1.1 Automatic acquisition of political actions data

Acquiring observations on human actions from news outlets is a form of information extraction (see Piskorski & Yangarber (2013)). An automatic system is given for acquiring ecosystem-affecting human actions from online news stories. This system combines web-crawling with a natural language parsing algorithm to extract reports of human actions that pertain to an ecosystem that is to be managed. The parsing step of this system is accomplished with a modified version of the memory-based, shallow parsing algorithm of Daelemans, Buchholz & Veenstra (1999).

Most parsing algorithms recognize several types of verbs. Single-word verbs can be either regular or irregular. And a multi-word verb uses more than one word to convey its meaning, e.g. “picked up.” See British Council (2017) for a review of these types. Here, all of these types are subsumed under the term m-word verb where m is a positive integer. The Daelemans, Buchholz & Veenstra (1999) algorithm is modified by replacing their word part-of-speech lexicon with a stored lookup table of action-related m-word verbs, direct-object phrases, and prepositional phrases. See Aarts (2011) for definitions of these phrase types.

Human actions that can affect an ecosystem range from direct, physical contact with the ecosystem such as poaching plants and animals – to more indirect actions such as setting aside land for a wildlife reserve. Because human actions are embedded in political processes (such as laws defining what poaching is, what constitutes ecosystem protection, and what landuses are allowed) hereafter, human actions that affect an ecosystem will be referred to as political actions to emphasize this political milieu that ultimately characterizes all ecosystem-affecting human actions.

A more free-form approach to information extraction is to mine text for topics for which people have sentiments toward. People now often express their sentiments on social media such as Facebook™ or Twitter™. Algorithms have been developed to discover these topics and associated sentiments. One such algorithm is given herein and, as an example, used to extract topics and sentiments surrounding ginseng root (Panax) consumption.

1.2 Automatic acquisition of ecosystem data

Remote sensing of ecosystem variables can be an inexpensive way to regularly acquire some information on an ecosystem’s status. Many countries allow their citizens to purchase satellite image products from private firms, hereafter referred to as image providers. Small spatio-temporal patches of such products have prices similar to those of personal computers. Using such images to extract data on ecosystem metrics is one way to break the data bottleneck referred to above. An algorithm is developed herein to estimate abundance of a pre-determined species of megafauna using two satellite images taken sequentially in time. An example of its use is shown.

A fundamental contradiction, however, arises when proposing to publish the locations plants or animals belonging to endangered species. On the one hand, the central goal of a public resource of political-ecological data is openness. On the other hand, making these locations public can lead to the loss (through poaching) of the very plants or animals the site has been set up to protect. In other words, there is a challenge to make such sensitive data accessible to those who intend to use it to further the ecosystem’s sustainability but keep it from those who, intentionally or not, would use it to degrade the ecosystem’s sustainability.

Based on these considerations then, an ecosystem management tool needs two types of security protocols to protect its data assets: one to safeguard the data against theft, called here a data security protocol, and one to protect against the dissemination of the locations of plants or animals belonging to endangered species to poachers. This latter protocol is referred to here as a data release security protocol.

The distinction between these two intended uses of ecosystem data is sometimes difficult. For instance, based on modeling, a data requestor may have concluded that culling a wildlife population in a certain ecosystem is the best way to make that population sustainable. Should this requestor be allowed to access data on the locations of plants or animals belonging to endangered species to aid their culling operation? A new data release security protocol is developed herein that uses a combination of expert judgement, a video call, and advances in cryptography to negotiate these sometimes complicated determinations in order to limit the release of sensitive ecosystem data to only those intending to promote the ecosystem’s sustainability.

1.3 Previous work

Several systems have been created for automatic acquisition of political events (see Schrodt & Van Brackle (2013)). These systems use software to parse news stories, and then associate each story with a member of a political action ontology. These systems have been developed for events surrounding international relations and terrorism. To this author’s knowledge, acquisition of joint observations of political events and associated ecosystem responses is not present in the literature.

Michener (2015) lists 19 data portals to ecological data. One of these is the well-known Long Term Ecological Research (LTER) portal (LTER 2017). LTER consists of data from mostly fixed sites in the U.S.. LTER is not intended to focus on a specific ecosystem let alone a political-ecological system. Rather, LTER is a depository for data from individual, unconnected research efforts that are funded to answer specific ecological questions rather than to build a picture of a single ecosystem through time. This is in-part a result of the short-term nature of most ecological research grants as mentioned above. LTER avoids the risk of having the locations of endangered species members fall into the hands of poachers by simply not placing such data on its portal, see Cedar Creek (2018).

