Challenging the Scientific Foundations for an IUCN Red List of Ecosystems

The International Union for Conservation of Nature (IUCN) is currently discussing the development of a Red List of Ecosystems (RLE) that would mirror the categories and criteria used to assess the conservation status of species. The suggested scientific foundations for the RLE are being considered by IUCN for adoption as the backbone of the RLE. We identify conceptual and operational weaknesses in the draft RLE approach, the categories, and criteria.While species are relatively well-described units, there is no consistent means to classify ecosystems for assessing conservation status. The proposed RLE is framed mostly around certain features of ecosystems such as broad vegetation or habitat types, and do not consider major global change drivers such as climate change. We discuss technical difficulties with the proposed concept of ecosystem collapse and suggest it is not analogous to species extinction. We highlight the lack of scientific basis for the criteria and thresholds proposed by the RLE, and question the need to adopt the structure of the Red List of Species for an RLE. We suggest that the proposed RLE is open to ambiguous interpretations and uncertain outcomes, and that its practicality and benefit for conservation should be carefully evaluated before final approval

Introduction

THE WOLF IS TRULY a special animal. As the most widely distributed of all land mammals, the wolf, formally the gray wolf (Canis lupus), is also one of the most adaptable. It inhabits all the vegetation types of the Northern Hemisphere and preys on all the large mammals living there. It also feeds on all the other animals in its environment, scavenges, and can even eat fruits and berries. Wolves frequent forests and prairies, tundra, barren ground, mountains, deserts, and swamps. Some wolves even visit large cities, and, of course, the wolf\u27s domesticated version, the dog, thrives in urban environments. Such a ubiquitous creature must, as a species, be able to tolerate a wide range of environmental conditions, such as temperatures from -56° to +50°C (-70° to +120°F). To capture its food in the variety of habitats, topographies, and climates it frequents, the wolf must be able to run, climb, lope, and swim, and it performs all these functions well. It can travel more than 72 km (43 mi)/day, run at 56-64 km (34-38 mi)/hr, and swim as far as 13 km (8 mi) (P. C. Paquet, personal communication), no doubt aided by the webs between its toes

Wolf Social Ecology

THE FIRST REAL BEGINNING to our understanding of wolf social ecology came from wolf 2204 on 23 May 1972. State depredation control trapper Lawrence Waino, of Duluth, Minnesota, had caught this female wolf 112 km ( 67 mi) south of where L. D. Mech had radio-collared her in the Superior National Forest 2 years earlier. A young lone wolf, nomadic over 100 km2 (40 mi2) during the 9 months Mech had been able to keep track of her, she had then disappeared until Waino caught her. From her nipples it was apparent that she had just been nursing pups. This was the puzzle piece I needed, stated Mech. I had already radio-tracked lone wolves long distances, and I had observed pack members splitting off and dispersing. My hunch was that the next step was for loners to find a new area and a mate, settle down, produce pups, and start their own pack. Wolf 2204 had done just that

References

Abrams, P. A. 2000. The evolution of predator-prey interactions. Annu. Rev. Ecol. Syst. 31:79-105. Abuladze, K. I. 1964. Osnovy Tsestodologii. Vol. IV. Teniatylentochnye gel\u27 minty zhivotnykh i cheloveka i vyzyvaevaniia. Nauka, Moscow. 530 pp. Achuff, P. L., and R. Petocz. 1988. Preliminary resource inventory of the Arjin Mountains Nature Reserve, Xinjiang, People\u27s Republic of China. World Wide Fund for Nature, Gland, Switzerland. 78 pp. Ackerman, B. B., F. A. Leban, M. D. Samuel, and E. 0. Garton. 1990. User\u27s manual for program Home Range. 2d ed. Technical Report no. 15. Forestry, Wildlife, and Range Experiment Station, University ofldaho, Moscow. Acorn, R. C., and M. J. Dorrance. 1990. Methods of investigating predation of livestock. Alberta Agriculture, Edmonton. 36 pp

References

Abrams, P. A. 2000. The evolution of predator-prey interactions. Annu. Rev. Ecol. Syst. 31:79-105. Abuladze, K. I. 1964. Osnovy Tsestodologii. Vol. IV. Teniatylentochnye gel\u27 minty zhivotnykh i cheloveka i vyzyvaevaniia. Nauka, Moscow. 530 pp. Achuff, P. L., and R. Petocz. 1988. Preliminary resource inventory of the Arjin Mountains Nature Reserve, Xinjiang, People\u27s Republic of China. World Wide Fund for Nature, Gland, Switzerland. 78 pp. Ackerman, B. B., F. A. Leban, M. D. Samuel, and E. 0. Garton. 1990. User\u27s manual for program Home Range. 2d ed. Technical Report no. 15. Forestry, Wildlife, and Range Experiment Station, University ofldaho, Moscow. Acorn, R. C., and M. J. Dorrance. 1990. Methods of investigating predation of livestock. Alberta Agriculture, Edmonton. 36 pp

Wolf Social Ecology

THE FIRST REAL BEGINNING to our understanding of wolf social ecology came from wolf 2204 on 23 May 1972. State depredation control trapper Lawrence Waino, of Duluth, Minnesota, had caught this female wolf 112 km ( 67 mi) south of where L. D. Mech had radio-collared her in the Superior National Forest 2 years earlier. A young lone wolf, nomadic over 100 km2 (40 mi2) during the 9 months Mech had been able to keep track of her, she had then disappeared until Waino caught her. From her nipples it was apparent that she had just been nursing pups. This was the puzzle piece I needed, stated Mech. I had already radio-tracked lone wolves long distances, and I had observed pack members splitting off and dispersing. My hunch was that the next step was for loners to find a new area and a mate, settle down, produce pups, and start their own pack. Wolf 2204 had done just that

