SVI is a nearly 5000 ha undeveloped barrier island along Florida’s panhandle coast at the west end of Apalachicola Bay (Fig. 1). The Island comprises about 98% of the Saint Vincent National Wildlife Refuge and is managed to conserve SVI in a natural state. This triangular-shaped Island is about 14.5 km long, 6.5 km wide at its east end and tapers to a point on its west end. SVI exhibits ridge and swale topography with upland habitats and salt- and freshwater wetlands. SVI supports an uncharacteristically high diversity of wildlife species for barrier islands in the region. This diversity includes a variety of federal and state-listed threatened and endangered species such as sea turtles (Caretta caretta, Chelonia mydas, Dermochelys coriacea) and shorebird species (e.g. Haematopus palliates, Charadrius nivosus, Sternula antillarum) that use the Island for nesting. SVI also has served since 1990 as an island propagation site for the red wolf (Canis rufus) recovery programme.

Figure 1.

Location of track plots and camera stations for calculating passive abundance indices to monitor feral swine and sympatric species at Saint Vincent Island in Florida, USA, 2015-2020.

Three large ungulates were present on the Island at the outset of this project: white-tailed deer (Odocoileus virginianus), sambar deer (Cervus unicolor) and feral swine (US FWS 2006). SVI, like all National Wildlife Refuges in the US, has a legal requirement to allow public hunts when compatible with a refuge’s mission. Moreover, SVI had a further requirement to specifically allow hunts for sambar deer. Recreational hunting for the deer species is a key management priority for the refuge, while feral swine were an ancillary species taken by deer hunters. White-tailed deer are native to the Island. Sambar deer, originally imported to SVI in 1908, are a relic from when the Island was used as a private hunting reserve stocked with exotic species (USFWS 2012). Feral swine initially served as a sport hunting species, but ultimately were regarded as a destructive invasive species meriting eradication due to their damage to the Island’s sensitive habitats, state-listed plants and to nesting by sea turtles and ground-nesting seabirds that use SVI’s beaches and a threat to SVI’s archaeological sites spanning millennia as documented elsewhere in Florida (Engeman et al. 2013a; 2017).

Removal of feral swine as part of eradication operations was conducted by government experts in an agreement with USDA Wildlife Services (WS), the Federal agency responsible for managing conflicts with wildlife Only approved and humane methods to euthanise animals conforming to guidelines in the 2013 Report of the American Veterinary Medical Association Panel on Euthanasia (American Veterinary Medical Association 2013) and set forth as agency policy in USDA/APHIS/WS Directive 2.505 were used.

Feral swine were primarily removed by capture in pen traps and sharpshooting. After identification of the most favourable locations to carry out trapping activities, pen traps were constructed and baited with soured corn to condition the feral swine to feeding at trap sites. After feral swine were consistently entering the pen trap to feed, the trap would be set and triggered remotely. During the times when control experts were on the Island to conduct trapping activities, they also were opportunistically removing feral swine by sharpshooting, including a small number of animals removed by sharpshooting from a helicopter. All feral swine were lethally removed by USDA/APHIS/Wildlife Services personnel during the regular course of their official duties. Control personnel were not permanently stationed on the Island, but carried out control activities there according to budget cycles, when their efforts would have maximal impact and demand for their services elsewhere in Florida. The methods for lethal removal in addition to ethical considerations regarding lethal take were fully considered in accordance with the National Environmental Policy Act (USDA 2015). All other components of the study received agency approval by way of the USDA/APHIS/Wildlife Services/National Wildlife Research Center Quality Assurance (QA) procedure: QA1394.

Unfortunately, there were some gaps beyond our control where data collection did not occur. The first occurred for summer 2015 due to financial constraints preventing expenditure on this research. Later in the study, the collection of track plot data was not feasible for spring 2017, which was during an abnormally dry weather period. This made the soft sand substrate unstable for maintaining track details and, therefore, distinguishing tracks amongst species impossible. Our data collection and indexing results for all species and all methods are summarised in Table 1 and Suppl. material 1: tables S2–S5; these tables also indicate gaps where data collection did not occur.

