Response of an Indicator Species, Dryopteris crassirhizoma, to Temporal and Spatial Variations in Sika Deer Density

Simple Summary

Deer can affect forest ecosystems through foraging behavior. Using indicator species that are sensitive to temporal and spatial variations in deer density has the advantage of managing deer effectively and practically. We examined the response of Dryopteriscrassirhizoma to the variations in sika deer density in Hokkaido, Japan. We showed that the grazing intensity of D. crassirhizoma was sensitive to short-term decreases in deer density and positively related to spatial variation in deer density within regions. Dryopteris crassirhizoma can be a useful indicator species and using grazing intensity could help managers rapidly determine their management direction and decide where to focus their efforts.

Abstract

Identifying appropriate indicator species for the impact of deer on forest vegetation is crucial for forest management in deer habitats and is required to be sensitive to temporal and spatial variations in deer density. Dryopteris crassirhizoma was selected as a new indicator to evaluate the response to these variations. We examined the population-level characteristics, morphological characteristics at the individual level, and grazing intensity of D. crassirhizoma at temporally different deer density sites in Hokkaido, Japan. The response of D. crassirhizoma to spatial variation in deer density was also examined within and between two regions in Hokkaido, Japan. Although the population-level characteristics and morphological characteristics did not significantly respond to short-term decreases in deer density, grazing intensity significantly decreased with decreasing deer density. The grazing intensity was also positively related to the spatial variation of deer density within both regions, but the estimated coefficient of the grazing intensity differed between regions. We concluded that D. crassirhizoma can be a useful indicator species of the impact of deer on forest vegetation. The grazing intensity of the indicator species was sensitive to temporal and spatial variations in deer density within the region.

Modelling the Relative Abundance of Roe Deer (Capreolus capreolus L.) along a Climate and Land-Use Gradient

European roe deer (Capreolus capreolus L.) are important given their economic, recreational and ecological value. However, uncontrolled roe deer numbers can result in negative impacts on forest regeneration and agricultural crops, disease transmission and occurrences of deer-vehicle collisions. Information on the abundance and distribution is needed for effective management. We combined distance sampling (DS) of roe deer dung pellet groups with multiple variables to develop a density surface model (DSM) in the federal state of Bavaria in south-eastern Germany. We used the estimates of pellet group density as a proxy for roe deer relative abundance. We extrapolated our best DSM, conducted a quantitative evaluation and contrasted relative abundance along climate and land-use gradients. Relative abundance of roe deer was influenced by a combination of habitat type, climate and wildlife management variables, which differed between seasons and which reflected changes in food and shelter availability. At the landscape scale, the highest abundance was observed in agriculture-dominated areas and the lowest in urban areas. Higher abundance was also observed in areas with intermediate temperatures compared to the warmest areas. Our results provide information on possible future changes in the distribution of relative abundance due to changes in climate and land-use.

Effectiveness of thermal cameras compared to spotlights for counts of arid zone mammals across a range of ambient temperatures

Effective monitoring of mammal species is critical to their management. Thermal cameras may enable more accurate detection of nocturnal mammals than visual observation with the aid of spotlights. We aimed to measure improvements in detection provided by thermal cameras, and to determine how these improvements depended on ambient temperatures and mammal species. We monitored small to medium sized mammals in central Australia, including small rodents, bettongs, bilbies, European rabbits, and feral cats. We conducted 20 vehicle-based camera transects using both a spotlight and thermal camera under ambient temperatures ranging from 10°C to 35°C. Thermal cameras resulted in more detections of small rodents and medium sized mammals. There was no increased benefit for feral cats, likely due to their prominent eyeshine. We found a strong relationship between increased detections using thermal cameras and environmental temperature: thermal cameras detected 30% more animals than conventional spotlighting at approximately 15°C, but produced few additional detections above 30°C. Spotlighting may be more versatile as it can be used in a greater range of ambient temperatures, but thermal cameras are more accurate than visual surveys at low temperatures, and can be used to benchmark spotlight surveys.

