Wildlife Ecology: Animal Population Sampling & Estimation: Brief Introduction

Wildlife Ecology: Animal Population Sampling & Estimation: Brief Introduction

Mohammed Ashraf

You can download the portable document format of this article by clicking PDF here.

Ecology is not the kind of science that takes people by storm hence I am not expecting that it is just what the doctor ordered. But we at Species Ecology are pretty ‘gung ho’ about the motion and rolled up our sleeves and buckled down to do our part to ensure science bound ecological sustainability find its niche in the face of anthropogenic development across the chessboard. I am not going to beat around the bush hence one of the main purposes of reaching out to people neatly rooted into the fact that collaborative and collective actions are fundamental to reinforce the conservation pillars in which wildlife science and ecology are basic ingredients. Therefore, I am at the crossroad reaching out potential academic scholars so that collectively we could go back to the drawing board and crank out rudiments of common language (mathematics) to preserve mosaic of heterogeneous pristine ecological units from Baluchistan in Pakistan to Yosemite in California and anything in between. I like to keep the ball rolling and I am twisting arms to get scholars on board depending on what variety of fresh food (ecology) and ingredients (mathematical tools) they can bring on the table.

Lot of ecological inquires can be modeled into finding priority action measures and to predict scenarios hence for example looking into fish population (denoted with P) which can be modeled into quadratic equation to predict future population size. Here I have modeled the fish population P below and solved the equation to determine the time (in days) when fish population will reach 500. This is just an example of some of the works I am pretty ‘gung ho’ about.

\left( 3t + 10 \sqrt{t} + 140 \right) = P

\left( 3(\sqrt{t})^{2} + 10 \sqrt{t} + 140 \right) = 500

\left( 3(\sqrt{t})^{2} + 10 \sqrt{t} - 360 \right) = 0

\left( ax^{2} + bx + (-360) \right) = 0

t = \left( \frac{-10 \pm \sqrt{(10)^{2} - 4 \cdot{3} \cdot{-360}}}{2 \cdot {3}} \right)

t = \left( \frac{-10 \pm \sqrt{(10)^{2} - 4 \cdot{3} \cdot{-360}}}{2 \cdot {3}} \right)

t = \left( \frac{ -10 \pm \sqrt{100 + 4320}} {6} \right)

t = \left( \frac{-10 \pm \sqrt{4420}}{6} \right)

t = \left( \frac{-10 \pm 66.48}{6} \right)

t = \left( \frac{56.48}{6} \right)

t = 9.41

\sqrt{t} = 9.41

(\sqrt{t})^{2} = (9.41)^{2}

t = 88.5

Fish population Model

Therefore for fish population to reach 500 it would require 88.5 days or roughly 12 weeks. Refer to the 3t + 10\sqrt{t} + 140 = P population model curve.

Its critically important to develop an algorithm so that we can generalize quadratic model and in this example I have used Python programming language to model the equation (3t + 10 \sqrt{t} + 140) = P into square-root function for the purpose of fish population prediction.

Once critically endangered Florida Panther: Subspecies of Mountain Lion recovered from population decline : Thanks to dedicated science bound conservation measure…

