Application of Least-Cost Movement Modeling in Planning Wildlife Mitigation Measures along Transport Corridors: Case Study of Forests and Moose in Lithuania

The effects of habitat fragmentation on the genetic structure of wild boar (Sus scrofa) population in Lithuania

BACKGROUND

Wild boar (Sus scrofa) is a widely distributed ungulate whose success can be attributed to a variety of ecological features. The genetic variation and population structure of Lithuania's wild boar population have not yet been thoroughly studied. The purposes of this study were to investigate the genetic diversity of S. scrofa and assess the effects of habitat fragmentation on the population structure of wild boar in Lithuania. A total of 96 S. scrofa individuals collected from different regions of Lithuania were genotyped using fifteen microsatellite loci.

RESULTS

The microsatellite analysis of the wild boars indicated high levels of genetic diversity within the population. Microsatellite markers showed evidence of a single panmictic wild boar population in Lithuania according to STRUCTURE's highest average likelihood, which was K = 1. This was supported by pairwise Fst values and AMOVA, which indicated no differentiation between the four sampling areas. The results of the Mantel test revealed a weak isolation by distance and geographic diversity gradients that persisted between locations. Motorway fencing and heavy traffic were not an effective barrier to wild boar movement.

CONCLUSIONS

There was limited evidence of population genetic structure among the wild boar, supporting the presence of a single population across the study area and indicating that there may be no barriers hindering wild boar dispersal across the landscape. The widespread wild boar population in Lithuania, the high level of genetic variation observed within subpopulations, and the low level of variation identified between subpopulations suggest migration and gene flow between locations. The results of this study should provide valuable information in future for understanding and comparing the detailed structure of wild boar population in Lithuania following the outbreak of African swine fever.

Mitigating the Impacts of Development Corridors on Biodiversity: A Global Review

Development corridors are extensive, often transnational and linear, geographical areas targeted for investment to help achieve sustainable development. They often comprise the creation of hard infrastructure (i.e., physical structures) and soft infrastructure (i.e., policies, plans, and programmes) involving a variety of actors. They are globally widespread, and likely to be a significant driver of habitat loss. Here, we describe the development corridors phenomenon from a biodiversity perspective and identify the elements of best practice in biodiversity impact mitigation. We use these to carry out a review of the peer reviewed literature on corridors to respond to three questions: (i) how impacts on biodiversity and ecosystem services are assessed; (ii) what mitigation measures are discussed to manage these impacts; and (iii) to what extent do these measures approximate to best practice. We found that of 271 publications on development corridors across all continents (except for Antarctica) mentioning biodiversity or ecosystem services, only 100 (37%) assessed impacts on biodiversity and 7 (3%) on ecosystem services. Importantly, only half of these (52, 19% of the total 271 articles) discussed mitigation measures to manage these impacts. These measures focused on avoidance and minimisation and there was scant mention of restoration or ecological compensation illustrating a deficient application of the mitigation hierarchy. We conclude that the academic literature on corridors does not give sufficient consideration to comprehensive mitigation of biodiversity impacts. To change this, impact assessment research needs to acknowledge the complexity of such multi-project and multi-stakeholder initiatives, quantify biodiversity losses due to the full suite of their potential direct, indirect and cumulative impacts, and follow all the steps of the mitigation hierarchy impact framework. We suggest a series of research avenues and policy recommendations to improve impact assessments of corridors towards achieving better biodiversity outcomes.

Current Zoology Reconstructing landscapes of ungulate parturition and predation using vegetation phenology

Enhanced vegetation index (EVI) data can be used to identify and define the space in which ungulates practice parturition and encounter predation. This study explores the use of EVI data to identify landscapes linked to ungulate parturition and predation events across space, time, and environmental conditions. As a case study, we used the moose population (Alces alces) of northern Minnesota in the USA. Using remotely sensed EVI data rasters and global positioning system collar data, we quantified how vegetation phenology and moose movement shaped the births and predation of 52 moose calves from 2013 to 2020 on or adjacent to the Grand Portage Indian Reservation. The known sources of predation were American black bears (Ursus americanus, n = 22) and gray wolves (Canis lupus, n = 28). Satellite-derived data summarizing seasonal landscape features at the local level revealed that landscape heterogeneity use by moose can help to quantitatively identify landscapes of parturition and predation in space and time across large areas. Vegetation phenology proved to be differentiable between adult moose ranges, sites of cow parturition, and sites of calf predation. Landscape characteristics of each moose group were consistent and tractable based on environment, suggesting that sites of parturition and predation of moose are predictable in space and time. It is possible that moose selected specific landscapes for parturition despite risk of increased predation of their calves, which could be an example of an "ecological trap." This analytical framework can be employed to identify areas for future ungulate research on the impacts of landscape on parturition and predation dynamics.

