63 Implications of the 2013 Python Challenge® for Ecology and Management of Python molorus bivittatus (Burmese Python) in Florida Frank J. Mazzotti1,*, Mike Rochford1, Joy Vinci1, Brian M. Jeffery1, Jennifer Ketterlin Eckles2, Carla Dove3, and Kristen P. Sommers4 Abstract - The 2013 Python Challenge® provided an opportunity to learn more about the ecology and management of Python molorus bivittatus (Burmese Python). Goals of the 2013 Python Challenge were to raise awareness about Burmese Pythons, remove pythons, increase public participation and agency cooperation in removal and reporting of pythons, increase knowledge of python ecology, and examine effectiveness of incentives to increase public participation in invasive wildlife management. Over 1500 participants registered for the competition. Sixty-eight Burmese Pythons were removed during the Challenge. Thirteen females (19%), 54 males (79%), and 1 young-of-the-year (1%) python of undetermined sex were captured. More pythons—73 (68 from the Challenge and 5 incidental)—were removed during the 2013 Python Challenge period than during similar time periods during 2008–2012. We found no evidence of unintended consequences such as removal of native species. We identified 13 prey species: 6 mammals (46%), 6 birds (46%), and 1 alligator (8%). The potential of recreational-harvest incentive programs to impact python populations is uncertain. Incentive programs are potential tools in invasive-species management programs, but they should be managed diligently and evaluated for effectiveness. Introduction Invasive species are a major, pervasive threat to conservation of biological diversity (Clout and Williams 2009). Invasive species can displace or eliminate native species through several processes including competition (Human and Gordon 1996), predation (Rodda and Savidge 2007), and toxic effects (Lentic et al. 2008). Florida, particularly the southern subtropical portion of the state, has proven vulnerable to establishment and invasion by nonnative species. Florida currently has more established nonnative animals than any other state (Mazzotti and Harvey 2012) and more nonnative reptiles and amphibians than anyplace else in the world, with over 140 introduced species and 50 established species (Krysko et al. 2011, Meshaka 2011). Reptiles, in particular Python molurus bivittatus Kuhl (Burmese Python), have become the symbol of this ecological invasion (Harvey et al. 2008, Kraus 2009). Burmese Pythons are native to Southeast Asia and have been popular in the pet 1Fort Lauderdale Research and Education Center, University of Florida, 3205 College Avenue, Davie, FL 33314. 2Florida Fish and Wildlife Conservation Commission, 3205 College Avenue, Davie, FL 33314. 3Smithsonian Institution, Division of Birds, NHB E-600, MRC 116, Washington, DC 20560. 4Florida Fish and Wildlife Conservation Commission, 620 South Meridian Street, Tallahassee, FL 32399. *Corresponding author - fjma@ufl.edu. Manuscript Editor: John Placyk Everglades Invasive Species 2016 Southeastern Naturalist 15(Special Issue 8):63–74 Southeastern Naturalist F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 64 Vol. 15, Special Issue 8 trade, with 99,000 snakes imported from 1996 to 2006 (Harvey et al. 2008) and an unknown number bred in captivity in the US. They have become well established in the Everglades ecosystem in the southern portion of the Florida peninsula (Snow et al. 2007a). Diet studies of pythons in Florida indicate that they eat birds, mammals, and Alligator mississippiensis (American Alligator) (Dove et al. 2011; Snow et al. 2007a, b), and road surveys conducted between 1993 and 2011 showed a decline in mammals in areas where pythons were present (Dorcas et al. 2012). These findings, combined with direct evidence of python predation on rabbits, suggests a potential for Burmese Pythons to impair ecological functions in the Everglades (McCleery et al. 2015). Only recently has information become available to help resource managers respond to this invasion (e.g., Smith et al. 2016) and protect vulnerable resources such as endangered species and wading-bird colonies. Studies and unpublished data have consistently shown that Burmese Pythons are difficult to detect in Florida (e.g., Reed et al. 2011, Willson et al. 2011). In spite of this low detectability (even for highly experienced searchers; Willson et al. 2011), recommendations to resource managers frequently mention the concept of increasing public participation to increase removal of pythons. These recommendations include