The other example I would like to draw attention to is sampling size and the determination of sampling size based on simple (or stratified) random sampling. Animal population estimation is function of two critical parameters. 1. Occupancy 2. Detectability. Here the probability of animal occupancy is one of the statistical factors that need to be taken into account before carrying out animal population survey. In other words, statistically valid survey design is at paramount importance. Generally speaking, at one given time, our chance to detect any animal depends on whether our sampling units are true representation of the population size. For example, If I ought to find out the Florida panther (subspecies of mountain lion) population in any given area of 100 sq km, my primary objective is design a survey unit based on proportional and true representation of all the units. It simply means, if we conduct animal detection survey of roughly 2 sq km that I can cover in a day on foot, then I need to ensure that each 2 sq km I choose is a true representation or have the equal probability of selection among my fifty 2-sq-km panther survey unit (50 times 2 equates our total 100 sq km). Surely 100 sq km is a relatively big area for me to survey on my own but I still need to conduct the survey hence I could survey 40 sq km out of my total 100 sq km potential survey area to estimate the Florida panther population. Since my survey units are all 2 sq km each, hence 40 sq km translates to total 20 blocks which I would need to randomly select out of total 50 blocks or 100 sq km. Here I have used R programming language to write up a function that will allow me to randomly select 20 blocks out of 100 or any numbers of blocks depending on how many blocks you wish to include into your survey as random sample. I have provided below the block matrix of 100 in which twenty 2-sq-km block are randomly selected. Blocks are highlighted for the purpose of clarity. Also note, this is not an algebraic matrix that you may often utilize to solve problems in linear algebra. This is just a block sample that some folks may simply present in a grid block as oppose to block matrix.

Sampling blocks under conceptually unified statistically valid random sampling procedure undertaken through R programming language

These 20 blocks are true representation of my sampling survey area and if survey is carried out in these blocks, even if I can detect only few panther from my survey unit, the sampling size would still be true representation of the population size hence it would allow me to estimate the detection probability of panther population from the entire 100 sq.km. ecological unit. As an example, if I manage to detect only 3 panther out of my 40 sq km survey unit and my detection probability stands out 0.1, it then translates to undetected panther population size of 30 which in turn give me the total population size of 33 in that particular Everglade mangrove habitat.

This is just a short article providing some very brief understanding with regards to ecological study focusing animal population survey design and estimation techniques. The article deduced hard core mathematical rigor and modeling techniques to produce succinct easy-to-understand ecological piece without compromising the statistical rigor. The primary objective of this short essay is to publicize these rather mathematically challenging models in simplistic coherent format so that average people from non scientific background yet avid conservationist can able to digest the rudiment of population ecology and its conservation implications.

This draft is prepared in \LaTeX – the brainchild of Donald Knuth, developed by American Mathematical Society (AMS) and created by George Gratzar from University of Manitoba Department of Mathematics. I have also utilized both Python and R Programming Language to develop quadratic population model and for designing random sampling matrix. No commercial software under capitalistic market share is used in preparation of this draft. UNIX variant GNU-Debian Linux is used throughout as core to run all software packages.


Wetland ecosystems, its benefits and science based conservation management practice

World Wetland Day: Wetland ecosystems, its benefits and 
science based conservation management practice 

Mohammed Ashraf

Waterfowl in wetland

February 02 is World Wetland Day. It may or may not mean much to people who are either too busy focusing on ‘earning money’ hence to use money as their standard yardstick of development when it comes to their personal and career growth or simply not interested on this topic. For many years, wetlands are considered as wastelands and still in many developing nations, wetlands are rapidly transforming into agricultural paddy field or cash farm of shrimp aquaculture in the expense of large scale degradation or complete decimation of this biologically most productive ecosystems on earth. So what is wetlands, how do we distinguish wetlands from other water bodies, what are the benefits of conserving wetlands, what are their international conservation status or designation, who is responsible for maintaining the global database of wetlands, how to access these database, what is the biological and or ecological potential of wetlands comparing to other ecosystems like rain forest or shrub land and what species serve as an ecological indicator and how do we go about measuring these species in order to ensure ecological integrity, functions and the processes of the wetlands are maintained so that we human can continue to receive the vested unconditional ecosystem services e.g. fresh water supply either directly or from ground water, nutrients in the form of fish and other aquatic fauna, shoreline stability, retention of silts hence avoiding coastal erosion and so forth.

In this short essay, I am going to provide some brief background information about wetlands of different types, the importance of wetland conservation to human, and the species that wetland ecologist or limnologist may consider measuring and monitoring as part of implementing the wise use and conservation management of wetland ecosystems.