In October 2018, Hurricane Michael, a devastating Category 5 hurricane, struck Florida’s panhandle coast (Byrne 2019) including SVI and the destruction severely affected our ability to collect indexing data (Suppl. material 1: table S1). Data collection using camera traps in autumn 2018 and winter 2019 could not be made when we could not access and sample the Island (data collection opportunities lost). For the same reason, the autumn 2018 track plot sampling was lost. Further, no data were collected for track plots in spring 2019 because damage and debris were still too severe to implement the 100 m plots.

Prior to Category 5 Hurricane Michael reaching the Florida panhandle, a series of hypotheses were formulated around impacts on vertebrate populations. These considerations were based on how directly the hurricane would hit and the magnitude and timing of high tide and, therefore, whether a storm surge might over-wash the entire Island (possibly as deep as 2 m). We expected that a substantial over-wash might cause an acute reduction in numbers of some animals, especially armadillos. It also could hasten the elimination of an already-reduced feral swine population. The deer and racoons were expected to mostly survive the storm. Besides the acute threat to animal numbers from a potential island over-wash, the environmental destruction from such a powerful storm could have longer term population impacts through such impacts as destruction of food sources. Depending on island access and destruction levels post-hurricane, our population indexing methods were well-suited for assessing hurricane impacts to SVI’s wildlife populations.

Between January 2015 and October 2019, WS experts invested a total of 559 person-days to remove a total of 417 feral swine. During that same time, 21 feral swine were removed by hunters during 15 refuge hunts (3863 hunter-days), for a combined total of 438 feral swine removed by both methods. As is often the case (see Engeman et al. (2019a, b)), the last few feral swine individuals to be removed in this eradication effort involved the greatest effort/time per individual (see Expert Take and Recreational Hunter Take in Suppl. material 1: table S2; Fig. 3). During the first year of eradication efforts, expert take was 3.692, 1.960, 0.771 and 1.061 swine per person-day of effort for winter, spring, summer and autumn, respectively (Fig. 3). By 2019 when the eradication was completed, the expert take per person-day of effort had dwindled to 0.207, 0.068, 0.037 and 0.000 swine per person-day of effort (Fig. 3). It took 77 person-days to remove the last three animals.

Figure 3.

Feral swine take by federal experts in relation to person/hunt days (take per hunt day) on Saint Vincent Island, Florida, USA, 2015-2019.

Similarly, for recreational hunter muzzleloader season, take per hunter day numbers decreased over years from a high in 2015 at 0.016 swine per hunter day to 0.008, 0.000, 0.006 and 0.000 for 2016, 2017, 2018 and 2020, respectively (there was no muzzleloader hunting season in winter 2019 due to hurricane damage). The archery season followed the same pattern with 0.026 swine taken per hunter day in 2015, then declining to 0.021, 0.000 and 0.000 for years 2016, 2017 and 2019, respectively (no archery season was held in late autumn 2018 due to hurricane damage). Lastly, the number of feral swine taken per hunter day during the sambar deer season was never high and quickly dropped to zero with the takes per hunter day in 2015, 2016, 2017, 2020 of 0.006, 0.008, 0.000 and 0.000, respectively (no sambar season was held in autumn 2018 due to hurricane damage).

While both track plot and camera indices followed the feral swine population decline, another primary question to consider is which index method is most sensitive to the presence of low numbers of animals. A higher percentage of stations detecting the target species for a monitoring method reduces the number of such stations that would be needed to monitor the population, especially important if resources, logistics or labour are at a premium. Although the indices based on track plot and camera data each well-documented the decline and removal of the feral swine population, it is not surprising that a higher percentage of track plots detected feral swine than camera stations on the first day during an observation session, because track plots were 100 m in length, whereas camera views were about a tenth of that (Table 1). The difference in station size between track plots and cameras would be expected to be mitigated somewhat by field logistics where cameras were operating for a greater number of days (5 days) without effort, while the labour involved in reading and smoothing tracking plots placed practical limits on the number of days (3 days) track plots were maintained in the field.

