. Translating and simplifying the data through interactive maps and incorporating real-world examples will make these studies and their outcomes more meaningful and useful to policy makers and to the general public. I am using my GIS, remote sensing and my interest in understanding the effects of climate change on terrestrial ecosystems to make interactive web maps and infographic to show this effects for general public. The three stories in this portfolio depict the effect of climate change on natural resources. Chapter one is a narrative outlining the stories, my reportage and plans for publication. Chapter two: Missing Migration: The Elk of Ya Ha Tinda. Chapter three: Drier, Hotter, Faster: How Climate Change and Drought Affect Wildfire. Chapter four: Roaring Lion Fire: Climate Change Hits Home.https://scholarworks.umt.edu/grad_portfolios/1002/thumbnail.jp

Modeling the potential distribution of wildlife species in the tropics

The process of ecosystem destruction during the last century not only caused habitat fragmentation, but also loss of many species. Unfortunately, the traditional strategies for protecting these natural treasures were not successful enough to secure the survival of remaining biodiversity. In this scenario, a rapid assessment for predicting the distribution of the remaining species and habitats is essential. "While tropical rain forests are known as biological hotspots, only few studies have been done to determine the potential distribution of species. Distribution species modelling in tropical areas with high rate of deforestation and loosing connectivity is critically important for endangered wildlife species management program. Habitat modelling using remote sensing plays an important role in measuring and monitoring habitat characteristics in a large scale. This paper highlights and reviews the need of using geospatial modelling techniques to determine endangered species distribution in the tropics

Indoor environment assessment of special wards of educational hospitals for the detection of fungal contamination sources: A multi-center study (2019-2021)

Background and Purpose: The hospital environment was reported as a real habitat for different microorganisms, especially mold fungi. On the other hand, these opportunistic fungi were considered hospital-acquired mold infections in patients with weak immune status. Therefore, this multi-center study aimed to evaluate 23 hospitals in 18 provinces of Iran for fungal contamination sources.Materials and Methods: In total, 43 opened Petri plates and 213 surface samples were collected throughout different wards of 23 hospitals. All collected samples were inoculated into Sabouraud Dextrose Agar containing Chloramphenicol (SC), and the plates were then incubated at 27-30ºC for 7-14 days.Results: A total of 210 fungal colonies from equipment (162, 77.1%) and air (48,22.9%) were identified. The most predominant isolated genus was Aspergillus (47.5%),followed by Rhizopus (14.2%), Mucor (11.7%), and Cladosporium (9.2%). Aspergillus(39.5%), Cladosporium (16.6%), as well as Penicillium and Sterile hyphae (10.4% each), were the most isolates from the air samples. Moreover, intensive care units (38.5%) and operating rooms (21.9%) had the highest number of isolated fungal colonies. Out of 256 collected samples from equipment and air, 163 (63.7%) were positive for fungal growth.The rate of fungal contamination in instrument and air samples was 128/213 (60.1%) and 35/43 (81.2%), respectively. Among the isolated species of Aspergillus, A. flavus complex (38/96, 39.6%), A. niger complex (31/96, 32.3%), and A. fumigatus complex (15/96, 15.6%) were the commonest species.Conclusion: According to our findings, in addition to air, equipment and instrument should be considered among the significant sources of fungal contamination in the indoor environment of hospitals. Airborne fungi, Hospital, Indoor air, Equipment, Sources of fungal contamination in the indoor environment of hospitals

Habitat modeling and potential distribution of the Malayan sun bear (Helarctos Malayanus raffles) using geospatial information technolog

While tropical rain forests are known as biological hotspot, few studies have been conducted to determine the potential distribution of species. Distribution modeling in tropical areas with high rate of deforestation and loosing connectivity is critically important for species management programs. Identifying the ecological requirements of the species and delineating the distribution of species throughout the entire habitat are fundamental in nature conservation. Simulations of spatially – explicit habitat enables conservation planners to identify key areas to protect, detection of wildlife corridors and preserve landscapes. This study assessed the application of Species Distribution Modeling (SDM) to Malayan Sun Bear habitat using the Maximum Entropy (MaxEnt) and Ecological Niche Factor Analysis (ENFA) with special emphasis on remote sensing and geographic information system (GIS) data. In order to test the models, two different spatial scales of Krau Wildlife Reserve (KWR) and Peninsular Malaysia were selected to model suitable habitat of Malayan Sun Bear. Results showed that both modeling outputs were acceptable in two different scales even though, MaxEnt could discriminate marginal and high suitable habitats better when applied on larger scale. On the other hand, ENFA had better results when applying in smaller scale. These contrasts in suitability maps were admitted by Area Under the receiver operator characteristic Curve (AUC) plots as well. AUC values for models ranged from 0.87 in small scale of KWR and 0.97 for large scale of Peninsular Malaysia, suggesting strong and accurate predictable species-Ecogeographical matching. The environmental variables applied in methodology such as land cover, vegetation indexes and climatic variables had higher correlation with suitability map creation. Comparing the output of suitability maps of the models showed that in Peninsular Malaysia, MaxEnt separated high suitable area by covering 5% of the total 131598 km2 and 13% of the total area to the marginal habitat. On the other hand, ENFA suitable habitat was doubled to 10% and for marginal habitat it was covering 24% of Peninsular Malaysia. Results of the best model revealed that the protected areas covered only 21.9% of the total marginal and suitable habitat. Extending the boundaries of protected areas and establishing new areas has the highest priority for any conservation action plans. This study plays an important role in increasing the limited knowledge of habitat preferences of the Malayan Sun Bear