Let me start by finding out what is not a wetland? A fraction of land that is covered with rain water hence formed a small rainwater pool, is not a wetland. It is not a wetland because the water is not a permanent one and the soil may not be the one that is adapted to water based plant species to grow and thrive on it. Water base species of plants means a selective group of botanically important species that can only grow in soil that are either permanently or seasonally wet but wet, nevertheless, throughout the year. These kind of plants are also know as Hydrophytes or aquatic plants and the soil they grow on is classed as hydric soil. Now that we have some crude idea what is not a wetland, lets just move on finding out what is actually a wetland. Wetland is an ecosystem comprise with hydric soil and hydrophytes. This is possibly the simplest definition to appreciate. Wetlands sometimes are also known as ecotone which refers to a transition inter phase between dry soil and wet soil. For example, mangrove ecosystem is essentially an unique ecotone due to the fact that the wetland within the mangroves is in the transition zone between dry or semi-dry land surface that are regularly and periodically inundated by tidal waves. Dry or semi dry wetland based ecosystems are also known as terrestrial ecosystem although the diversity and the characteristic of floral species may set it apart from other terrestrial ecosystems and the botanical species that would inhabit in this particular kind of wet-terrestrial ecosystem would be predominantly hydrophytes. Strictly speaking, there are four major kinds of wetlands-marsh lands, swamp forest, however not all swamp forests are mangroves, but all mangroves are swamp forest, bog lands and fen lands.

Although, bogs and fens are often collectively known as mires. Below is the list of different types of wetlands that are under various degree of anthropogenic threats.

1.Lakes including oxbow lakes (both man made and natural), 2. Rivers 3. Swamps, 4. Marshes, 5. Peat lands, 6. Bogs 7. Fends 8. tidal flats or also known as mud flats, 9. Estuaries, 10. Oases, 11. Deltas, 12. Wet grasslands, 13. Near shore marine areas, 14. Freshwater Corridors or River Corridors that are often found meandering through path of forest and finally 15. Man made sites such as fish ponds, rice paddies, reservoirs and salt pans.

Hence, it is no surprise, that wetlands are one of the most biologically diverse ecosystems on earth. Despite its significant ecological and conservation importance across the globe, wetlands are in peril both in developed and developing nations. This sorry state of affair largely stem from lack of ecological education focusing wetlands of various types as listed above and unscrupulous and eco-ignorant development policy implementation that either overlook integrated ecological based development approach or simply failed to adhere the ‘UN declarations on Sustainable Development’} also known as Rio+ declaration. Generally speaking, ecosystem benefits of wetlands are punishingly downplayed and the integration of both informal and formal ecological education on wetlands and its benefits to human is virtually absent in majority of countries. Here the paradox is wetlands have been received UN ratification in the from of United Nations’ Educational, Social, and Cultural Organization (UNESCO) declared World Heritage Sites, The Convention on Wetlands of International Importance informally known as Ramsar Convention and UN Man and Biosphere Reserve Designation. Despite all these international recognition of the values of wetlands, human seem to remain notoriously indifferent to acknowledge the significance of wetlands. The benefits human receive from healthy wetland ecosystems are enormous. Firstly, wetlands are the heart beat of human survival regime considering our life depends on potable water supply and wetlands’ purify, retain and replenish water table. Without wetlands, there would be no freshwater supply for human and even in 21st century, human has not found a economically feasible way to technologically advance the desalination of marine water for their consumption. Secondly, tropical countries are often most prone to cyclones, tsunami and tidal surges (Cyclone is also known as Hurricane in North America and Typhoon in North East Asia) and considering large number of people both in developing and developed nations are regionally migrating to coastal belts, they are putting themselves in high risk of cyclone induced perturbation. Healthy coastal belts depends on wetlands as it retain soils hence invigorate the coastal lands through soil accretions, silt deposition. Maintaining healthy ecotone for example mangrove ecosystem can shelter millions of people from coastal tidal surges- a re-occurring natural calamity in tropical countries like Bangladesh and India. Thirdly, wetlands help reduce flooding that cost life in tropical developing nations. Wetlands posses an immense biodiversity value and healthy biological diversity is an essential pre-requisite for healthy human welfare and their socio-cultural value-wisdom. Human living in or around large wetlands in tropical belt often are artisan fisher folk communities and their traditional livelihood is intricately entwined with wetlands in terms of harnessing the natural resources from the ecotones and its associated ecosystems. These values have significant anthropological, cultural, religious, economical, ethno-botanical, socio-political and research significance which human will loose if efforts are not made to conserve wetlands across the tropical eco-regions. In other words, the economic justification or the monetary significance of converting the wetlands to urban development or large scale irrigation projects carries very minimal values in the long run than to maintaining a ‘self sustaining’ wetlands and its ecological functions viz-a-viz ecosystem services that human continue to receive from the wetlands and its associated biodiversity.