providing incentives, allowing the general public access to remote areas, and increasing involvement of individuals with experience catching snakes. The 2013 Python Challenge® provided an opportunity to cast these recommendations as management hypotheses and evaluate them. The 2013 Python Challenge® (hereafter, the Challenge), developed by the Florida Fish and Wildlife Conservation Commission (FWC) and partner organizations, was the first public competition to remove Burmese Pythons from state lands in Florida. The Challenge was a month-long event, lasting from 12 January to 10 February 2013, which included 2 categories of participants: the general public in a competition (hereafter, competitors), and experienced individuals permitted to remove pythons on state and South Florida Water Management District (SFWMD) lands in a permit-holder competition (hereafter, permittees). Prizes for longest and greatest number of snakes were offered as incentives to both categories of participants. The goals of the Challenge were to: (1) raise awareness about Burmese Pythons and other exotic wildlife in Florida, (2) increase public participation and agency cooperation in python removal and reporting of pythons to FWC, (3) remove pythons and collect data to increase knowledge of python occurrence and ecology, and (4) examine effectiveness of using incentives and training to facilitate public participation in invasive wildlife management. We evaluated goals 3 and 4 in this study. We evaluated goal 3 by examining demography, diet, habitat, and geography of removed pythons, and goal 4 by examining capture numbers and rates with those from previous efforts (see Methods for data sources). The purpose of this study was to report what we learned from the Challenge about Burmese Python ecology and management. Specifically, in our study, we addressed the following questions: What were the body sizes, sexes, diets, habitats, and locations of the pythons removed? Did the Challenge lead to removal Southeastern Naturalist 65 F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 Vol. 15, Special Issue 8 of non-target native species? How did the number of pythons removed during the Challenge period compare to the number of pythons removed during a similar time period in previous years? How did locations of pythons removed during the Challenge relate to locations of prior removals in southern Florida? Did incentives lead to increased removal of pythons? And, what were the python catch-per-unit-efforts (CPUEs) of the 2 participant categories across different habitats and compared to other efforts to remove pythons in Florida? Methods All Challenge participants were required to register and pay a $25 registration fee. The general and permit-holder competitions included 1558 and 24 registrants, respectively. All participants followed a set of rules and regulations that specified where pythons could be removed, how they were to be euthanized, and what data were to be collected (time, date, and locations of removal for all participants as well as survey routes for permittees). Competitors were also were required to review training materials or participate in online sessions to learn to identify Burmese Pythons and native species. For participants in the general competition, the only valid entries were Burmese Pythons harvested in the wild from 4 state wildlife management areas (WMAs): Everglades and Francis S. Taylor, Holey Land, Rotenberger, and Big Cypress (Fig. 1). The Big Cypress WMA was only open to this event through 1 February 2013, the end of small-game season. Participants in the permit holder’s competition were allowed to harvest and submit snakes from the 4 WMAs listed above plus 2 other areas jointly managed by FWC and SFWMD: Southern Glades Wildlife and Environmental Area (WEA) and Rocky Glades Public Small- Game Hunting Area (Fig. 1). Habitats in the interior of the competition areas are characterized as wet marshes and swamps with scattered, drier tree-islands surrounded by roads, canals, berms, and levees (Lodge 2010). Challenge rules required that competitors euthanize Burmese Pythons using humane methods and deliver them to an established drop-off location within 24 h of removal. Road-killed pythons were not counted in the competition. All participants were required to submit a data sheet for each harvested python on which they recorded their name and the GPS coordinates for each python removed. Permittees also were required to submit GPS track-logs of their survey routes for successful trips. All Burmese Pythons