Comparing unmanned aerial systems with conventional methodology for surveying a wild white-tailed deer population

Abstract Context Ungulate populations are subject to fluctuations caused by extrinsic factors and require efficient and frequent surveying to monitor population sizes and demographics. Unmanned aerial systems (UAS) have become increasingly popular for ungulate research; however, little is understood about how this novel technology compares with conventional methodologies for surveying wild populations. Aims We examined the feasibility of using a fixed-wing UAS equipped with a thermal infrared sensor for estimating the population density of wild white-tailed deer (Odocoileus virginianus) at the Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota, USA. We compared UAS density estimates with those derived from faecal pellet-group counts. Methods We conducted UAS thermal survey flights from March to April of 2018 and January to March of 2019. Faecal pellet-group counts were conducted from April to May in 2018 and 2019. We modelled deer counts and detection probabilities and used these results to calculate point estimates and bootstrapped prediction intervals for deer density from UAS and pellet-group count data. We compared results of each survey approach to evaluate the relative efficacy of these two methodologies. Key results Our best-fitting model of certain deer detections derived from our UAS-collected thermal imagery produced deer density estimates (WR20204_IE1.gif, 95% prediction interval = 4.32–17.84 deer km−2) that overlapped with the pellet-group count model when using our mean pellet deposition rate assumption (WR20204_IE2.gif, 95% prediction interval = 4.14–11.29 deer km−2). Estimates from our top UAS model using both certain and potential deer detections resulted in a mean density of 13.77 deer km−2 (95% prediction interval = 6.64–24.35 deer km−2), which was similar to our pellet-group count model that used a lower rate of pellet deposition (WR20204_IE3.gif, 95% prediction interval = 6.46–17.65 deer km−2). The mean point estimates from our top UAS model predicted a range of 136.68–273.81 deer, and abundance point estimates using our pellet-group data ranged from 112.79 to 239.67 deer throughout the CCESR. Conclusions Overall, UAS yielded results similar to pellet-group counts for estimating population densities of wild ungulates; however, UAS surveys were more efficient and could be conducted at multiple times throughout the winter. Implications We demonstrated how UAS could be applied for regularly monitoring changes in population density. We encourage researchers and managers to consider the merits of UAS and how they could be used to enhance the efficiency of wildlife surveys.

Cost-effectiveness of thermal imaging for monitoring a cryptic arboreal mammal

ContextThe development of reliable and cost-efficient survey techniques is key to the monitoring of all wildlife. One group of species that presents particular challenges for monitoring is the arboreal mammals. Traditional techniques for detecting these species often yield low detection probabilities (detectability) and are time-consuming, suggesting the potential for novel methods to enhance our understanding of their distribution, abundance and population trajectories. One technique that has been shown to increase detectability in a range of terrestrial species is thermal imaging, although it has rarely been applied to arboreal species. The true conservation status of Lumholtz’s tree-kangaroo (Dendrolagus lumholtzi) is uncertain because of low detectability under typical survey techniques, and a more suitable method is required to enable effective monitoring of the species, making it an ideal candidate for the present study. AimsWe aimed to compare the success and cost-effectiveness of surveys utilising thermal imaging with two traditional methods, namely, spotlighting and daytime surveys, so as to optimise monitoring of D. lumholtzi. MethodsWe conducted surveys at 10 sites in Queensland (Australia) where D. lumholtzi was known to occur, by using each method, and modelled both the detectability of D. lumholtzi and the cost-effectiveness of each technique. Key resultsDetectability of D. lumholtzi was significantly higher with the use of thermal imaging than it was with the other survey methods, and thermal detection is more cost-effective. In average survey conditions with a trained observer, the single-visit estimated detectability of D. lumholtzi was 0.28 [0.04, 0.79] in a transect through rainforest, by using thermal imaging. Using only spotlights, the detection probability was 0.03 [0, 0.28] under the same conditions. ConclusionsThese results show that incorporating thermal technology into monitoring surveys will greatly increase detection probability for D. lumholtzi, a cryptic arboreal mammal. ImplicationsOur study highlighted the potential utility of thermal detection in monitoring difficult-to-detect species in complex habitats, including species that exist mainly in dense forest canopy.