Looking at the percentage of track plots versus the percentage of camera stations that recorded feral swine on the first day shows a much greater likelihood at each common observation session that the track plots would detect feral swine (Table 1). At least 1.5 to 6 times as many stations recorded feral swine activity on the first day of observations (noting that, at the autumn 2017 session, 42.9% of track plots recorded feral swine, but no cameras recorded swine on the first day). One could argue that this is due strictly to the track plots being 100 m in length, whereas a camera station’s view would be a fraction of that (~ 10%). However, the cameras were recording observations for 5 days, while the track plots were only recording for 3 (except for the autumn 2019 session which only had 2 days of observations). Having a 67% longer observation period for cameras brought the detection percentages over an entire observation session for cameras closer to that for track plots through the spring 2016 sampling sessions, (within 70–91% of track plot detection rates, Table 1). However, after the feral swine population had been decreased substantially, the proportion of track plots recording swine during 3 days of sampling was ~ 2–3 times higher than for 5 days of camera observations (see the results in Table 1 from autumn 2016 through spring 2018). Thus, having more camera observation days at each session did not make up for the greater size of the track plots.

The levels of agreement amongst the primary indexing methods for feral swine are reflected in the pairwise correlation coefficients amongst the methods (the results for the take results from the three hunting seasons are not considered since feral swine were not the target species). All indexing approaches indicated the declining feral swine populations. Of particular interest, the track plot index was reasonably well-correlated with the camera index with r = 0.73 (n = 7; Fig. 5). Similarly, the index, based on expert take, was also reasonably well-correlated with the track plot index, with r = 0.78 (n = 7; Fig. 5). However, the camera index and the index based on expert take were highly correlated at r = 0.94 (n = 17; Fig. 5). To explain the implications from these correlation results, we again must consider the relative sensitivities of the methods. A high correlation between two of the three methods does not imply that either of those methods is of higher quality than the third method. For expert take, there is not an analogous measure for detection on the first day of observation, nor an analogue for the percent of stations detecting feral swine during an observation session. This is due to multiple control methods being used simultaneously with those methods applied at differing and changing locations through the course of a control session. While the sensitivity of cameras compared to track plots was apparently limited by its smaller area sampled at each station, the magnitude of control by experts would also be expected to be limited by the manpower and trapping resources available, resulting in lower potential for large take numbers. These limitations for breadth of camera data and take by experts undoubtedly contributed to correlations with track plot indices not being higher.

Figure 5.

Heat-map of Pearson’s correlation coefficients for feral swine indexing methodologies, Saint Vincent Island, Florida, USA, 2015-2019.

While white-tailed deer were not the subject of population removal, they appeared to exhibit an overall population decline over the course of the study (Suppl. material 1: table S2). The correlation between track plot and camera indices for white-tailed deer was nearly the same as for feral swine, with r = 0.72 (n = 7). As the camera data were obtained quarterly, some interesting patterns emerged that could not be detected with the track plot data obtained twice per year. Going from each winter to spring assessment, the camera index showed a similar drop through the course of the study. A further, but smaller drop was then observed going from each spring to summer. Yet, by the ensuing winter, the index values returned to a similar level as seen in the previous winter. These patterns hold true throughout the course of our study, although with diminished numbers after Hurricane Michael (see Wildlife impacts from Hurricane Michael Subsection). As a result of data not being obtained for track plots due to Hurricane Michael for autumn 2018 and spring 2019, coupled with tracking observations not being feasible in spring 2017, similar seasonal patterns could not be elucidated from the track index values, but the index values within the available seasons appear supportive of the camera results indicating a stable population.

As observed for feral swine, track plots were much more likely than camera stations to record white-tailed deer on the first day of observation (Suppl. material 1: table S2). Similar to the feral swine results, the percentage of stations recording white-tailed deer over the three observation days for track plots exceeded the percentage of stations recording white-tailed deer over the five observation days for cameras (Suppl. material 1: table S2).

Unlike for white-tailed deer, neither the camera index nor the track plot index revealed a consistent annual pattern in sambar deer abundance across years (Suppl. material 1: table S3). The two indices were moderately correlated, with r = 0.62 (n = 7) and both seemed to indicate a relatively stable population prior to Hurricane Michael (see Wildlife impacts from Hurricane Michael Subsection) or more on the hurricane’s effects). As with the other ungulate species, the percent of track plots recording sambar deer on the first day and the percentage of track plots recording sambar deer during an observation session were each higher than for the camera stations, although the discrepancies for this species tended to be less than for white-tailed deer and especially feral swine.