were verified, sexed, measured, weighed, and necropsied using standardized protocols (Farris et al. 2013). We placed Pythons into the following size classes based on total length: young of the year (YOY, <150 cm), juvenile males (150–199 cm), juvenile females (150–259 cm), adult males (>199 cm), and adult females (>259 cm) (Reed and Rodda 2009). We removed gastrointestinal (GI) tracts or their contents and individually bagged and froze, or stored them in 70% ethyl alcohol. Each sample was placed in a sieve, rinsed with water to clean individual food items (e.g., hair, bones, claws, feathers, beaks, osteoderms, scutes, and hooves), and air-dried overnight. We attempted to identify mammal, bird, and reptile remains to genus and species level. We sent avian samples to the Feather-identification Lab at the Smithsonian National Southeastern Naturalist F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 66 Vol. 15, Special Issue 8 Museum of Natural History, Washington, DC, for identification via methods described in Dove et al. (2011). We used microfeatures on dorsal guard hairs and/or by enamel patterns on molars to identify mammalian samples. We mounted guard hairs between 2 slides for examination under a dissecting microscope, then keyed them out according to Wilkins et al. (1982). Figure 1. State-managed lands open to participants in the 2013 Python Challenge®. WMA = wildlife management area. Southeastern Naturalist 67 F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 Vol. 15, Special Issue 8 We calculated CPUE by both distance (pythons/km) and time (pythons/hour). We did not calculate CPUE for Big Cypress WMA because we did not receive track logs from trips in that area. We determined time spent searching and distance traveled from GPS track logs provided by participants or from data sheets listing start and stop times. Permittees who caught pythons submitted estimates of effort for both successful and unsuccessful trips; 3 competitors also submitted estimates of effort for successful trips, although they were not required to do so. We did not receive data from 1544 competitors who did not catch pythons. We used additional sources of data to compare Challenge removals to previous removal efforts. First, we used data collected by the National Park Service (NPS) and the FWC on Burmese Pythons removed from federal and state lands (eliminating duplicate records) to calculate numbers of pythons captured during the contest period (12 January to 10 February) in previous years (2008–2012). We analyzed these data in ArcMap 10.1 to create a kernel-density map for locations of pythons removed during these periods. We compared capture rates reported during the Challenge with FWC data on Burmese Pythons removed year-round by individuals permitted to search for pythons on state lands from 2009 to 2012. We also reported capture rates for 70 walking surveys conducted year-round (2012–2014) along a survey route in the Southern Glades WEA. Detailed survey methods have not been published for these other sources of survey data. We used spatially referenced data layers for vegetation and water-management structures from FWC and SFWMD to distinguish between swamp/marsh/treeisland habitats and road/ berm/levee habitats. We used ArcMap 10.1 to plot a 6-m buffer around roads and levees within areas searchable by Challenge participants. We considered snakes captured within this buffer to have been found on levees, and snakes found outside this buffer as occurring in marsh and tree-island habitat. Results Participants removed 68 Burmese Pythons during the 2013 Python Challenge. Seven of the 24 permittees removed 42 (62%) of the snakes and 14 of the 1558 competitors removed 26 (38%) of the snakes (Table 1). Mean total length for pythons captured was 252.1 cm (min = 94.4 cm, max = 434.5 cm, n = 68, SD = 62.1). Thirteen females (19%), 54 males (79%), and 1 YOY python of undetermined sex (1%) were captured. Four (6%) of the pythons were YOY, 6 (9%) were juveniles, and 58 (85%) were adults. No native species were turned in during the Challenge. We examined GI tracts from 66 (97%) of the 68 pythons captured. One of the unsampled pythons was too decomposed to sample, and 1 was part of another study. We found 72 prey items in 64 of the 66 (97%) GI tracts: 36 (50%) were mammalian, 35 (49%) were avian, and 1 (1%) was reptilian (Table 2). We identified 13 prey species— 6 (46%) mammals, 6 (46%) birds, and 1 (8%) American Alligator. Capture locations are shown in Figure 2. No pythons were captured by permittees or competitors in