Why estimate population size

A biologist originally marked 40 butterflies in Wilson Park. Of those , 80 were found to have tags. Based on this information, what is the estimated population size of the butterflies in Wilson Park? He later does another capture exercise at the community garden near the high school. In this area, he captured and marked 40 butterflies. The traps in this location found butterflies where 50 of them had tags. What is the population size of the butterflies at the school?

The department of natural resources regularly collects data on population numbers in states. Discuss reasons why population numbers would be important and how this data could be used to manage wildlife populations in the state.

As with complete counts, distances between observers and between members of the drive crew are critical for success. A strip census can be used to estimate grouse population sizes. An observer walks a transect through a representative section of habitat and records the distances at which birds flush to either side. The population size, P , is estimated to be where A is the area of the habitat censussed, Z is the total number of grouse flushed, X is the total distance walked and Y is twice the average distance from the observer to the bird when flushed.

The fundamental assumptions of this method are 1 birds vary randomly in distances at which they flush, 2 birds are scattered randomly across the study area and 3 the average flushing distance is a good estimate of the "true" average. Which of these assumptions are likely to be met? What if some birds will not flush? A Wildlife Monograph has dealt extensively with these types of population size estimates Burnham et al. An index of population indicates relative size of a population and shows population trends up, down, stable but does not provide an actual estimate of the number of animals.

Examples of indirect counts include counting numbers of muskrat houses, counting scats fecal pellets of deer and rabbits, and counting numbers of nests or den sites in a given area. Sometimes counting the number of birds heard singing is considered an incomplete count and sometimes it is considered an indirect count.

Which makes more sense? In either case, the first thing to do after establishing the transects or plots is to remove all old pellets. Then, at a predetermined interval, count all new piles of fecal pellets.

This is an index of the number of deer or rabbits in the area: the more animals, the more pellets produced. What assumptions does this index make? If, for a given area, one knows the average number of muskrats living in each house, then the number of houses can be used to estimate the population size. It should be remembered, however, that indirect counts are only indices of population sizes unless other information is known, such as the average number of muskrats living in each house.

The approach was first used by Petersen to study European plaice in the Baltic Sea and later proposed by Lincoln to estimate numbers of ducks. Petersen's and Lincoln's method is often referred to as the Lincoln-Petersen Index, even though it is not an index but a method to estimate actual population sizes.

Should it not be the Petersen-Lincoln Estimate? Their method involves capturing a number of animals, marking them, releasing them back into the population, and then determining the ratio of marked to unmarked animals in the population.

The population P is estimated by the formula:. This is derived from the equation:. Some of the assumptions behind this method are: 1 mortality is the same for marked and unmarked animals; 2 marked individuals do not lose their marks; 3 marked individuals are caught at the same rate as unmarked individuals no trap-happy or trap-shy animals ; 4 the population has no significant recruitment, or ingress births or immigration ; 5 the population has no significant egress deaths or emigration ; 6 marked animals mixed randomly with unmarked animals; and 7 each trapping session captures a representative sample of various age and sex categories from within the population.

Think about these assumptions with respect to wildlife. Assumptions 4 and 5 taken together mean that a population is closed. Remember, the Lincoln-Petersen method provides and estimate of the true population size; it does not state the actual, or true, population size. Example of the Lincoln-Petersen Index Imagine that you set out live traps in a muskrat marsh. Since you actually captured 14 muskrats, you know that the population size is at least Several modifications construct stratified indices whereby data are collected separately for specific sub-groups of the population, such as age and sex categories or trap-happy and trap-shy animals.

Thus, researchers must uniquely mark each individual captured and record information about that individual, such as sex and age.

These modifications also insure an order of magnitude increase in the complexity of the mathematics and are available in computer software, such as Capture. The Jolly-Seber Method relaxes the assumption that a population is closed. That is, the population can be open and have ingress births and immigration and egress deaths and emigration. By keeping track of capture histories for individual over many capture sessions, ingress and egress can be estimated.

Jolly-Seber Estimates can be calculated by hand but the exercise is complicated. Several software packages provide Jolly-Sever Estimates. In its simplest form, the Robust Design uses an L-P Estimate for total population size during each of several, regularly scheduled trapping sessions and uses of the Jolly-Seber approach to estimate ingress and egress between trapping sessions. The MNA method avoids the use of estimators, using instead the minimum number of animals known to be alive during a sampling period as a biased estimator of the population size.

Hilborn et al. However, we hope that this may be applicable in other communities around Lake Victoria, or fishing communities based on other lakes in East Africa. Area based methods have historically been used in urban environments [ 14 , 15 ] or used large numbers of variables to define typologies [ 16 ]. Large amounts of the work around defining populations through remotely sensed data is in defining these typologies.