Track plot and camera indices for raccoons did not correlate well (r = -0.15, n = 7) and only to a limited degree for armadillos (r = 0.47, n = 7). The lack of correlation is easily understood when examining the proportion of stations recording either species on the first day or for the duration of the sampling event (Suppl. material 1: tables S4, S5). Track stations were much more likely to record presence of both raccoons and armadillos (Suppl. material 1: tables S4, S5). First day detections across track stations for armadillos ranged from 28.6% to 71.4%, whereas only 0% to 6.9% of camera stations recorded armadillo on the first day, with 0% observing the most common result over all observation sessions. Detections of armadillos across track stations over the course of an observation session ranged from 66.7% to 100% of stations, whereas the percentage of camera stations recording armadillo ranged from 3.4% to 26.7%.

During Category 5 Hurricane Michael, the Island experienced a storm surge, but only portions of the Island were over-washed. Swine survival following the hurricane was readily documented through animal signs and ultimately in the take by experts in spring 2019.

Both species of deer were documented by track plots and cameras to have survived the hurricane, as expected. Nevertheless, the white-tailed deer track plot index (Suppl. material 1: table S2) after the hurricane in autumn 2019 was only roughly half what it was in the previous autumn assessments in 2015, 2016 and 2017 with an observed 50.27% decrease when comparing pre-hurricane autumn seasons (2015–2017) and post-hurricane autumn seasons (2019; Fig. 6). The spring and summer 2019 camera index values for white-tailed deer were very close to the spring and summer values prior to the hurricane. However, the autumn 2019 and winter 2020 white-tailed deer camera index values were much smaller than the autumn and winter values before the hurricane, which also was reflected by the autumn 2019 track plot index. A 64.06% decrease was observed when comparing pre-hurricane (2015–2017) and post-hurricane autumn season camera index values (2019; Fig. 6).

Figure 6.

Percentage change in camera and track plot index values between pre- and post-hurricane sampling periods. Camera and track plot index values were compared between pre- (2015-2017) and post-hurricane autumn seasons (2019). No data were collected in autumn 2018.

Sambar deer track plot and camera index values followed patterns pre- and post-hurricane (Suppl. material 1: table S3) similar to the patterns for white-tailed deer. The autumn 2019 track plot index after the hurricane was only a fraction (61.03% decrease) of the autumn index values in preceding years, as was the case for white-tailed deer (Fig. 6). The spring 2019 camera index for sambar deer was similar to spring results in preceding years. The summer 2019 camera index was lower than all previous summer results, except for 2018 when an exceptionally low index resulted. As for white-tailed deer, the autumn 2019 and winter 2020 camera index values were much less than all previous index values from 2015–2018. A 75.70% decrease was observed when comparing pre-hurricane and post-hurricane autumn season camera index values (2019; Fig. 6).

Raccoons did not appear to have their abundance strongly affected by the hurricane (Suppl. material 1: table S4). When comparing autumn seasons pre- and post-hurricane, a 73.08% decrease in raccoon camera index values was observed, whereas a 16.28% increase in raccoon track plot index values was observed (Fig. 6). Armadillos also did not appear to have their abundance severely diminished by the hurricane (Suppl. material 1: table S5). The opposite pattern was observed when comparing autumn seasons pre- and post-hurricane for armadillos; there was a 41.67% increase in camera index and a 12.59% decrease in track plot index values (Fig. 6). The track plot index values for both species after the hurricane were similar to their values before the hurricane.

Control by experts targets all population demographics while typically being cost-effective (Engeman et al. 2003, 2004, 2007b, 2010), yet it requires consistent application to be effective. For an insular population where immigration is a negligible or even a non-existent threat, consistent effective control by experts can eventually lead to eradication. In the case of SVI, control by experts through numerous (typically) week-long operational visits each year resulted in the final swine being eliminated from this 5000-ha Island in just under 5 years, primarily by trapping, but also sharpshooting (including four occasions from a helicopter).