the Holey Land or Rotenberger WMAs. Competitors captured 6 pythons (9%) in Big Cypress WMA and 20 pythons (29%) in Everglades and Francis S. Taylor WMA. Permittees captured 16 pythons (24%) in the Southern Southeastern Naturalist F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 68 Vol. 15, Special Issue 8 Table 1. Summaries of participants, and sizes and sexes of pythons removed during the 2013 Python Challenge®. Pythons were placed into size classes based on total length: young of the year (<150 cm), juvenile males (150–199 cm), juvenile females (150–259 cm), adult males (>199 cm), and adult females (>259 cm). Competitors were members of the general public. Permittees were experienced individuals permitted to remove pythons on state and South Florida Water Management District lands. Size class Pythons captured Sex ratio (male:female) Young of year 4 2:1 (1 unknown) Juvenile 6 2:4 Adult 58 50:8 Total 68 54:13 (1 unknown) Participant type # Successful participants Total participants Pythons captured Permittee 7 24 (2% of total) 42 (62% of total) Competitor 14 1558 (98% of total) 26 (38% of total) Table 2. Prey items discovered in Python molurus bivittatus (Burmese Python) captured during the 2013 Python Challenge®. Number of Class Order Family Species pythons Reptilia Crocodilia Alligatoridae Alligator mississippiensis Daudin 1 (American Alligator) Aves Anseriformes Anatidae Unknown 2 Aves Gruiformes Rallidae Fulica americana Gmelin (American 1 Coot) Aves Gruiformes Rallidae Rallus elegans Audubon (King Rail) 1 Aves Gruiformes Rallidae Unknown 5 Aves Pelecaniformes Ardeidae Botaurus lentiginosus (Rackett) 1 (American Bittern) Aves Pelecaniformes Ardeidae Unknown 2 Aves Pelecaniformes Threskiornithidae Unknown 1 Aves Pelecaniformes Threskiornithidae Eudocimus albus (L.) (White Ibis) 1 Aves Passeriformes Icteridae Sturnella magna (L.) (Eastern 1 Meadowlark) Aves Podicipediformes Podicipedidae Podilymbus podiceps (L.) 10 (Pied-billed Grebe) Aves Unknown Unknown Unknown 10 Mammalia Artiodactyla Cervidae Odocoileus virginianus (Zimmermann) 1 (White-tailed Deer) Mammalia Didelphimorpha Didelphidae Didelphis virginiana Kerr (Virginia 2 Opossum) Mammalia Lagomorpha Leporidae Sylvilagus palustris Bachman (Marsh 4 Rabbit) Mammalia Rodentia Cricetidae Neofiber alleni True (Round-tailed 4 Muskrat) Mammalia Rodentia Muridae Rattus rattus (L.) (Black Rat) 5 Mammalia Rodentia Cricetidae Sigmodon hispidus Say & Ord 10 (Hispid Cotton Rat) Mammalia Unknown Unknown Unknown 10 Southeastern Naturalist 69 F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 Vol. 15, Special Issue 8 Glades WEA, 20 pythons (26%) in the Rocky Glades, 7 pythons (10%) in Everglades and Francis S. Taylor WMA, an 1 (1%) on a road east of and adjacent to the Everglades and Francis S. Taylor WMA.. Participants caught 53 pythons (78%) on levees or roads in an area that comprised 13 km2, or 0.002% of the study area and Figure 2. Location of pythons captured during the 2013 Python Challenge® period compared to kernel-density estimates of all pythons captured from 12 January to 10 February in the years 2008–2012. Southeastern Naturalist F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 70 Vol. 15, Special Issue 8 15 pythons (22%) in marshes or on tree islands in an area that comprised 5435 km2, or 99% of the study area. State and federal employees not associated with the Challenge captured an additional 5 Burmese Pythons during the dates of the Challenge, for a total of 73 pythons removed during the Challenge period. By comparison, 47 Burmese Pythons were removed between 12 January and 10 February 2008, 26 in 2009, 70 in 2010 (historic freeze), 18 in 2011, and 27 in 2012. Individuals permitted to search for pythons on state lands removed 7 pythons in 2010, 5 in 2011, and 11 in 2012, during the period 12 January–10 February of each year. Hunters removed 3 pythons during hunting seasons within a 4-y span, 2009–2012. During the Challenge, permittees conducted 48 surveys to capture 42 pythons. Mean distance traveled per survey was 36.1 km (min = 3.1, max = 115 km, SD = 26.1), and mean number of pythons removed per survey was 0.73 (min = 0, max = 3, SD = 0.77). Permittees removed 0.02 pythons/km during 48 trips. Competitors removed 0.003 pythons/km during 13 successful trips. Permittees removed 0.18 pythons/h during 38 trips (including successful and unsuccessful trips). Competitors removed 0.04 pythons/h during 15 trips. The combined removal rate for competitors and permittees was 0.14 pythons/h. It took an average of 7.32 h to capture