So how we go about making sure we are doing the right thing to conserve and manage wetlands. In other words how do we ensure that our current management practice is as such that we can confidently say that our wetlands are healthy, productive ecological unit. One critically important management practice deeply rooted in to the science of ecology-lending mathematically sophisticated tools and integrating them with further powerful tool of geographical information science. Here we will briefly provide the fundamentals of mathematical estimator that if appropriately employed by wetland ecologist for collecting data can serve as baseline index to measure and monitor the health of wetland ecosystems. Wetlands are dominated by hydrophytes. Although there are relatively good number of vascular aquatic plants inhabiting the terrestrial wetlands. However, these woody species need to compete very hard with each other resulting in the local dominance of single or few species through the process of competition. Therefore these species are not necessarily a good indicator of understanding or measuring the health of the wetlands considering their low diversity and richness. Therefore, wetland ecologists often relies on rooted submerged aquatic plants known as macrophytes. Some of these macrophytes are halophytes- a group of submerge aquatic species that grow in high salinity in the water}. Macrophytes in general serve as an ecologically valid indicator species to statistically appraise the health of the wetland ecosystem. In other words, high diversity (number of different species of macrophytes, their proportional abundance, and evenness of the different species) of macrophytes means grater production of algal species, high biomass and lower loss of phosphorus, all signs of a healthy wetlands. The implication from management perspective is, estimating and monitoring the aquatic macrophytes diversity can at least provide us the lower bound of the index measure to detect the overall health of the wetlands. Ecologically speaking, management practice that maintain macrophyte diversity and monitor the diversity index both in spatial and temporal scales, can potentially enhance the ecological functioning and associated services of wetland ecosystems. So how we go about establishing a diversity index focusing macrophyte species. Here I have introduced a diversity estimator and mathematically illustrated it through algebraic simplifications.

Diversity Index
Diversity index is a mathematically valid numeric representation of a value that not only reflects how many different species are there but also simultaneously take into account the evenness that is how equally or (unequally) the types of different species are distributed across the data sample. Here I start with general estimator and algebraically work my way down to come up with suitable diversity estimator that we can employ in our macrophyte diversity estimation in wetlands. Please note this is a very brief mathematical treatment of figuring out the diversity index hence the estimator. For full treatment, please refer to standard ecological literature that are at your disposal.

q_{D}=\frac{1}{\sqrt[q-1]{\sum_{i=1}^{R} p_{i} p_{i}^{q-1}}}

Here, D is our Diversity Index, q is diversity order, in other words, the value of q can help us to model the estimator in terms of understanding how sensitive our diversity index is that is rare versus abundance species across the species’ proportional abundance in our sample data, p_{i} is our proportional abundance of ith type of species and R is our total number of species. Notice R is actually Species Richness that simply reflects how many total number of different types of species we have found. We now simplify the above equation below:

q_{\textbf{D}}=\left(\sqrt[q-1]{\sum_{i=1}^{R} p_{i} p_{i}^{q-1}}\right)^{-1}

q_{\textbf{D}}=\left(\left[\sum_{i=1}^{R} p_{i} p_{i}^{q-1}\right]^{\frac{1}{q-1}}\right)^{-1}