In most places where feral swine are found, re-immigration after control operations is a concern. Effective control has substantially reduced feral swine populations on mainland Florida in a variety of places, including across even very large areas (e.g. Engeman et al. (2007a, 2013b)). Nevertheless, feral swine are ubiquitous in Florida, making re-immigration into an area a persistent threat. Re-immigration, coupled with feral swine possessing the greatest reproductive potential of any large mammal in North America (Wood and Barrett 1979; Hellgren 1999), means the beneficial effects of feral swine population reduction can be quickly undone. Thus, in areas where re-immigration can occur, control efforts must be re-administered consistently over time to maintain the beneficial effects of the initial control efforts.

In general, recreational hunting can inflict a source of mortality on a population of feral swine, but hunters generally do not equally target all population segments, thereby potentially limiting the severity of population reduction (Festa-Bianchet 2007; Braga et al. 2010; Plhal et al. 2011; Keuling et al. 2013). Moreover, recreational hunters on SVI were focused on the two deer species, with feral swine being an ancillary target. This further diminished the potential for recreational hunting to serve as a significant factor for eradicating the species from the Island. Luckily, SVI is surrounded by a barrier of water, although the distance to the mainland does not make it impossible for feral swine to cross, especially at low tide and especially in winter. Yet, it is sufficient to make such crossings unlikely and infrequent without human assistance.

The importance of making comparisons over years from the same timeframe within years was highlighted by our results. Many, if not most, animal species go through seasonal changes in activities. This would influence the animal intercept rate at indexing observation stations, thereby influencing observation rates for calculating population abundance estimates. Be it observations or captures, the probability of intercepting an animal at any given location is influenced both by that species’ abundance and by its activity level. Activity levels vary through the year according to various lifecycle factors including mating, rearing of young and dispersal of young. For example, the increased camera index values for white-tailed deer in the winter each year likely reflects increased activity during the rut, which would increase the number of white-tailed deer interceptions at observation stations. Importantly and as professed in various seminal papers on indexing (e.g. Engeman (2005)), this demonstrates the necessity of comparing index values from the same timeframe each year when looking at trends over years. Otherwise, indexing results between years would be confounded with seasonal results within years. Use of a valid sampling design that incorporated observations seasonally each year also resulted in our indexing data appearing to indicate that white-tailed deer harvest by hunting was in a stable balance with population recruitment, as the within-season indexing results did not have substantial changes across years. While most species likely show seasonal fluctuations in activity, it is not always the case. For example, no consistent within-year pattern in sambar deer abundance was revealed across years by either the camera index or the track plot index (see Sanbar deer population monitoring and sensitivity of indices Subsection). This is probably because sambar deer do not have a distinct rutting season and males can be in rut nearly any time of year. Yet, seasonal sampling each year would still be crucial for showing if there was an unexpected impact on the population from a hunting season or other event.

Thus, to avoid confounding between differences amongst years with differences amongst seasons, comparisons amongst years are only valid when examining the same season across years. Such seasonal activity changes and the impact on abundance measures were clearly borne out in our camera indexing results for white-tailed deer. Making appropriate seasonal comparisons is a well-known design facet for long-term abundance monitoring (Engeman 2005). However, it is not always well-applied, as with the studies described by Allen et al. (2011) that led to invalid inferences and questionable policy recommendations. Even for species where patterns are not as easily discernible as those for white-tailed deer, it is still essential to make comparisons from the same within-year timeframes to avert potential confounding in inferences across years.

To monitor the decline and confirm the eradication of the feral swine population on SVI, we considered: (1) two station-based indices, (2) a track plot index and a camera index, (3) take indices by control experts and (4) take indices by recreational hunters during three types of annual recreational hunting seasons. As indicated already, the recreational hunters on SVI were there to target white-tailed deer or sambar deer, with feral swine only taken opportunistically as an ancillary species, as reflected in only low numbers of hunter take for feral swine (only 21 over the course of this study). Thus, for indexing purposes in situations similar to SVI, recreational hunt indices where feral swine are not the primary target should not be used as the population index for feral swine or to provide information for feral swine management. Unlike hunter take, control by WS aimed to target all population demographics. Even when simultaneously integrating multiple control methods with effort for each unfeasible to define, an adequate index can be formed using take per person-day as the take measure. We saw this in our results and it was previously applied successfully by Avery et al. (2014). Control in general and especially for eradication, very typically targets a particular species, making the simultaneous acquisition of indexing measures on multiple species highly unlikely.