Too many wild boar? Modelling fertility control and culling to reduce wild boar numbers in isolated populations

Wild boar and feral swine number and range are increasing worldwide in parallel with their impact on biodiversity and human activities. The ecological and economic impact of this species include spread of diseases, vehicle collisions, damage to crops, amenities and infrastructures and reduction in plant and animal abundance and richness. As traditional methods such as culling have not contained the growth and spread of wild boar and feral pigs, alternative methods such as fertility control are now advocated. We used empirical data on two isolated wild boar populations to model and compare the effects of different regimes of culling and fertility control on population trends. We built a Bayesian population model and applied it to explore the implications for population control of various management options combining culling and/or contraception. The results showed that, whilst fertility control on its own was not sufficient to achieve the target reduction in wild boar number, adding fertility control to culling was more effective than culling alone. In particular, using contraceptives on 40% of the population to complement the culling of 60% of the animals, halved the time to achieve our target reduction compared with culling only. We conclude that, assuming the effort of adding fertility control to culling was found to be cost-effective in terms of population reduction, these two methods should be used simultaneously if a rapid decrease in wild boar number is required for a closed population.

Evaluating the use of thermal imaging cameras to monitor the endangered greater bilby at Astrebla Downs National Park

Spotlight surveys are widely used to monitor arid-zone-dwelling species such as the greater bilby (Macrotis lagotis). These surveys require a sufficient sample size to adequately model detection probability. Adequate sample sizes can be difficult to obtain for low-density populations and for species that avoid light and or have poor eyeshine like the bilby. Abundance estimates based on burrow counts can be problematic because of the variable relationship between the number of burrows used and bilby abundance. In 2013, feral predators devastated a Queensland bilby population and a method was required that could locate and monitor the remaining bilbies. We report on a study that compared density estimates derived from spotlighting and thermal cameras. Bilbies were surveyed annually over three years, using spotlights and thermal cameras on different nights but using the same transects to compare the methods. On average, thermal cameras detected twice the number of bilbies per kilometre surveyed than spotlighting. Despite this difference in the number of bilbies detected, density estimates (bilbies km−2) were similar (thermal camera versus spotlight: 0.6 versus 0.2 (2014), 3.4 versus 3.4 (2015) and 4.8 versus 3.3 (2016)). Nevertheless, the larger sample size obtained using thermal cameras gave greater confidence in modelling detection probability.

Improving daytime detection of deer for surveillance and management

Maximising the detection of a target species reduces the uncertainty of survey results and can improve management outcomes. Deer (Cervidae) populations are managed worldwide due to their impacts on anthropocentric interests. In the UK, deer can only lawfully be shot during the daytime, from 1 h before sunrise to 1 h after sunset, when deer activity is at its lowest. We evaluated performance of a thermal imager relative to binoculars for their ability to detect deer during the daytime and at twilight (1 h either side of dawn and dusk). Transect surveys on Thorne Moors, UK, revealed that more roe and red deer were observed using a thermal imager than when using binoculars. More deer in much larger groups were observed at twilight than during the other daylight hours. Variation in animal detectability at different times of the day must be considered during wildlife surveys if their outputs are to be as accurate and precise as possible. The results support the continued focus of deer culling efforts during the hours of twilight. They also highlight the potential utility of thermal imagers for maximising detection probability at twilight.

How many are there? Multiple-covariate distance sampling for monitoring pampas deer in Corrientes, Argentina

Context The pampas deer (Ozotoceros bezoarticus) is an endangered species in Argentina. Scarce information exists about one of the four last populations that survive in Corrientes province, where direct counts estimated a population of <500 individuals. Aims To evaluate the status of the pampas deer population in Corrientes by applying a standardised methodology and to develop methodological recommendations for future deer monitoring. Methods We conducted six population censuses between 2007 and 2010, using line transects placed on roads throughout 1200 km2 of grasslands in the Aguapey region, Corrientes, Argentina. From a moving vehicle, we counted every pampas deer group observed along transects. We used Distance 6.0 and its multiple-covariate distance sampling engine to estimate deer density, while exploring the potential effect of roads, habitat type, hour, season, observer experience and survey effort on deer occurrence and density estimation. Key results The occurrence of pampas deer was irrespective of transect location (minor or major road) but a greater number of animals was detected over transects in minor roads and in areas covered by grasslands with young pine plantations. We estimated a density of 1.17 individuals km–2 (s.e. = 0.52), and habitat type was the most important covariate for density estimation. We estimated a total population of 1495 deer (95% CI = 951–2351, CV = 23.27%) for the Aguapey region in Argentina. Conclusions Corrientes hosts one of the largest populations of pampas deer in Argentina, with ~1000 individuals. The fact that we estimated a larger population than did previous studies could be explained either by actual population growth during the past 10 years, or by the use of more exhaustive and sophisticated sampling design and data analysis. Implications Population surveys using covariate distance sampling on ground line transects can provide more realistic population estimates than do other simpler methods. Our population estimates and methods can be used as a baseline for future monitoring of this population, as long as factors such as sampling effort, type of roads for locating transects, and habitat type are considered in future analysis.