a python: 5.42 h for permittees and 26.9 h for general competitors. Two individuals permitted to search for pythons on state lands provided logs of successful and unsuccessful python-removal trips for 2011, 2012, and 2013. For the 12 January to 10 February period, these 2 permittees captured 0.07 pythons/h for 12 trips in 2011, 0.07 pythons/h for 6 trips in 2012, and 0 in 1 trip in 2013. Annual rates of removal by the 2 permittees were 0 in 30 trips in 2010, 0.05 pythons/h in 84 trips in 2011, 0.07 pythons/h in 59 trips in 2012, and 0.10 pythons/h in 12 trips in 2013. The mean annual-capture rates for the route in the Southern Glades WEA were 0.01 pythons/km (range = 0–0.03, SD = 0.02) and 0.11 pythons/h (range = 0–0.26, SD = 0.13). Discussion and Implications More pythons (73) were removed during the Challenge period than during similar time periods during 2008 to 2012; however, a record freeze in 2010 resulted in discovery of more dead pythons (n = 40) than in other years (see Mazzotti et al. 2011 for review of impacts of freeze on pythons). Incentives were correlated with an increase in the number of pythons removed as well as the rate at which pythons were removed (see below for further discussion of CPUE). The 68 pythons removed by Challenge participants likely did not reduce (which was not a goal of the Challenge) or increase (unintended consequence as described by Zipkin et al. 2009) the python population in southern Florida. No native species were turned in to the drop-off locations. However, we could not assess whether any native species were removed and not turned in as part of the competition. Evaluating impacts to non-target species and to natural habitats containing invasive species should be components of any control program (Pasko and Goldberg 2014). None of the prey items identified included those typically fed Southeastern Naturalist 71 F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 Vol. 15, Special Issue 8 to captive pythons such as Rattus norvegicus (Berkenhout) (Norway Rat), which suggested that none of the pythons turned in had been captive animals. All prey items consumed by Challenge pythons removed from the landscape had been reported in previous studies (Dove et al 2011, Snow et al. 2007b). However, Challenge pythons consumed a greater proportion of birds (47%) and smaller proportion of mammals (51%) than was found by previous diet studies. Dove and others (2011) identified birds in 25% of the samples, and Snow and others (2007a, 2007b) found 28% of the prey items were birds, 70% were mammals, and 2% were alligators. We do not know if the increase in proportion of birds consumed by pythons in our study was a one-time occurrence, an artifact of python-removal location, a seasonal variation in diet (i.e., perhaps pythons eat more birds in winter), or represents a shift in diet in response to a decline in mammal populations (Dorcas et al. 2012, McCleery et al. 2015), or other factors. Podilymbus podiceps (Pied-billed Grebe), Sigmodon hispidus (Cotton Rat), and Sylvilagus palustris (Marsh Rabbit) were the most frequently eaten native prey items in this (Table 1) and prior studies (Dove et al. 2011; Snow et al. 2007a, 2007b). Rails and wading birds have also been reported as frequently eaten by Pythons (Dove et al. 2011). The assortment of prey items consumed by Burmese Pythons reflects their association with edges of aquatic habitat s. During the Challenge, most pythons were captured from the same areas where most pythons were removed during 2008–2012 (Fig. 2). Most Challenge pythons were captured by permittees on properties managed by the South Florida Water Management District that were not accessible to competitors (e.g., Rocky Glades SGA, Southern Glades WEA). The higher proportion of pythons captured on these lands may be a result of a combination of relative abundance of pythons; accessibility of locations to participants; detectability of pythons; and observer effort, experience, and skill. Future studies with a larger sample size could evaluate the influence of these factors as co-variates. Water Management District lands accessed by permittees were characterized as aquatic habitats (i.e., marshes and canals) adjacent to terrestrial habitats. The edge between aquatic and terrestrial habitat was often abrupt and created by a levee or road. Levees and roads allowed public access and provided basking sites for pythons, thereby making pythons more detectable at these sites than in other areas. Levees and roads were found to comprise a very small proportion of the