Both track plots and cameras provided data simultaneously for multiple sympatric species (five species) from which abundance indices could be calculated. This study raised a variety of factors to consider if a choice had to be made between these two methods. When considering comparisons or trade-offs between track plots or trail cameras as tools for collecting monitoring data, we must realise that the track plot and camera methods we applied represent one of many possible configurations for each. Cameras, in particular, have myriad settings that can be employed to meet the field circumstances and in-office examination/sorting of photos. Similarly, track plots have been deployed in a wide range of sizes for many species. For example, besides the 100 m length we used in the present study, successful monitoring of feral swine and sympatric species in Florida has been conducted using track plots 3 m in length (Engeman et al. 2003), 0.8 km in length (Engeman et al. 2013a) and 1.6 km in length (Engeman et al. 2007b).

That being said, our results offer considerable insight into the performance of the two substantially different station-based observation methods. First, when we only considered what the amount of data collected would have been if the stations were only set out on one day, we found that a much higher percentage of track plots than cameras recorded the presence of an animal. The size of each track plot was much greater than the area viewed by a camera, which led us to consider the percentage of track plots and the percentage of cameras that recorded the presence of each species during their respective deployment of 5 days for cameras and only 3 days for track plots. Still, the track plots had a higher percentage for recording presence of each species than cameras. The discrepancy in recording presence of a species was much greater when looking at the data for the two species of smaller stature: raccoons and armadillos. Our primary focus was to monitor the eradication of feral swine, along with the concurrent interest to monitor the populations of the two important sport hunting species: white-tailed and sambar deer. The positioning of cameras for recording the high priority ungulates might have resulted in decreased detection of the smaller species. As camera height at the observation stations was optimised for feral swine, the species of lesser physical stature (raccoons and armadillos) may have been too low to the ground to trigger the cameras in much of the area of camera view where a larger animal would trigger the camera. In contrast, the sandy substrate made the track plots very sensitive to the deposition of spoor by all species, making the presence of all species that entered the plot detectable as long as tracks could be identified. Thus, it was not a surprise that a method (cameras) that had difficulty recording the presence of these species did not correlate well with a method that is much more sensitive to their presence (track plots).

Considering that if track plot dimensions were similar to the areas within camera views, similar sensitivities for recording animals might have resulted. If the number of camera stations were doubled or tripled to better match the areas surveyed with track plots, the individual probability that a particular camera could detect a target animal would remain the same, but the probability that a target animal could be detected by the full accumulation of cameras would increase. However, the trade-off for such an increase in cameras (especially for an entity with a limited budget) might be fiscally impractical. In contrast, as we can see from the gaps in our data and the reasons for those gaps, track plots are more vulnerable to environmental conditions and vehicle or foot traffic (e.g. by livestock or humans) destroying data or preventing data collection than cameras. Cameras, on the other hand, especially in areas with fewer limits on public access, can be highly vulnerable to vandalism or theft.

Labour and cost are amongst other determining factors when considering an indexing method to apply. For track plots, the size of the plot, the soil substrate and the means for smoothing the plot surface determine the amount of labour and cost. Short plots (e.g. 3 m in length) would typically be prepared and smoothed by hand using a rake or broom. Longer plots, such as the 100 m plots used in this study, would be prepared and smoothed by mechanical means, such as a pickup truck or all-terrain vehicle with an attached or towed device. This may or may not be a significant cost. For example, the 1.6 km track plots used by Engeman et al. (2007b) were made simply by dragging a section of weighted chain link fencing behind a pickup truck along well-maintained fire lines. Thus, no extra cost or labour was incurred from observing the plots. Track plots do require an observer to “read” and resurface the plots (erase tracks) each day. Camera stations, on the other hand, require less preparation and maintenance. When using permanent station locations as we did, occasional vegetation maintenance may be required to maintain consistency in the field of view across sampling events. Beyond that, however, cameras only need to be set out at their stations at the start of the observation session and then retrieved at the end. The ease in the field with which cameras can collect data (while recognising the concomitant strain in lab or office of managing the photos) can provide continuous data collection for extended periods of time, offering more detailed assessments of seasonal effects. Monitoring with cameras may require a considerable upfront cost to purchase the cameras if they are not already on hand. Ideally, all cameras would be the same make and model, but at the minimum, they should all be capable of having the identical settings for time lags between photos, burst ability and burst size etc. In addition, unlike for track plot data, the resource intensive portion for camera data occurs in the office or lab where potentially thousands of photos must be sorted through to create the indexing dataset.