We continue further algebraic simplification

q_{\textbf{D}}=\left(\left[\sum_{i=1}^{R} p_{i} p_{i}^{q-1}\right]^{\frac{1}{-(1-q)}}\right)^{-1}

q_{\textbf{D}}=\left(\sum_{i=1}^{R} p_{i} p_{i}^{q-1}\right)^{\frac{1}{1-q}}

If we take radical of (1-q) in both side of the equation, things will start to make more sense:

\sqrt[1-q]{q_{D}}=\left(\sqrt[1-q]{\sum_{i=1}^{R} p_{i} p_{i}^{q-1}}\right)^{\frac{1}{1-q}}

\sqrt[1-q]{q_{D}}=\sum_{i=1}^{R} p_{i} p_{i}^{q-1}

Now this is simply a raw version of Shannon-Weaver Index, also commonly known as Shannon entropy. Lets trim the equation to make more sense out of it.

\left[q_{\textbf{D}}\right]^{\frac{1}{-(q-1)}}=\sum_{i=1}^{R} p_{i} p_{i}^{q-1}

\left[q_{D}\right]^{\frac{1}{-(q-1)}}= \sum_{i=1}^{R} p_{i} p_{i}^{q-1}

\left(\sqrt[q-1]{q_{\textbf{D}}}\right)^{-1} = \left(\sum_{i=1}^{R} p_{i} p_{i}^{q-1}\right)

We are going to take natural logarithm ln in both side of our equation in order to bring down our exponents.

ln \left(\sqrt[q-1]{q_{\textbf{D}}}\right)^{-1} = ln \left(\sum_{i=1}^{R} p_{i} p_{i}^{q-1}\right)

-ln \sqrt[q-1]{{q_{\textbf{D}}}} = \sum_{i=1}^{R} p_{i} (q-1) ln p_{i}

We multiply both side of our equation with negative (-) resulting simply a Shannon entropy.

ln \sqrt[q-1]{q_{\textbf{D}}}= -\sum_{i=1}^{R} (q-1) p_{i} ln p_{i}

The left hand side of our equation is simply a value of Shannon Diversity Index and can be written as H, however, the value of q as mentioned earlier can take up any magnitude although, considering to the fact that p_{i} is our proportional number of species and we are interested to find out the weighted distribution and the richness (R) in order to enumerate the value of species diversity index that takes both the distribution and evenness into account, therefore, a general rule of thumb in this case would be to consider the value of q=1 that would give us a weighted geometric mean across our proportional number of individual type of species in our sample data set. Hence plugging in the value of q=1 in our equation results a Shannon Diversity Index Estimator:

\textbf{H}= - \sum_{i=1}^{R} p_{i} ln p_{i}

Please note the above equation. Shannon index is grounded to the weighted geometric mean of the proportional abundances of the types of species viz-a-viz the species richness R, and it equals the logarithm of true diversity as calculated with q = 1. We can carry out further simplification of our H as shown below:

\textbf{H} = -\sum_{i=1}^{R} ln p_{i}^{p_{i}}

This can also be written as

\textbf{H} = - \left(ln p_{1}^{p_{1}} + ln p_{2}^{p_{2}} + ln p_{3}^{p_{3}} + ln p_{4}^{p_{4}} + \dots + ln p_{R}^{p_{R}} \right)

Which equals

\textbf{H} = - ln p_{1}^{p_{1}} ln p_{2}^{p_{2}} ln p_{3}^{p_{3}} ln p_{4}^{p_{4}} \dots ln p_{R}^{p_{R}}