In general, current data portals have dealt with the issue of releasing sensitive data by simply not doing so. As another example, the access page for survey data on endangered plant species in the Gammon Ranges National Park in Australia provided by the TERN data portal contains the following note:

Project Completeness

Restricted – Species data: Observational data of sensitive species

is obfuscated.

(Gammon Ranges 2017).

In summary, current ecological data portals are mainly repositories for individual research projects as opposed to projects that follow a single ecosystem over a long time interval. And, in lieu of applying a data release security protocol that restricts release of sensitive data for sustainable uses only, these portals avoid the risk of releasing such data by simply denying access to it.

A static, one-time collection of social-ecological data on the Brazilian Amazon is described in Lima et al. (2016). To this author’s knowledge, however, other than the work reported on herein, there is no literature and no exemplars of websites that continuously gather political-ecological data, offer it to the public through security protocols, and offer software for using its data to compute ecosystem management policies.

1.4 Article layout

The remainder of this article proceeds as follows. In Section 2, as a specific example of automatic acquisition of political data, a new method is described for acquiring stories about political actions that affect an ecosystem along with a new method for discovering themes contained in social media posts about wildlife products. For illustration, political actions surrounding the poaching of the white rhinoceros (Ceratotherium simum) in South Africa are automatically acquired, and the product themes discovery algorithm is used to detect themes surrounding the use of the ginseng root – a plant currently experiencing poaching pressure. Then, as a specific example of automatic acquisition of ecosystem data, a new satellite image-based method is described in Section 3 that can inexpensively acquire spatio-temporal data on those ecosystem metrics that can be remotely observed. This algorithm is used to estimate the abundance of elephants inside San Diego Zoo Safari Park in Southern California, United States. In order to show how these three types of data could be collected and distributed, the workings of an ecosystem management tool’s website is described in Section 4 along with an example focused on the conservation of white rhinos. The white rhino is facing poaching pressure driven by the illicit trade in rhino horn. I discuss issues surrounding political-ecological data acquisition and associated data release security protocols in Section 5. I reach several conclusions concerning the future of such systems in Section 6.

2.1 Acquiring political actions stories from online sources

An ecosystem management tool needs a constant stream of data on political actions. The frequency of such actions affecting any given ecosystem is usually too great for human coding to be used. This is because humans take too long and are too expensive to have them continuously reading and coding online news articles about a particular ecosystem. Indeed, an entire field of machine coding of political events has developed to address this speed and cost bottleneck in the acquisition of political event data. See Schrodt & Van Brackle (2013) for an overview of these efforts. Therefore, here, an automatic method is developed for acquiring political actions that relate to a particular managed ecosystem. This method is a new text mining (see Ignatow & Mihalcea (2018)) method that is based on a taxonomy of political-ecological actions that is described next.

2.1.6 Application to rhino conservation

The political actions extraction algorithm is applied to the political-ecological system of rhino conservation in South Africa. Online news stories were searched over the period January 2011 through June 2017. This data is summarized in Table 2 and plotted as a time series in Figure 1.

Table 2

Summary measures of the political actions data set acquired for the rhino conservation political-ecological system.

Summary measure	Value
Number of stories	205
Number of unique EMAT action types	4
Number of rural resident action types	1
Number of rural resident actions	111
Number of anti-poaching unit action types	3
Number of anti-poaching unit action types	94

Figure 1 

Political actions observed between 2007 and 2016 that are related to the rhino conservation political-ecological system. A plot symbol is the first letter of the group executing the output action: (p)oachers, and (a)nti-poaching forces. Estimates of rhino abundance in Kruger National Park, South Africa appear in the lower plot.

2.2 Detecting themes around wildlife products

Another type of information that can be extracted from online sources is data on sentiments expressed towards aspects of the ecosystem such as its wildlife, wildlife products, poaching, and anti-poaching measures. For example, posts from Facebook and Twitter made by Asians in the market that mention the topic of rhino horn could be used to discover what other topics and/or sentiments are associated with rhino horn. In addition, because rhino poachers often maintain a social media presence, sentiments towards the perceived risks and benefits from rhino poaching could also be collected from individuals living in areas that harbor poachers.

Detecting sentiments that consumers hold towards consumption of a wildlife product in a way that is not affected by well-known survey biases (see Nuno & ST. John (2015)) could prove to be an accurate way to determine the effectiveness of wildlife product demand reduction campaigns (see for example, United States (2016)). It is possible, however, that consumers do not have a positive opinion of a wildlife product but rather view it as a necessity. But typically, sentiment analysis implicitly assumes that social media participants have a positive-to-negative valence towards a product. See for example, Lim & Buntime (2014). For a relatively unexplored product such as rhino horn, it may be more useful to first discover what characteristics consumers attach to the product through an exploratory analysis of social media postings concerning it. If such a positive-to-negative valence does appear, then a more accurate Method to ascertain the strength of that sentiment may be applied such as the algorithm of Lim & Buntime (2014). Otherwise, demand reduction campaigns should be tuned to address the most prevalent themes associated with the product to be targeted. What is needed then, is a way to find themes that consumers associate with a product. To this end, a new product themes discovery algorithm is given next.