Whether an index can detect known or expected changes in a population and whether multiple methods show agreement on population changes are essential components to index validation. Fully enumerated, known populations virtually never exist in nature, but would be ideal for testing and validating relative abundance indices (Engeman 2005; Allen and Engeman 2015). In the absence of known wild populations that a method can be tested on, guidance has been developed for evaluating and validating the use of an index for monitoring population abundance (Allen and Engeman 2015). Multiple different monitoring tools simultaneously applied provide the greatest assurances that population trends can be detected (Allen and Engeman 2015). Moreover, validation methods for indexing procedures include evaluating concurrence amongst different methods when assessing abundance amongst changing populations and whether those results line up with expected changes in abundance.

Agreement amongst multiple different monitoring methods provides strong assurance that the observed population trend is true. We observed that track plots, camera stations and take by experts obtained observations well-suited for calculating indices that documented the demise of the feral swine population on SVI with varying levels of sensitivity. This was a crucial first step to evaluating the efficacy of the indexing procedures.

Another key to validating monitoring procedures is whether they can detect known or strongly expected changes in a population. Concomitantly, that the monitoring methods tracked the decrease in the feral swine population as it was being removed was one means of addressing this second validation point. Besides the diminishing population of feral swine, the impacts of Hurricane Michael provided further validation opportunity to assess indexing procedures relative to expected population changes.

The possibility of a severe hurricane causing an island to be over-washed with its storm surge would naturally be expected to negatively impact wildlife populations on the island. On SVI, the storm surge did not completely inundate the Island, yet it apparently impacted some of the wildlife populations. Its effect on feral swine was indeterminate because the population was already very low through control efforts and the monitoring efforts that could have possibly detected the storm’s immediate impacts could not be applied immediately prior to the storm or immediately afterwards. By the time the camera stations were able to be re-implemented after the hurricane and the track plots later after that, the final feral swine had been removed. Although at the time that the final animal was eliminated, it was not certain that it was the final animal. Yet, we know feral swine survived the storm because the eradication was completed after the storm. Even with a complete over-wash, a proportion of the terrestrial species can survive a storm surge. For example, during the eradication effort for Gambian giant pouched rats on Grassy Key, Florida, the storm surge from Hurricane Wilma over-washed much of the island with over a 1 m of water. While there was hope that the population might have been eliminated, the monitoring methods in place quickly showed survival of the population (Engeman et al. 2007b). Track plot index values and camera index values both indicated concurrence that Hurricane Michael had an impact on the populations of both species of deer, thus, satisfying the above two validation points of concurrence amongst different methods when assessing abundance amongst changing populations and whether those results line up with expected changes in abundance. Moreover, both the track plot index and the camera index showed concurrence in an ultimate decline by autumn 2019 in white-tailed and sambar deer populations. Our results suggest that a portion of the white-tailed deer population may not have been directly eliminated by the hurricane, but rather succumbed due to lasting effects on their health or environmental resources necessary for survival. Similarly, sambar deer also appeared to, at least initially, survive the hurricane, but subsequently showed a population reduction perhaps due to health or environmental resource deficiencies. Thus, while cameras did not reveal an immediate decline after the hurricane, an eventual decline was, nevertheless, a reasonable expectation due to the landscape destruction from a Category 5 storm, with both indices fulfilling that expectation.

The authors have declared that no competing interests exist.

No ethical statement was reported.

This research was supported by the U.S. Department of Agriculture, Wildlife Services, National Wildlife Research Center, the USFWS and U.S. Department of Agriculture, Wildlife Services, Florida Operations Program.

All authors have contributed equally.

Eric A. Tillman https://orcid.org/0000-0001-8222-0549

Betsy A. Evans https://orcid.org/0000-0001-8492-016X

Bradley S. Smith https://orcid.org/0000-0001-5192-1123

Bryan M. Kluever https://orcid.org/0000-0001-8417-4339

All of the data that support the findings of this study are available in the main text or Supplementary Information.