area occupied by pythons, and we know that movements and habitat use of Burmese Pythons are not limited to these habitats (Hart et al. 2015, Pitman et al. 2014). Radio-telemetry studies suggest that pythons select habitats with forest cover and without deep flooding (Walters et al. 2016). Interpretation of the CPUE estimates was limited by small sample-size, the tendency of competitors to report data only from successful trips, and a lack of data from most participants. Nonetheless, some patterns of CPUE among participants emerged that could be evaluated during future removal efforts. For example, it appeared that permittees captured pythons more efficiently than general competitors in terms of both time and distance. Permittees had access to Southeastern Naturalist F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 72 Vol. 15, Special Issue 8 areas that competitors did not, knowledge of where pythons had been captured, and experience in capturing pythons. Permittees captured pythons more efficiently during the Challenge than during other reported permittee attempts to find pythons. In these cases, access to and knowledge of pythons were similar. Challenge permittees could have been more skilled than other permittees, could have been surveying for pythons during a good python-detection period (we used year-round data from other permittees), or were incentivized to perform better by competition. We suggest evaluating whether increasing skill level of the public by providing training opportunities that include how to search for and capture pythons could improve their effectiveness at removing pythons. Usefulness of data collected by Challenge participants varied. Better training and closer management of participants, and having all participants collect and report GPS routes of surveys and waypoints of sightings would provide an improved basis for comparing locations of captures, and rate of capture between participant categories, among participants, and in relation to other efforts. Requiring all participants to turn in all records for unsuccessful as well as successful surveys of the same route could allow us to calculate probability of detection of individual pythons. This knowledge would further improve our ability to evaluate and monitor python-control efforts, and would help transform a public-participation effort into a citizen-science effort. Why are some harvest programs able to reduce populations of target species to low levels (e.g., rattlesnake roundups and hunting for crocodilians and sea turtles) while other programs are not effective? Species that are predictable, visible, and vulnerable, (e.g., rattlesnakes while denning or sea turtles and crocodilians while nesting) are susceptible to population reduction through harvest (Pasko and Goldberg 2014). Species that are cryptic and that live in inhospitable or inaccessible habitats can be difficult to locate and remove (Pasko and Goldberg 2014). These same factors limit ability to evaluate effectiveness of incentive programs. Even if we improve our ability to predict where and when pythons occur, effectiveness of control programs will still be limited by the low probability of detection of individual pythons. As invasive species problems intensify, more emphasis is being placed on incentive-based programs to increase their rate of removal and to increase public awareness. Our data suggest that the 2013 Python Challenge led to an increase in the number and rate of pythons removed. We found no evidence of unintended consequences such as removal of native species. However, the potential of incentive programs to impact populations of pythons is uncertain as is the potential for unintended consequences (Pasko and Goldberg 2014). Incentive programs can be potential tools in invasive-species management programs, but they should be managed diligently and evaluated for effectiveness. Acknowledgments This study was funded by the Florida Fish and Wildlife Conservation Commission and supported by the University of Florida. Southeastern Naturalist 73 F.J. Mazzotti, M. Rochford, J. Vinci, B.M. Jeffery, J.K. Eckles, C. Dove, and K.P. Sommers 2016 Vol. 15, Special Issue 8 Literature Cited Clout, M.N., and P.A.Williams. 2009. Invasive Species Management: A Handbook of Principles and Techniques. Oxford University Press, Oxford, UK. 336 pp. Dorcas, M.E., J.D. Wilson, R.N Reed, R.W. Snow, M.R. Rochford, M.A. Miller, W.E. Meshaka Jr., P.T. Andreadis, F.J. Mazzotti, C.M. Romagosa, and K.M. Hart. 2012. 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