Roadkill-Data-Based Identification and Ranking of Mammal Habitats

Wildlife–vehicle collisions, as well as environmental factors that affect collisions and mitigation measures, are usually modelled and analysed in the vicinity of or within roads, while habitat attractiveness to wildlife along with risk to drivers remain mostly underestimated. The main goal of this study was the identification, characterisation, and ranking of mammalian habitats in Lithuania in relation to 2002–2017 roadkill data. We identified habitat patches as areas (varying from 1 to 1488 square kilometres) isolated by neighbouring roads characterised by at least one wildlife–vehicle collision hotspot. We ranked all identified habitats on the basis of land cover, the presence of an ecological corridor, a mammalian pathway, and roadkill hotspot data. A ranking scenario describing both habitat attractiveness to wildlife and the risk to drivers was defined and applied. Ranks for each habitat were calculated using multiple criteria spatial decision support techniques. Multiple regression analyses were used to identify the relationship between habitat ranks, species richness, and land cover classes. Strong relationships were identified and are discussed between the habitat patch ranks in five (out of 28) land cover classes and in eight (out of 28) species (97% of all mammal road kills). We conclude that, along with conventional roadkill hotspot identification, roadkill-based habitat identification and characterisation as well as species richness analysis should be used in road safety infrastructure planning.

Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns

The number of road traffic accidents decreased in Lithuania from 2002 to 2017, while the ungulate–vehicle collision (UVC) number increased and accounted for approximately 69% of all wildlife–vehicle collisions (WVC) in the country. Understanding the relationship between UVCs, traffic intensity, and implemented mitigation measures is important for the assessment of UVC mitigation measure efficiency. We assessed the effect of annual average daily traffic (AADT) and wildlife fencing on UVCs using regression analysis of changes in annual UVCs and UVC hotspots on different categories of roads. At the highest rates, annual UVC numbers and UVC hotspots increased on lower category (national and regional) roads, forming a denser network. Lower rates of UVC increase occurred on higher category (main) roads, forming sparser road networks and characterized by the highest AADT. Before 2011, both UVC occurrence and fenced road sections were most common on higher-category roads. However, as of 2011, the majority of UVCs occurred on lower-category roads where AADT and fencing had no impact on UVCs. We conclude that wildlife fencing on roads characterized by higher speed and traffic intensity may decrease UVC numbers and at the same time shifting UVC occurrence towards roads characterized by lower speed and traffic intensity. Wildlife fencing re-allocates wildlife movement pathways toward roads with insufficient or no mitigation measures.

The effect of wildlife carcass underreporting on KDE+ hotspots identification and importance

Many approaches have been developed in order to mitigate wildlife-vehicle collisions (WVC), their causes and consequences. Reliable data on the amount and location of killed animals along roads are therefore necessary. The existing WVC databases are usually, however, far from complete. This data underreporting causes problems when identifying the riskiest places along a transportation infrastructure. WVC data underreporting can distort the results of WVC hotspots determination. In this work, we simulated WVC hotspots identification and stability under various rates of WVC data underreporting. Our aim was to investigate whether WVC hotspots can be found at the original locations even when data are strongly underreported. We applied the KDE + method for WVC hotspots identification. The KDE + method also allows for hotspots ranking according to cluster strength and collective risk. These two measures were then used for detection of diminishing hotspot signals with a rising level of underreporting. We found that WVC hotspots with a greater cluster strength suffered less from underreporting whereas hotspots will lower values of both cluster strength and collective risk were not detected when underreporting in the data increased. Hotspots with a cluster strength above 0.5 were almost always detected when data underreporting remained below 50%. More than 50% of these hotspots (with cluster strength above 0.5) were detectable even when underreporting rate was between 50 and 80%. We further studied the effects of both spatial and temporal underreporting. Whereas temporal change of underreporting was not a problem in hotspots detection, spatial underreporting introduced significant errors producing both false positive and false negative results (hotspots). We conclude that both researchers and practitioners should be aware of the phenomenon of underreporting and should also try to maintain the same sampling effort of spatial reporting.

Temporal patterns of ungulate-vehicle collisions in Lithuania

Wildlife mortality caused by vehicles is a serious conservation and economic problem as collisions with large mammals are global, pervasive and increasing. We analysed 14,989 reports of ungulate-vehicle collisions (UVC) that occurred in Lithuania from 2002 to 2017. We analysed UVC data for four major ungulate species (roe deer, red deer, moose and wild boar) and checked for potential seasonal or daily trends. The temporal distribution of collisions was species-dependent. UVC analysis showed strong monthly and hourly pattern. Most occurrences took place before or during sunrise (dawn) and after or during sunset (dusk) during the year. In spring, the highest UVC peaks occurred early in the morning and late in the evening, while in winter these peaks occurred in late mornings and early evenings. With most UVC occurring on Fridays, daily variations were weak. We conclude that temporal variations of UVC distributions are result of a complex interaction of phenological factors and animal behaviour. The information provided in this study reinforces the knowledge on the dynamics and patterns of UVC and represents an important element for the identification of mitigation measures. Our findings suggest that efforts to reduce UVC should also focus on driver attitudes considering the seasonal and daily variations in UVC.