\textbf{H} = ln \left(\frac{1}{p_{1}^{p_{1}} p_{2}^{p_{2}} p_{3}^{p_{3}} p_{4}^{p_{4}} \dots p_{r}^{p_{R}}} \right)

The final algebraic simplification of the above equation can be written in a succinct form as shown below:

\textbf{H} = ln \left(\frac{1}{\prod_{i=1}^{R} p_{i}^{p_{i}}} \right)

Algebraic simplifications of our original general estimator lead us to workable succinct Shannon-Weaver Diversity Index which if utilized in a conceptually unified and statistically valid sampling framework can produce ecologically correct diversity index of macrophyte species in wetland ecosystems. Therefore conservation management plan, particularly in the developing nations where tropical wetlands are facing grim future, must focus on integrating scientifically valid statistical sampling design- lending necessary mathematical estimator to better appraise the species diversity index. With adequate baseline data on macrophytes both in spatial and temporal scales, conservation managers will be armed with accurate limnological knowledge to detect any changes in the overall functionality of the wetland health hence to adopt sound management prescription that can help maintain macrophyte diversity viz-a-viz enhancing the functioning and associated ecological services of the wetland ecosystems both in tropical and semi tropical belt.

The Power of Linux-Resources for Wildlife Ecologists

The Power of Linux and its Utilization: Free and Open Source Software (FOSS) Resources for Wildlife Ecologists & Conservation Biologists

Mohammed Ashraf

I want to tell you a story. No, not the story of how, in 1991, a guy from Finland called Linus Torvalds wrote the first version of the Linux kernel. You can read that story in lots of Linux books. Nor are we going to tell you the story of how, some years earlier, Richard Stallman began the GNU (GNU is not Unix) Project to create a free Unix-like operating system. That’s an important story too, but most other Linux books have that one, as well. No, we want to tell you the story of how you can take back control of your computer. When we began working with computers as a school student in the mid 1980s, there was a revolution going on. The invention of the microprocessor had made it possible for ordinary people like you and us to actually own a computer. It’s hard for many people today to imagine what the world was like when only big business (American Telegraph and Telephone-AT&T) and big government (Pentagon or FBI) ran all the computers. Let’s just say you couldn’t get much done. Today, the world is very different. Computers are everywhere, from iPhone to giant data centers to everything in between. In addition to ubiquitous computers, we also have a ubiquitous network connecting them together. This has created a wondrous new age of personal empowerment and creative freedom, but over the last couple of decades something else has been happening. A single giant corporation (guess who?) has been imposing its control over most of the world’s computers and deciding what you can and cannot do with them. Fortunately, people from all over the world are doing something about it. They are fighting to maintain control of their computers by writing their own software. They are building Linux. Many people speak of “freedom” with regard to Linux, but I don’t think most people know what this freedom really means. Freedom is the power to decide what your computer does, and the only way to have this freedom is to know what your computer is doing. Freedom is a computer that is without secrets, one where everything can be known if you care enough to find out.

Why Use the Command Line?

Have you ever noticed in the movies when the “super hacker”—you know, the guy who can break into the ultra-secure military computer in under 30 seconds—sits down at the computer, he never touches a mouse? It’s because movie makers realize that we, as human beings, instinctively know the only way to really get anything done on a computer is by typing on a keyboard. Most computer users today are familiar with only the graphical user interface (GUI) and have been taught by vendors and pundits that the command line interface (CLI) is a terrifying thing of the past. This is unfortunate, because a good command line interface is a marvelously expressive way of communicating with a computer in much the same way the written word is for human beings. It’s been said that “graphical user interfaces make easy tasks easy, while command line interfaces make difficult tasks possible,” and this is still very true today. Since Linux is modeled after the Unix family of operating systems, it shares the same rich heritage of command line tools as Unix. Unix came into prominence during the early 1980s (although it was first developed a decade earlier), before the widespread adoption of the graphical user interface and, as a result, developed an extensive command line interface instead.