3.1 Remote sensing of megafauna abundance

Due to their size, the abundance of a megafauna species may be estimated with object recognition algorithms applied to satellite images (see Yang et al. (2014)). Consider a region, R that is to be monitored. Say that R is partitioned into a grid of N quadrats. Purchase two sets of images, I_jk, j = 1, 2 and k = 1, …, n separated in time by one repeat interval. For example, the WorldView-4 satellite has a repeat interval of three days (Richardson 2016). Image I_1k is colocated with I_2k.

The motivation for using two images is that animals differentiate themselves from stationary objects such as terrain features and vegetation by moving to different locations between the two images. This idea has previously been employed in the animal detection algorithms of Oishi & Matsunaga (2014) and Terletzky & Ramsey (2014).

Some type of spatial sampling is usually needed because the range of many megafauna species may be so large that an image of such a large region may be cost-prohibitive. For example, say that rhino abundance in KNP is to be estimated. The area of this region is about 19,485 km². Currently, WorldView-4 image products cost about US$16.00 per km² (see the Example, below).

A terrestrial species may occupy a landscape in social groups of a particular size. For example, white rhinos are usually found in groups of 2 to 3 individuals (Shrader & Owen-Smith 2002). This means that such a species will distribute itself across the landscape as a collection of small clusters of individuals. Landscape characteristics such as water and food availability affects where a social group chooses to locate itself in the landscape.

If a population is highly clustered, a sampling unit that has a small area is more efficient than a smaller number of units each with a large area. This is because on average, much of the area of a large unit is wasted in that it will contain no animals. An equivalent case of this characteristic is the use of a ship to sample a marine animal: a point in the ocean is essentially the (zero) area of the sampling unit. For example, Mier & Picquelle (2008) find that a systematic sample is best for sampling a highly patchy marine animal.

Because satellite imagery is priced by unit of area, to keep imagery costs low, it is desirable to use the minimum amount of area that can provide a useful abundance estimate. Further, unlike ground-based sampling costs, there is no travel-time expense to consider when selecting a sampling design. For example, Panahbehagh (2016) cites reduced travel time as an advantage of adaptive sampling designs. These considerations suggest that a sample composed of satellite images should consist of a large number of small areal extent images located according to a systematic sampling design.

What should be the spatial layout of such a sample? Through a simulation study, Ferguson (1992) finds that a size-30 sample positioned on a region-filling, equally-spaced herringbone layout usually results in estimates with modest standard errors when the total area of animal clusters is about 5% of the region’s area.

3.2 Megafauna abundance estimation algorithm

The following two-image abundance estimation (TIAE) algorithm uses two co-located images to return an estimate of the abundance of a megafauna species. All images have been converted from their delivered format (usually GeoTIFF) into the ENVI bands interleaved by pixel (.bip) format. One way to do this is with the free GDAL utility, gdal_translate.exe (GDAL 2017).

1. Let l_a be the maximum length (expressed in numbers-of-pixels) of the animal species for which abundance is to be estimated. Execute the following object-detection procedure first on I_1k, and then on I_2k to locate the center points of all objects in each image. Set g_k, and h_k to the number of these objects in I_1k, and I_2k, respectively.
   Object-Detection Procedure: Step across each row of the image. Each pixel’s set of spectral values is compared to those of the animal’s spectral signature. If a match is found, the pixel’s location is added to the set of a previously-started candidate object that shares a corner or side with the pixel as long as doing so doesn’t make the object have a maximum inter-pixel distance larger than l_a. Otherwise, a new set of pixel locations is started for a new candidate object. After all pixels have been visited, those candidate objects are retained that have an inter-pixel distance larger than 0.3l_a.
2. Let c_k be the number of objects that are colocated between these two images. Declare two locations to be the same if their distance apart is less than l_a/5.
   Compute g_k = g_k – c_k so that g_k becomes the number of object locations in I_1k that are empty in I_2k. Similarly, compute h_k = h_k – c_k to arrive at the number of object locations in I_2k that are empty in I_1k.
3. Let β_f > 0 parameterize the tendency of species members to stay close to their social group. This tendency becomes stronger as β_f becomes larger. For i = 1, …, c_k, let
   