Small villages in many parts of rural Africa consist of buildings of similar type: single-storey, with little variation in building materials and construction. It is therefore surprising to find the variation in population density in FCs as we observed around the Ugandan shores of Lake Victoria. The extremes of variation may be due to the different types of FCs, some being villages that are on the lakeshore and so fishing is their main livelihood, while others may be temporary and used during certain parts of the year when the fish are in that location.

The communities may be more cramped due to space limitations on islands or peninsular areas. There could also be a difference in the population makeup, as fishing communities often consist primarily of working age men and contain fewer families, resulting in varying average building occupancy rates. These would both alter the population density of the FC. We also acknowledge the difference between the date of the survey and the date the corresponding satellite image was taken; this could increase errors in our predictions if there were significant changes in the FC population between those dates.

Following our analysis, we applied parameters from our complete dataset to data from FC villages in Uganda, identified in GEP, for which we had area, but not population data. The total area was 10,, m 2. The average density method M1 estimated an overall population of ,; method M2 estimated ; and the regression method estimated , Whilst it is impossible to verify the accuracy of these estimates, it is reassuring that in this setting they produced similar values and gives us hope that the simplest method is not significantly worse than a more complex approach even when estimating larger populations in a large number of villages.

The use of GEP as the source of images and the method to define area had pros and cons. It is easy to use and readily available. In a very short time, it is possible to learn how to mark regions and to extract the area. Training carried out with researchers from the three countries which border Lake Victoria gave us first-hand experience that GEP could be learned in less than one hour.

It was also very quick to map areas: the dataset for all villages along the Ugandan lakeshore of Lake Victoria took one person less than three to produce. The availability and age of imagery is more of an issue. In some areas, images are plentiful and are often taken many times per year. These areas are typically areas of human activity cities, areas of conflict, deforestation or where natural disasters have occurred.

But this is not the case in all parts of the world, particularly in more rural areas. Around Lake Victoria most images happened to be from , the preceding 12 months to the majority of the population surveys. There was often only one image available. Although images were available for the majority of the area, these were not always the very high sub-metre resolution images best suited to assessing structures, but did allow the populated area to be identified. In communities that are very stable with little change the image date is not an issue.

However, in fishing communities such as those in this research, change can be very rapid, in terms of both increase and decrease in size. If the community is temporary for instance the duration of a fishing season then the residents may move sites frequently. There is a similarity with displaced populations, which also can change rapidly. A further limitation is that we excluded structures which were located away from the main village. This was done to ease the process of defining the boundary, and hence obtain an estimate for the village area.

However, it does mean that if there are varying numbers of people living in structures away from a village, this method may not be appropriate.

The date and availability of images resulted in us having to exclude 19 villages from the analysis. In addition, the calculation of FC area from satellite images may have been inaccurate.

One advantage of a simple area method is that the village areas do not have to come from satellite images; it would be possible for fieldworkers to use a handheld GPS device to define the outline of the village and thus calculate the area, removing these inaccuracies. Our results match what is typical of other studies [ 7 ], that errors are larger for individual areas than for the population as a whole.

Using a simple area method to estimate populations of groups of villages is feasible and would be a rapid and low skills way to get populations in these settings. Care would be needed to use this method to estimate populations in individual villages.

Further work is needed to investigate if assigning typologies, or using more recent satellite images or calculating area from on the ground would improve the results. Some progress has recently been made by organisations on population datasets such as WorldPop Stevens FR.

These datasets are still far from complete and could not be used around Lake Victoria in our target villages. There is an overlap in the work of defining population densities for different urban typologies, and it is important that data from small surveys are able to feed into the large datasets and vice versa.

There is also a need for the larger datasets to be made available through simple-to-use software, and not rely so heavily on GIS skills. However it is filled, there is still a gap for a rapid, low skill method that can be applied in settings where GIS capabilities are very limited or population change was rapid.

Simplified methods are needed to determine the size of populations at high health risk in resource-limited settings. Satellite images may be able to help provide information in areas where access and resources to perform surveys is limited, or for which a rapid estimation is required. We have shown that it is straightforward to generate the required spatial data using widely available software such as GEP, without the need for more technical GIS skills.

We have shown that using an average density of 0. However, care is needed when using area based methods with migratory populations, where estimates for individual communities may be associated with large errors. Overall population estimations balance out, and with further validation in more stable communities it may prove to be more viable for individual locations. Similarly using a GPS device to obtain the area of the village and multiplying by regional population densities would give a simple method where visiting the location was an option.

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Trop Med Int Health. Int J Remote Sens. Li G, Weng Q. Photogramm Eng Remote Sens. Harvey J. Estimating census district populations from satellite imagery: some approaches and limitations. Download references. All authors provided critical comments. All authors read and approved the final manuscript. We gratefully acknowledge the principal investigators and field staff of surveys that provided reference data which was collected as part of other studies not linked to this paper.