Linux on Conservation Science

If your work pivots around ecological science encompassing the broader rubric of conservation biology, you are bound to carry out research that is deeply grounded to mathematical modeling. The 21st century modern ecologists cannot escape the hard core mathematical programming in the direction of estimating the ecological parameters; be it demographic or niche model of endangered vertebrates or phylogenetic analysis of species that is literally extinct in the wild. Modern wildlife biologists require tools the broadly falls under the mathematical underpinnings of numerical modeling: the tasks that require strong command of computer programming language. For example, if you wish to conduct a ecological research to estimate the distribution parameter of tigers in the Sundarbans mangrove ecosystem in Bangladesh, you would need to require mandatory skills in environmental/ecological statistics, first and second derivative of calculus, matrix algebra, spatial, algebraic and statistical modeling and so forth. None of these areas are possible to explore or to understand without the power and freedom of computers that would allow you to perform advance mathematical programming tasks or to help you generate visually engrossing highly sophisticated graphs. If you are from tropical developing nations where biodiversity is most rich but economical resources are most poor, you face a dwindling situation to strike the right balance to manage meager fund that are at your disposal against the backdrop of prioritizing the tasks that you can do as a serious wildlife ecologist without compromising the quality and the breadth of the rigorous hard core science of ecology, wildlife biology, and mathematics. Unless you are backed up with ‘gigantic corporate based conservation research for lets say, transnational corporations protecting their vested interests, your work will largely dominate by the key issue of how good you are at managing your scarce funding hence to publish your invaluable research work into ‘high impact’ journals (e.g. Journal of Wildlife Management or Conservation Biology). Evidently you would require software that have the utilities, tools and the power to present you with robust mathematical algorithms which at times you can extrapolate, overlay in spatial and temporal scale, and generate stochastic models and scenarios under conceptually unified rigorous statistical framework.

Linux has the power and the necessary tool to provide you with the mathematical packages and the programming power that would enable you to accomplish your research work without taxing your scarce research grant. Yes, Linux packages comes under GNU (a recursive acronym that means GNU is Not Unix)-GPL (General Public License) developed under Free and Open Source Software (FOSS) project that was founded by legendary computer scientist Richard Stallman back in 70s. Therefore you are not liable of ‘copyright infringe’ nor you are liable of getting harassed by ‘corporations as such Microsoft’ that takes up all your hard earn cash for their own vested profit. In Linux Operating System (OS), everything that comes with it is free and its all neatly packaged with most of the Linux distributions that are at your disposal. Below are the necessary GNU Linux Distribution based software packages that would help wildlife ecologist and conservation biologist alike to carry out solid hard core statistical, algebraic, spatial and temporal modeling with the power of geographic information system (GIS) based structured query language (SQL) without paying thousands of dollars under Windows based operating system. All these Linux Packages are free.

For ecological numeration of statistical modeling and spatial and temporal map production use R programming environment. For wildlife science, ecology, natural resource economics, and conservation biology that strongly integrate the tools, formula, principles and theorems of mathematical modeling including matrix algebra, calculus, trigonometry, analytic geometry, stochastic simulation, Monte Carlo simulation, Markov Chain models, Boolean algebra you can use both R and Python programming language. If you already use Java programing then Python would be very easy for you to pick up. For Species Distribution Model (SDM) that borrows tools from statistics and other branches of Mathematics: Python programming language, R programming and Octave programming environment is ideal along with QGIS for GIS based modeling and mapping in large ecological or hydrological landscape.

All these highly sophisticated mathematical software comes under GNU-GPL Linux free of cost and users are free to distribute, copy and manipulate the scripts indefinitely. I am providing top five Linux distributions (also known as Linux OS) that I would of benefit to serious wildlife ecologists and wildlife science students.

1. Debian Linux

2. Open Mandriva

3. Ubuntu Linux

4. PCLinuxOS

5. Linux Mint

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