   (3)

   $f_i\equiv \max \left\{\underset{{}_{j\in (1,\dots ,g_k)}}{\min }[d_{1\mathit{\text{ij}}}],\underset{j\in (1,\dots ,h_k)}{\min }[d_{2\mathit{\text{ij}}}]\right\}$

   M3 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {f_i} \equiv \,\,\max \left\{{\mathop {\min }\limits_{_{j \in \,(1,\, \ldots,\,{g_k})}} [{d_{1ij}}],\;\mathop {\min }\limits_{j \in \,(1,\, \ldots \,,\,{h_k})} \,[{d_{2ij}}]} \right\} \] \end{document}
   
   where d_lij ≡ distance (object_i, animal_lj) (in meters) and animal_lj is the location of the j^th unique animal in image l.
   Let the probability that a colocated object is actually an animal be
   
   (4)

   $\text{logit}(p_{\mathit{\text{ai}}})\equiv {\beta }_0+{\beta }_f\frac{1}{(1+f_i)}.$

   M4 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {\rm{logit}}({p_{ai}})\,\,\, \equiv \,\,{\beta _0} + {\beta _f}\frac{1}{{(1\,\, + \,\,{f_i})}}. \] \end{document}
   
   Using these probabilities, an adjusted count may be computed of colocated animals between the two images that takes into account the possibility that each colocated object is an animal. This adjusted count is $o_k\equiv {\Sigma }_{i=1}^{c_k}{I}_{\{p_{\mathit{\text{ai}}}>{\pi }_a\}}(p_{\mathit{\text{ai}}})$M6 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ {o_k}\, \equiv \,\,\Sigma _{i = 1}^{{c_k}}\,{I_{\{ \,{p_{ai}} > \,{\pi _a}\} }}({p_{ai}}) \] \end{document} where π_a ∈ (0, 1).
4. The detected number of animals in the k^th pair of images is y_k ≡ (g_k + h_k)/2 + o_k. The idea behind the first term is to take advantage of the size-2 replication to average out some of the object detection algorithm’s false positives and false negatives. The second term adds-in each animal that either did not move between images, or was one of the two animals that occupied the same location between images.
5. The systematic sample’s estimate of abundance is $S={\rm N}\widehat{\mu }$M11 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ S = N\hat{\mu} \] \end{document} where $\widehat{\mu }=(1/n){\Sigma }_{k=1}^n{y}_k$M7 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \hat \mu = \,\,(1\,/\,n)\,\Sigma _{k = 1}^n{y_k} \] \end{document} . The design-based variance of this estimator is $((N-n)/(\mathit{\text{Nn}}(n-1))){\Sigma }_{k=1}^n{(y_k-\widehat{\mu })}^2$M8 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ ((N - n)\,/\,(Nn\,(n - 1)))\,\Sigma _{k = 1}^n{({y_k}\, - \hat \mu)^2} \] \end{document} (see Mier & Picquelle (2008)).

This algorithm has parameters β₀, β _f, and π_a that need to be assigned values. The idea behind equations (3) and (4) is that a colocated object has a chance of being an animal if there is a nearby unique animal. Also, no GIS layers on habitat features are needed as the characteristics of how the animals use the landscape is captured by the locations of the g_k and h_k animals in the two images, respectively. And, the colocated animals inherit these characteristics through (4).

3.3 Example

In order to assess the accuracy of the algorithm, it is necessary to count animals in a region where there is a known number of animals. This is a form of validation, called ground truth (see Wang et al. 2016). An ideal case would be an image of a protected area containing a population of a large, endangered species. Such images are difficult to obtain due to concerns by protected area managers of the images falling into the hands of poachers.

Therefore, to allow validation of the proposed algorithm, consider the elephant exhibit, “Elephant Valley” at the San Diego Zoo Safari Park (33.09745 latitude, –116.99572 longitude) in Southern California, USA which currently contains 13 elephants (SDZSP 2017). Two images of this park were downloaded in GeoTIFF format from a commercial image provider (TerraServer 2017). Because elephants are relatively large (5–7.5 m long) (TheAnimalFiles 2014), pan-sharpened images (see Tu et al. (2004)) in the “standard +” resolution were purchased at a cost of US$110.00 per image. These images were taken on August 8, 2016 (Figure 2), and October 10, 2016, respectively. These dates are separated by more than the satellite’s revisit period. As these particular elephants are captive, however, the TIAE algorithm will not be biased by emigrating or immigrating elephants.