Conclusion

WOLVES CAN LIVE almost anywhere in the Northern Hemisphere, and almost everywhere they do, they are an issue. In the vast emptiness of the northern tundra or the Arabian desert, on the outskirts of a European town or in the safety of an American national park, in meager agricultural lands in India or mountains in rich Norway or Switzerland, wolves always attract people\u27s attention. Wolves form a key part of many ecosystems, and they are considered charismatic creatures by most human cultures. Thus they polarize public opinion and make headlines year after year. If we look back 6o years to the first landmark monograph by Young and Goldman (1944), or just 30 years to Mech\u27s (1970) volume, we can see that both scientific knowledge of wolf biology and human attitudes toward the wolf have improved tremendously. The wolf has benefited from, and has often been a protagonist and a symbol of, the remarkable changes in the way Western societies regard conservation. However, much of this improvement paralleled the increasing distance between urban and rural cultures, and most of the changes occurred in urban populations

Eurasian otter (Lutra lutra) density estimate based on radio tracking and other data sources

Estimating animal population size is a critical task in both wildlife management and conservation biology. Precise and unbiased estimates are nonetheless mostly difficult to obtain, as estimates based on abundance over unit area are frequently inflated due to the Bedge effect^ bias. This may lead to the implementation of inappropriate management and conservation decisions. In an attempt to obtain an as accurate and conservative as possible picture of Eurasian otter (Lutra lutra) numbers, we combined radio tracking data from a subset of tracked individuals from an extensive project on otter ecology performed in Southern Portugal with information stemming from other data sources, including trapping, carcasses, direct observation of tagged and untagged individuals, relatedness estimates among genotyped individuals, and a minor contribution from non-invasive genetic sampling. In 158 km of water network, which covers a sampling area of 161 km2 and corresponds to the minimum convex polygon constructed around the locations of five radio-tracked females, 21 animals were estimated to exist. They included the five radio-tracked, reproducing females and six adult males. Density estimates varied from one otter per 3.71–7.80 km of river length (one adult otter per 7.09–14.36 km) to one otter per 7.67–7.93 km2 of range, depending on the method and scale of analysis. Possible biases and implications of methods used for estimating density of otters and other organisms living in linear habitats are highlighted, providing recommendations on the issue

Roe deer summer habitat selection at multiple spatio-temporal scales in an Alpine environment

Habitat selection is a hierarchical process that may involve different patterns depending on the spatial and temporal scales of investigation. We studied habitat selection by European roe deer (Capreolus capreolus) in a very diverse environment in the Italian eastern Alps, during summer. We sampled both coarse-grained habitat variables (topographic variables, habitat types and cover) and fine-grained habitat variables (forage components of habitat) in used and available locations along the movement trajectories of 14 adult roe deer equipped with GPS telemetry collars. We used conventional logistic regression to assess roe deer habitat selection at the seasonal home range scale, and conditional logistic regression to take into account the temporal aspect of habitat selection on a weekly basis. Our results indicate that topographic variables were not significant predictors for summer roe deer habitat selection. Roe deer strongly selected dense canopy cover, probably to avoid heat stress during warm summer days. In accordance with previous observations, roe deer preferred young forest stands dominated by pioneer species such as ash (Fraxinus spp.) and hazel (Corylus avellana) over climax environments. Roe deer positively selected shrubs (in particular Fraxinus spp., Erica herbacea, Rhododendron spp. and Vaccinium spp.) throughout the study period, whereas selection for grasses and sedges emerged only at the weekly scale. Habitat selection was clearly related to vegetation phenology, since roe deer selected plants in the most nutritive phenological stages, i.e., shrubs with buds, new leaves and fruits, and newly emergent grasses and sedges. Finally, we found stronger and more significant regression coefficients for forage components of habitat and habitat types at the weekly scale, indicating that matching spatial and temporal scales may improve our understanding of ecological patterns driving habitat selection. Conversely, selection patterns for canopy cover did not change across scales, indicating that this variable likely drives habitat selection in a similar way throughout the entire season

Roe deer summer habitat selection at multiple spatio-temporal scales in an alpine environment

Habitat selection is a hierarchical process that may involve different patterns depending on the spatial and temporal scales of investigation. We studied habitat selection by European roe deer (Capreolus capreolus) in a very diverse environment in the Italian eastern Alps, during summer. We sampled both coarse-grained habitat variables (topographic variables, habitat types and cover) and fine-grained habitat variables (forage components of habitat) in used and available locations along the movement trajectories of 14 adult roe deer equipped with GPS telemetry collars. We used conventional logistic regression to assess roe deer habitat selection at the seasonal home range scale, and conditional logistic regression to take into account the temporal aspect of habitat selection on a weekly basis. Our results indicate that topographic variables were not significant predictors for summer roe deer habitat selection. Roe deer strongly selected dense canopy cover, probably to avoid heat stress during warm summer days. In accordance with previous observations, roe deer preferred young forest stands dominated by pioneer species such as ash (Fraxinus spp.) and hazel (Corylus avellana) over climax environments. Roe deer positively selected shrubs (in particular Fraxinus spp., Erica herbacea, Rhododendron spp. and Vaccinium spp.) throughout the study period, whereas selection for grasses and sedges emerged only at the weekly scale. Habitat selection was clearly related to vegetation phenology, since roe deer selected plants in the most nutritive phenological stages, i.e., shrubs with buds, new leaves and fruits, and newly emergent grasses and sedges. Finally, we found stronger and more significant regression coefficients for forage components of habitat and habitat types at the weekly scale, indicating that matching spatial and temporal scales may improve our understanding of ecological patterns driving habitat selection. Conversely, selection patterns for canopy cover did not change across scales, indicating that this variable likely drives habitat selection in a similar way throughout the entire season