Moose Management Strategies under Changing Legal and Institutional Frameworks

Moose (Alces alces) management strategies in Lithuania, East Europe, were analyzed. The study was intended to show the (un)sustainability of the current management approach in relation to changes in hunting rules, hunting organization and development of the responsible administrative bodies. Moose population and bag dynamics were analyzed using I index in connected scatterplots and compound annual growth rates (CAGR). In 1962–2020, the CAGR of the moose population was 3.84%, resulting in a population size increase of nearly 10 times. The seesaw principle in moose management was confirmed, showing three periods of population decrease (1973–1977, 1989–1995, 2000–2005), and two periods of hunting bag decrease (1976–1978 and 1990–1993). All decline phases were related to legal and administrative issues in the country. Since 2006, population growth has not been controlled. Lithuania has no long-term strategy of the moose population management at any administrative level. The current management approach is not sustainable, as it has not ensured long-term stability of the moose population. The current continuous growth of population, followed by only a moderate increase in the hunting bag, is related to the possibility for owners to adopt long-term planning of the hunting plot units.

What attributes are relevant for drainage culverts to serve as efficient road crossing structures for mammals?

Roads increase wildlife mortality and present a movement barrier for many species. While wildlife passages have been advocated as a solution to many of the problems associated with roads, they are expensive and many roads still have none. However, roads usually have a series of drainage culverts designed to allow water to cross underneath the road, which might also be used by some mammals. This study aims to (1) determine what variables influence the number of successful passages of drainage culverts by mammals, and to (2) parse the effects that these variables have on the entry into and subsequent full passage of drainage culverts by individual mammals, using cameras and animal track stations along a 20 km stretch of autoroute 10 in Southern Quebec (Canada). Overall, 20 species were observed outside of the drainage culverts, but only about half of them were detected making full crossings. While various species were often seen outside, only animals highly tolerant to water, including raccoons (Procyon lotor) and American mink (Neovison vison), were observed fully crossing the structures with regularity, whereas the number of full crossings was small (<8) for all other species. High-water levels and use of polyethylene as a construction material were the strongest deterrents for both the number of successful passages and the probability of entry into the culverts. While several variables (e.g., water level, structure material, moon luminosity, distance to forest) influenced culvert entry, none had an influence on a mammal's probability of complete passage once it had entered. The results imply that ordinary drainage culverts are unsuitable as substitutes for designated wildlife passages for mammals. We recommend the installation of designated wildlife passages and fences, and that in places where wildlife passages are not feasible, dry ledges be installed in existing drainage culverts to better allow small and medium-sized mammals to safely cross under roads while avoiding the water inside of the culverts. To our knowledge, this study is the first to successfully combine trail cameras inside of drainage culverts with track-box data in the adjacent habitat.

Habitat Suitability Based Models for Ungulate Roadkill Prognosis

Simple Summary

Red deer, roe deer and wild boar movements and crossings of the two highways in Lithuania were modeled. Validity of obtained models was tested by comparing the pathway predictions to the real roadkill and roadkill cluster locations in 2002–2009 (at the time the models were created) and in 2010–2017 (testing the prognostic value of these models). Across both periods and on both highways, the roe deer roadkill locations were significantly closer to the model-predicted pathways than to randomly selected points. The prediction of roadkill locations was also good for wild boar. The roe deer roadkill clusters and multi-species clusters were significantly better represented by the model than by random distribution. Thus, habitat suitability-based models of ungulate movement are recommended as an additional tool for planning wildlife-vehicle collision mitigation measures.

Abstract

Roads do not only have a detrimental effect on nature (fragmenting habitats, isolating populations and threatening biodiversity), but the increasing numbers of wildlife-vehicle collisions are also a direct threat to humans and property. Therefore, mitigation measures should be placed with respect to animal distribution and movements across the roads. We simulated red deer, roe deer and wild boar movements in Lithuania, focusing on the two main highways A1 and A2. Using regional habitat suitability and linkage models, we calculated movement pathways and the most probable crossing zones in 2009. The prognostic value of these models was tested by comparing the pathway predictions to the real roadkill and roadkill cluster locations in 2002–2009 and 2010–2017. Across both periods and on both highways, the roe deer roadkill locations were significantly closer to the model-predicted pathways than to randomly selected points. The prediction of roadkill locations was also good for wild boar. The roe deer roadkill clusters and multi-species clusters were significantly better represented by the model than by random distribution. On both highways, the biggest differences in distance from the predicted locations were near big cities. We recommended wildlife movement models as an additional tool for planning wildlife-vehicle collision mitigation measures and we advise measures for increasing their predicting power.