A “standard +” image with area of 6.23 km² is 3698 lines (rows of pixels) × 2773 samples (columns of pixels). Consequently, the inter-pixel distance is

where ar = 3698/2773 is the aspect ratio. This resolution is sufficient to detect an elephant.

A 1.5% tolerance is used on each spectral component to decide if a particular pixel’s spectral component matches the corresponding component of an elephant’s spectral signature. This signature is found using Microsoft Paint’s™ Color Picker tool. Here, an elephant’s maximum length is judged to be 6.23 m. Expressing this value in pixels yields 6.23/0.7789 = 8 pixels. This value is used as an upper limit on the size of objects that are predicted to be elephants.

Use of these constants with the TIAE algorithm results in an elephant abundance estimate of 13 making the algorithm 100% accurate when run on this example’s image.

4.1 Operation

The institution that intends to host an ecosystem management tool website first hires a website coordinator to perform the following tasks:

1. Maintain the website’s availability.
2. Receive new political-ecological data and determine its type (sensitive or publicly available).
3. Enter publicly available data into the website’s public data folder, and sensitive data into its hidden, sensitive data folder.
4. Execute protocols for data security, and data release security.

The website coordinator has a fixed two-year tenure and is not eligible for re-appointment.

4.2 Security protocols

As mentioned in Section 1.2, ecosystem data can be used against the ecosystem. One example of this potential for abuse is would-be poachers accessing satellite images that show the locations of individual rhinos.

The potential for abuse begs the question: what security protocols should be followed for keeping an ecosystem management tool’s website secure? At first glance, one such protocol should be that locations of animals belonging to an endangered species are not made publicly available. There is a price to be paid, however, for adhering to such a protocol. There are several scientific reasons for making the original animal locations available to the public. These include the use of such data to develop and statistically estimate the parameters of animal movement models (see Allen & Singh (2016)); studies on animal social behavior (see de Weerd et al. (2015)); and habitat selection studies (see Francis et al. (2017)). One apparent way around this difficulty is to geographically mask the image so that animal locations cannot be determined from it (see Zandbergen (2014)). Almost any masking algorithm, however can be reversed given enough time. A safer approach then, is to avoid placing any images that show animal locations in the website’s public data folder and instead, implement the following data security, and data release security protocols.

1. A background check is conducted on an individual who is to be hired into a website coordinator position. Once hired, this individual is subjected to continuous screening (see Maurer (2016)) in order to detect any event that might cause the individual to become corrupt and begin to sell sensitive ecosystem data to wildlife traffickers. Automated, continuous screening of employees with access to sensitive corporate information is becoming more common. Continuous screening is also known as infinity screening (see Purpura (2013: 136)), post-hire screening, post-employment screening, and post-employment background checks. Some conservation agencies already engage in continuous screening. For example, SANParks conducts random polygraph tests of their rangers as part of their continual monitoring for rangers who might begin to collude with rhino horn traffickers (Presence 2016).
   Many firms and universities have staff and protocols for conducting security clearances for university employees who will be working on classified projects for the United States government (e.g. GMU (2017)). Such capability would be leveraged to provide background checks on newly-hired website coordinators and continuous screening of them throughout their two-year tenure. This fixed tenure is seen as needed because doing so gives a website coordinator less time to become corrupt. It is well-known that the size of loss due to fraud increases with the length of tenure of the employee committing the fraud (Kranacher, Riley, Jr. & Wells 2011: 18).
2. When new data is received by the website coordinator, (s)he performs the following tasks.
   1. Designates the data as sensitive if it contains specific locations of plants or animals belonging to endangered species.
   2. Any electronic files associated with this data are scanned for malware or any other hidden software attachments. Such attacks of course, would be aimed at downloading data that identifies the locations of plants or animals belonging to endangered species.
   3. Files deemed to contain sensitive data are collected into a RAR folder (see Library of Congress 2017). This RAR folder is embedded in a carrier image via the cryptographic and steganographic security transformation referred to as subcodstanography (Köhler, Lukić & Čik 2017). Then, this security-transformed file along with its symmetric key is stored in a hidden folder on the website.
   4. If a request is made for the data contained in this image, the website coordinator executes the following newly developed data release security protocol.
      1. A judgement is made of the likelihood that the requestor plans to use the image for purposes of poaching (either by their own hand or by selling the image to other, would-be poachers). If so, the request is denied. This judgement is based on the outcome of a vote of a panel of three website-affiliated scientists who have reviewed the requestor’s credentials. These affiliated scientists hold two-year, non-renewable appointments as data requestor reviewers.
      2. If the request is granted, the website coordinator conducts a video call (see Martinez & McLauglin (2017)) interview with the requestor. The purpose of this interview is to receive the public key (see Orman (2015: ch. 3)) from the requestor. This key will be used by the website coordinator to encrypt the carrier image’s symmetric key before it is emailed to the requestor. The use of a video call interview is intended to defeat any so-called Man-in-the-Middle (MITM) attacks (see de la Hoz et al. (2014)) during public key transmission from the requestor to the website coordinator and works by verifying that the apparent requestor is not in reality an attacker masquerading as the legitimate requestor. In order to do this, the website coordinator compares the face of the person on the video call with known photographs of the requestor, and asks a number of questions to verify that the person in the video call is not an attacker impersonating the requestor. After the website coordinator has decided that the video call interviewee is indeed the legitimate requestor, the requestor is asked to hold up to the video call camera a piece of paper that contains a 256 bit public key expressed as 64 hexadecimal numbers written in four rows of 16 hexadecimal numbers each. The website coordinator takes a screenshot of this piece of paper. The video call software’s screen-sharing utility is not employed for this activity as that communication channel could be subject to a MITM attack itself. This new, video call-based approach to authentication is an extension of the camera phone approach of McCune, Perrig & Reiter (2005).
      3. The website coordinator then sends to the requestor the symmetric key of the carrier image that holds the requested sensitive data. This symmetric key is sent as an encrypted email using the requestor’s public key. Once received, the requestor uses his/her public-private key pair to decrypt the image’s symmetric key.
      4. Finally, the website coordinator emails the subcodstenographically encrypted carrier image to the requestor. The requestor unpacks the sensitive data from the carrier image using the image’s symmetric key received in the previous step.