Modelling the potential distribution of the Malayan sun bear in Krau wildlife reserve, Malaysia

Information and data on the distribution of wildlife species become important when strategizing the management and conservation of the species. Species distribution models have increasingly been used as a predictive tool in wildlife conservation planning and management. This research assessed the distribution of the sun bear and identified the relevant distribution descriptive variables in the Krau Wildlife Reserve, Pahang, Malaysia using Ecological Niche Factor Analysis (ENFA). Marginality and specialization factors showed that the sun bear habitat is very specific compared to the entire study area and its niche breadth was slightly narrow. Among all the habitat suitability algorithms available in Biomapper software package, median with the extreme optimum algorithm was found to have the best predictive capabilities by means of a continuous Boyce index. Results showed that sun bear can rarely be found in high altitudes, prefers lowland Dipterocarp, and avoids Mountain Ericaceous and Mixed Hill Dipterocarp forests. It also prefers to live nearby rivers, tends to avoid villages and prefers regions with fine silty-clay and loamy textures soils

A geo-statistical approach to model Asiatic cheetah, onager, gazelle and wild sheep shared niche and distribution in Turan biosphere reserve-Iran

Presence data for four mammals in the Turan Biosphere Reserve in Iran including the Asiatic cheetah ('Acinonyx jubatus venaticus'), the Persian onager ('Equus hemionus onager'), the wild sheep ('Ovis vignei'), and the gazelle ('Gazelle Bennettii') were used to analyze and model their potential interaction, facilitation, habitat coverage and niche dimensions. A geostatistical approach using the spatial autocorrelation between the locality points, and their relationship with habitat resources and characteristics with application of remotely sensed maximum enhanced vegetation index (EVI) and surface temperature, elevation, aspect, vegetation cover and soil moisture was used to predict herbivores species niche. The potential suitable habitat of herbivores along with environmental variables was used to model the predator species (cheetah) niche. The model results were tested using fivefold cross validation by area under the curve (AUC) values on set of independent testing data and were compared to more commonly used models of generalized linear model (GLM) and MaxEnt. The results show that cheetah's potential suitable habitat has 61% overlap with wild sheep, 36% with onager, and 30% with gazelle. Onager habitat has 64% overlap with gazelle and 60% the wild sheep. Wild sheep on the hand, shares only 37% of its habitat with gazelle. The most prey and predator interaction exists between cheetahs and wild sheep, while onagers provides facilitation for gazelles and wild sheep by potentially providing extra water sources. Among the implemented modeling techniques, spatial GLM showed better performance over GLM and MaxEnt. We suggest that conservation effort should focus more on maintaining the population of wild sheep and onagers to support other species in the habitat

Predictive modeling and mapping of Malayan sun bear (Helarctos malayanus) distribution using maximum entropy

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population

Species distribution models to inform at-risk species status assessments in the southeastern US

The USFWS is working collaboratively with State Wildlife Agencies, Universities, Non-profits and others in the southeast to address the National Listing Workplan. The USFWS needs up-to-date information on current status and the likely impact of future changes to develop Species Status Assessments (SSAs), which help inform listing decisions. States, Universities and other partners are providing species expertise, location data, analytical support and logistical support (e.g. surveys). However, a significant knowledge gap remains in understanding potential species distributions, from which status surveys can be more strategically implemented. This project provides a bridge between species location information and the SSAs by developing empirically-driven, high resolution information on distributions of species, as well as their likely responses to landscape changes. Here we provide ensemble distribution model results for several species: Papaipema eryngii, Macbridea caroliniana, Scutellaria ocmulgee, Balduina atropurpurea and Rhynchospora crinipes. The raster files can be used in combination of other datasets in GIS for further species and landscape management

Remote Sensing Derived Fire Frequency, Soil Moisture and Ecosystem Productivity Explain Regional Movements in Emu over Australia

<div><p>Species distribution modeling has been widely used in studying habitat relationships and for conservation purposes. However, neglecting ecological knowledge about species, e.g. their seasonal movements, and ignoring the proper environmental factors that can explain key elements for species survival (shelter, food and water) increase model uncertainty. This study exemplifies how these ecological gaps in species distribution modeling can be addressed by modeling the distribution of the emu (<i>Dromaius novaehollandiae</i>) in Australia. Emus cover a large area during the austral winter. However, their habitat shrinks during the summer months. We show evidence of emu summer habitat shrinkage due to higher fire frequency, and low water and food availability in northern regions. Our findings indicate that emus prefer areas with higher vegetation productivity and low fire recurrence, while their distribution is linked to an optimal intermediate (~0.12 m³ m^-3) soil moisture range. We propose that the application of three geospatial data products derived from satellite remote sensing, namely fire frequency, ecosystem productivity, and soil water content, provides an effective representation of emu general habitat requirements, and substantially improves species distribution modeling and representation of the species’ ecological habitat niche across Australia.</p></div

Satellite remote sensing derived environmental variables used to explain emu summer habitat suitability, including soil moisture, gross primary production (GPP) and fire frequency used as respective habitat suitability metrics for water availability, food supply and shelter conditions.

<p>Satellite remote sensing derived environmental variables used to explain emu summer habitat suitability, including soil moisture, gross primary production (GPP) and fire frequency used as respective habitat suitability metrics for water availability, food supply and shelter conditions.</p