Can Reliable Sage-Grouse Lek Counts Be Obtained Using Aerial Infrared Technology?

More effective methods for counting greater sage-grouse (Centrocercus urophasianus) are needed to better assess population trends through enumeration or location of new leks. We describe an aerial infrared technique for conducting sage-grouse lek counts and compare this method with conventional ground-based lek count methods. During the breeding period in 2010 and 2011, we surveyed leks from fixed-winged aircraft using cryogenically cooled mid-wave infrared cameras and surveyed the same leks on the same day from the ground following a standard lek count protocol. We did not detect significant differences in lek counts between surveying techniques. These findings suggest that using a cryogenically cooled mid-wave infrared camera from an aerial platform to conduct lek surveys is an effective alternative technique to conventional ground-based methods, but further research is needed. We discuss multiple advantages to aerial infrared surveys, including counting in remote areas, representing greater spatial variation, and increasing the number of counted leks per season. Aerial infrared lek counts may be a valuable wildlife management tool that releases time and resources for other conservation efforts. Opportunities exist for wildlife professionals to refine and apply aerial infrared techniques to wildlife monitoring programs because of the increasing reliability and affordability of this technology.

Nocturnal distance sampling of a Mediterranean population of fallow deer is consistent with population projections

Context Precise and accurate methods are essential to assess wildlife populations for sound management. We surveyed a managed population of fallow deer (Dama dama) in a Mediterranean environment in Italy, where this non-native ungulate has a negative impact on biodiversity. Aims We compare nocturnal distance-sampling (deer are detected by thermal imagery at night) population estimates with demographic projections of the same population. Methods We estimated natural survival in fawns (0.86), yearlings (0.83), adult males (0.70) and adult females (0.90) using capture–mark–recapture. By integrating survival estimates with population structure, reproductive traits and harvest data, we performed demographic projections. We performed nocturnal distance sampling on foot by using a thermal imagery once a year (in autumn) from 2001 to 2005. We walked 75–77 km (71 transects) per each survey. Key results We showed that our survey design met distance-sampling assumptions. Distance sampling and demographic projections yielded similar and precise (12.6% < CV <24.1%) population estimates, showing a decreasing (–164.64 deer year–1) population trend from 2755 deer in 2001 to 1877 in 2005. Conclusions We showed that nocturnal distance sampling is useful to monitor wild deer populations in forests effectively and that it represents a cost-effective tool to develop sounded management policy for this non-native species. We also provided, for the first time, a comprehensive stochastic population model for fallow deer in a Mediterranean environment. Using these population estimates, managers could reduce fallow deer population size to a level compatible with the conservation of the endangered Italian roe deer and improve forest regeneration. Implications Nocturnal distance sampling can be used to assess ungulate population living in dense forested habitats effectively and efficiently.

Automatic detection of animals in mowing operations using thermal cameras

During the last decades, high-efficiency farming equipment has been developed in the agricultural sector. This has also included efficiency improvement of moving techniques, which include increased working speeds and widths. Therefore, the risk of wild animals being accidentally injured or killed during routine farming operations has increased dramatically over the years. In particular, the nests of ground nesting bird species like grey partridge (Perdix perdix) or pheasant (Phasianus colchicus) are vulnerable to farming operations in their breeding habitat, whereas in mammals, the natural instinct of e.g., leverets of brown hare (Lepus europaeus) and fawns of roe deer (Capreolus capreolus) to lay low and still in the vegetation to avoid predators increase their risk of being killed or injured in farming operations. Various methods and approaches have been used to reduce wildlife mortality resulting from farming operations. However, since wildlife-friendly farming often results in lower efficiency, attempts have been made to develop automatic systems capable of detecting wild animals in the crop. Here we assessed the suitability of thermal imaging in combination with digital image processing to automatically detect a chicken (Gallus domesticus) and a rabbit (Oryctolagus cuniculus) in a grassland habitat. Throughout the different test scenarios, our study animals were detected with a high precision, although the most dense grass cover reduced the detection rate. We conclude that thermal imaging and digital imaging processing may be an important tool for the improvement of wildlife-friendly farming practices in the future.