Conditional on the video call interview successfully detecting a would-be poacher, the remainder of this data release security protocol is essentially secure as it uses cryptographic algorithms that are known to be unbreakable. These two security protocols may appear to be unnecessarily complex. The rationale for this abundance of caution is two-fold. First, cyber crime can be catastrophic because the breaching of a single firewall can expose all data taken on an ecosystem to theft. Second, for the case of sensitive data on the locations of animals and/or plants belonging to endangered species, once a species goes extinct in-part through poaching, it may not be possible to bring it back. In other words, a species is essentially irreplaceable. These two characteristics of biodiversity protection suggest that any online archive of animal location data should be as resistant to intrusion as possible. This view is in agreement with current assessments in the literature towards website security. For a discussion, see Anwar et al. (2017). In essence, then, in lieu of a perfectly secure data center, the website should be run with protocols that are intended to make it as resistant to intrusion and misuse as possible.

4.3 Rhino conservation example

An ecosystem management tool is being developed for the management of white rhinos in South Africa (Haas 2018). The homepage of this tool’s website is displayed in Figure 3. The page has an intentionally simple layout.

This website makes available for download data on anthropogenic actions that affect the rhino-hosting ecosystem, estimates of rhino abundance, and a software package (namely, the id package) for computing ecosystem management policies. This software includes an implementation of the algorithm for computing the most practical ecosystem management plan. In addition, the website makes available all required input files to run the rhino conservation political-ecological simulator, documentation of this agent-based simulation model, and a user’s guide to running it. Note that the id package contains a number of capabilities that are not the focus of this article.

There is a need to build and statistically fit models of political-ecological systems that can be used to assess the effects of proposed ecosystem management options. For example, Haas & Ferreira (2018) develop a political-ecological model composed of an agent-based submodel of poachers interacting with an individual based submodel of the south African rhino population, an agent-based submodel of southeast Asian rhino horn consumers who purchase the poached rhino horn from these poachers, and a submodel antipoaching forces working to curb this poaching activity. Combinations of antipoaching initiatives and economic opportunities for the poachers need to be evaluated as to their probable effectiveness at changing local people’s inclination to poach rhinos and the consequent effect on the rhino population (see Haas & Ferreira 2018). To do this, political-ecological data is needed to statistically validate such a large political-ecological model. One such statistical approach based on maximum simulated likelihood (Haas 2011, ch. 4) requires data on some subset of the model’s observable variables. Model validation consists of several tasks including formal statistical parameter estimation, goodness-of-fit assessment, and a sensitivity analysis. When the model is large and stochastic, any of these activities requires large amounts of high-dimensional political-ecological data and access to high performance computing.

To make specific the nature of a submodel’s parameterization, consider the rhino poachers group decision making submodel. This submodel is programmed as a joint probability distribution of discrete random variables defined through tables of conditional probability constants. These constants are the submodel’s parameters. These parameters need to have their values estimated before the submodel can be used to study the effect of different management policies on a poacher’s decision to poach. This decision making diagram is shown in Figure 4. It is emphasized that this is a computational model that is capable of simulating a group reaching a decision rather than a non-functional, qualitative framework of the decision making process.

Mathematically, parameter values are found that maximize the agreement between the submodel’s decision output and observed decisions made by real-world poachers over a particular time period. Let out_i^(obs) (t_j) be the observed output action – target combination of group i at time t_j; out_i^(opt) (t_j) be the output action – target combination computed by the group i’s influence diagram at that time; and M_ij be unity if out_i^(opt) (t_j) = out_i^(obs) (t_j) and zero, otherwise. Then, the overall agreement of all group submodels with the entire set of observations on political actions is ${\Sigma }_{i=1}^m{g}_S^{(i)}({\beta }^{(i)})$M9 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ \Sigma _{i = 1}^m\,g_S^{(i)}({\beta ^{(i)}}) \] \end{document} where $g_S^{(i)}(\beta )\equiv {\Sigma }_{j=1}^T{M}_{i,j}$M10 \documentclass[10pt]{article} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \[ g_S^{(i)}(\beta)\,\, \equiv \,\,\Sigma _{j = 1}^T\,{M_{i,\,j}} \] \end{document} .

5.1 Building taxonomies

Building ecosystem-affecting action taxonomies such as the EMAT of Haas (2011: 123–141) is in its infancy. A steady stream of such actions along with an automatic way to discover new ones will allow such taxonomies to be built more quickly and allow comprehensive evaluation of taxonomy quality. In this case, quality refers to the taxonomy’s acceptance, comprehensiveness, completeness, mutual exclusiveness, repeatability, and specificity (Hansman & Hunt 2005). As the EMAT is an extension of a general political actions taxonomy developed by Leng (1999) and Leng & Singer (1988), it inherits much of this original taxonomy’s quality.

5.2 Demand reduction campaigns

Data produced by the new product themes discovery algorithm of Section 2.2.1 is critical to the effective design of advertising campaigns aimed at reducing the demand for wildlife products. For example, expensive advertising campaigns in China that attempt to discourage the consumption of rhino horn assume that the Chinese consume rhino horn as a way to broadcast their success in life, similar to why the Vietnamese consume rhino horn (Truong, Dang, & Hall (2016)). If, in reality, however, Chinese consumers buy rhino horn because they believe it will make them healthy, a campaign focused on social status may not be as effective as expected.

In general, demand reduction campaigns to reduce the demand for wildlife products such as rhino horn need to be tuned to address the beliefs that consumers hold towards these products (Liu et al. 2015). Developing automatic algorithms that can regularly gauge what these beliefs or themes are over many years is crucial to the success of such campaigns as it can take many years to change attitudes. For example, attitudes towards cigarette smoking changed slowly but significantly over the years 1947–1974 in the United States due to anti-smoking campaigns (Warner 1977). Because the consumption of many wildlife products is not physically addictive, it may be easier to reduce demand for them relative to products such as cigarettes or drugs that are.

5.3 Data type interrelationships

A political-ecological system has effects that flow in both directions: anthropogenic actions affect an ecosystem, such as poaching affects rhino population dynamics but the ecosystem, in-turn, affects political and social processes such as wildlife causing crop and livestock damage, and downward trends in rhino abundance causing increased law enforcement activity to attempt to curb the poaching of rhinos.

Therefore, multivariate datasets composed of observations on political actions coupled to ecological actions are needed to formulate, estimate, validate, and then use models of political-ecological systems to help formulate policies that have some chance of making the managed ecosystem sustainable.

As mentioned above, Haas & Ferreira (2016a) build a stochastic, political-ecological model of the rhino management system by coupling together an agent-based submodel of consumer demand for rhino horn, a submodel of rhino poachers, and an individual-based submodel of South African rhino population dynamics. After fitting this model to data, these authors use it to show that changes in demand for rhino horn affect poaching effort which in-turn, affects rhino population dynamics. Further, Haas & Ferreira (2016b) use this model to show that only a coordinated policy has a chance of sustaining the South African rhino population. This coordinated policy would include finding alternative economic opportunities for would-be poachers, increasing antipoaching enforcement efforts, disrupting poaching syndicates (see Haas & Ferreira (2015)), and reducing the demand for rhino horn in countries where it is consumed.

Focusing on the poachers, Haas & Ferreira (2018) use poaching action data to estimate the parameters that represent the decision of a poacher to poach a rhino along with the effect that such poaching has on rhino abundance as captured by the above-mentioned submodel of the South African rhino population.