Large-scale model-based assessment of deer-vehicle collision risk

Ungulates, in particular the Central European roe deer Capreolus capreolus and the North American white-tailed deer Odocoileus virginianus, are economically and ecologically important. The two species are risk factors for deer–vehicle collisions and as browsers of palatable trees have implications for forest regeneration. However, no large-scale management systems for ungulates have been implemented, mainly because of the high efforts and costs associated with attempts to estimate population sizes of free-living ungulates living in a complex landscape. Attempts to directly estimate population sizes of deer are problematic owing to poor data quality and lack of spatial representation on larger scales. We used data on 74,000 deer–vehicle collisions observed in 2006 and 2009 in Bavaria, Germany, to model the local risk of deer–vehicle collisions and to investigate the relationship between deer–vehicle collisions and both environmental conditions and browsing intensities. An innovative modelling approach for the number of deer–vehicle collisions, which allows nonlinear environment–deer relationships and assessment of spatial heterogeneity, was the basis for estimating the local risk of collisions for specific road types on the scale of Bavarian municipalities. Based on this risk model, we propose a new “deer–vehicle collision index” for deer management. We show that the risk of deer–vehicle collisions is positively correlated to browsing intensity and to harvest numbers. Overall, our results demonstrate that the number of deer–vehicle collisions can be predicted with high precision on the scale of municipalities. In the densely populated and intensively used landscapes of Central Europe and North America, a model-based risk assessment for deer–vehicle collisions provides a cost-efficient instrument for deer management on the landscape scale. The measures derived from our model provide valuable information for planning road protection and defining hunting quota. Open-source software implementing the model can be used to transfer our modelling approach to wildlife–vehicle collisions elsewhere.

Drive counts as a method of estimating ungulate density in forests: mission impossible?

Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of ‘virtual experiments’ in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size.

Mapping wildlife: integrating stakeholder knowledge with modelled patterns of deer abundance by using participatory GIS

Context. Some species that are perceived by certain stakeholders as a valuable resource can also cause ecological or economic damage, leading to contrasting management objectives and subsequent conflict between stakeholder groups. There is increasing recognition that the integration of stakeholder knowledge with formal scientific data can enhance the information available for use in management. This is especially true where scientific understanding is incomplete, as is frequently the case for wide-ranging species, which can be difficult to monitor directly at the landscape scale. Aims. The aim of the research was to incorporate stakeholder knowledge with data derived from formal quantitative models to modify predictions of wildlife distribution and abundance, using wild deer in the UK as an example. Methods. We use selected predictor variables from a deer–vehicle collision model to estimate deer densities at the 10-km square level throughout the East of England. With these predictions as a baseline, we illustrate the use of participatory GIS as a methodological framework for enabling stakeholder participation in the refinement of landscape-scale deer abundance maps. Key results. Stakeholder participation resulted in modifications to modelled abundance patterns for all wild deer species present in the East of England, although the modifications were minor and there was a high degree of consistency among stakeholders in the adjustments made. For muntjac, roe and fallow deer, the majority of stakeholder changes represented an increase in density, suggesting that populations of these species are increasing in the region. Conclusions. Our results show that participatory GIS is a useful technique for enabling stakeholders to contribute to incomplete scientific knowledge, especially where up-to-date species distribution and abundance data are needed to inform wildlife research and management. Implications. The results of the present study will serve as a valuable information base for future research on deer management in the region. The flexibility of the approach makes it applicable to a range of species at different spatial scales and other wildlife conflict issues. These may include the management of invasive species or the conservation of threatened species, where accurate spatial data and enhanced community involvement are necessary in order to facilitate effective management.