This overview suggests that some political-ecological data is clearly inter-related such as data on political actions and an ecosystem’s responses to those actions. Other types of data, although not directly related, are part of causal processes such as wildlife product themes that point to motivations that drive demand for wildlife products. An early model of a wildlife product consumer’s decision making is given in Haas & Ferreira (2016a). This model does not attempt to capture the goals that such a consumer is trying to attain such as social status or health guarantees – but rather focuses only on how the consumer’s price sensitivity may affect their decision to purchase rhino horn or not. For this early model, data on the themes surrounding rhino horn would appear to not be related to political actions data or rhino abundance data. But if a consumer decision making model replaced the simple price sensitivity model, the product themes data would become related to these other two data types. It appears then, that the inter-relatedness of different political-ecological data types and indeed, the very types of data that need to be acquired, depends on what models of the managed political-ecological system are being entertained. More complete models will need more types of data to support their estimation, validation, and use in formulating ecosystem management policies.

5.4 Data release security protocols

It is possible that poachers could purchase satellite images themselves, analyze these images using software taken from (say) the website described herein, and then poach those animals that they detect. Poachers are known to hack into GPS location signals sent to satellites from radio-collared animals (Welz 2017), and to find animals from location information contained in scientific articles that announce the discovery of new species (Neslen 2016). Further, Koh (2015) predicts that poachers will someday locate animals to poach by hacking into the video signal transmitted by monitor drones flown by protected area managers.

An online search performed circa 2018 for articles reporting on the use of satellite imagery by poachers does not produce any results. At present then, poachers may not have the analytical resources needed to post-process satellite imagery. But as image processing software becomes easier to acquire and use, the world may see such use of legally purchased satellite images.

The threat of such a scenario suggests that image providers themselves should employ data release security protocols similar to the ones proposed in Section 4, above. Simple protocols such as only releasing older images may not be practical. For instance, legitimate consumers of these products, e.g. marketing research firms, may require images that are as recent as possible. Hence, a data release security protocol that restricts the image provider to selling images that are of some minimum age may not be followed by the image provider.

Federal and state environmental protection agencies might argue that sensitive ecosystem data should be strictly controlled by them in order to guard against its abuse. But these very agencies may have, unbeknownst to them, corrupt personnel who either give sensitive ecosystem data to wildlife traffickers or join in the slaughter themselves. A case in point is the incident of two SANParks rangers being arrested for rhino poaching (defenceWeb 2017). Leaving ecosystem data security in the hands of designated government agencies then, may not be a 100% secure solution.

A related issue concerning data release security protocols is the issue of acquiring remote imagery for a bespoke date and region. For instance, images used in the elephant abundance example of Section 3.3 are from an image provider’s online catalog for which image dates were arbitrarily determined by the image provider. If an ecosystem management tool’s website coordinator were to request images taken on other dates, there may be permission issues. This potential difficulty is not explored here but may become problematic if requested dates are very recent.

5.5 Limited access to the web and computing resources

Use of a website to disseminate political-ecological data presupposes unrestricted access to the web. This is not the case in several countries ruled by totalitarian regimes. Providing such information to people in these countries is critically important but this article offers no suggestions for how to do so.

Estimation of simulator parameters, execution of a sensitivity analysis, and computation of a most practical ecosystem management plan requires access to high performance computing systems – although it is possible to execute partial analyses on personal computers. Many interested individuals may not have access to such high performance computing resources and hence would only be able to examine the output of simulator estimation, sensitivity analyses, and most practical ecosystem management plan computations produced by others. But such outputs may not be shareable in the first place. For example, a private firm may wish to keep the results of such analyses confidential to protect its competitive advantage. Therefore, open and planet-wide access to computing resources for purposes of analyzing political-ecological data is needed but is currently a challenge.

5.6 Evaluating an ecosystem management tool

The worth of the proposed ecosystem management tool is in its ability to contribute to biodiversity, i.e., species persistence. According to an ecologically motivated definition given in Haas (2011, p. 10), a species has low extinction risk if its probability of becoming extinct at some point in time 50 animal generations into the future is less than 0.01. Therefore, the success of an ecosystem management tool should be measured by computing the managed species’ extinction probability 50 animal generations into the future. The tool is more effective as this probability approaches 0.01. To make this calculation, a stochastic simulation model of the managed political-ecological system needs to be fitted to political-ecological data and then run forward in time under ongoing and proposed management policies to arrive at the species’ extinction probability 50 animal generations into the future.

Haas & Ferreira (2018) performed a shortened version of this calculation for the South African rhino population. There, rhino abundance data had been acquired through expensive helicopter surveys and was allowed to be included in a publication only through the existence of a collaborative research project between SANParks, South Africa and an academic. The TIAE algorithm described herein could be used to acquire megafauna abundance data when such research relationships and/or aerial surveys do not exist.