Habitat Models of Focal Species Can Link Ecology and Decision-Making in Sustainable Forest Management

A fundamental problem of sustainability is how to reduce the double complexity of ecological and social systems into simple operational terms. We highlight that the conservation concept of focal species (selected species sensitive to a set of anthropogenic threats to their habitat) links multiple issues of ecological sustainability, and their habitat models can provide a practical tool for solving these issues. A review of the literature shows that most spatial modeling of focal species focuses on vertebrates, lacks the aspect of aquatic and soil habitats, and has been slow in the uptake by actual management planning. We elaborate on a deductive modeling approach that first generalizes the main influential dimensions of habitat change (threats), which are then parameterized as habitat quality estimates for focal species. If built on theoretical understanding and properly scaled, the maps produced with such models can cost-effectively describe the dynamics of ecological qualities across forest landscapes, help set conservation priorities, and reflect on management plans and practices. The models also serve as ecological hypotheses on biodiversity and landscape function. We illustrate this approach based on recent additions to the forest reserve network in Estonia, which addressed the insufficient protection of productive forest types. For this purpose, mostly former production forests that may require restoration were set aside. We distinguished seven major habitat dimensions and their representative taxa in these forests and depicted each dimension as a practical stand-scale decision tree of habitat quality. The model outcomes implied that popular stand-structural targets of active forest restoration would recover passively in reasonable time in these areas, while a critically degraded condition (loss of old trees of characteristic species) required management beyond reserve borders. Another hidden issue revealed was that only a few stands of consistently low habitat quality concentrated in the landscape to allow cost-efficient restoration planning. We conclude that useful habitat models for sustainable forest management have to balance single-species realism with stakeholder expectations of meaningful targets and scales. Addressing such social aspects through the focal species concept could accelerate the adoption of biodiversity distribution modeling in forestry

Patterns and correlates of claims for brown bear damage on a continental scale

Wildlife damage to human property threatens human-wildlife coexistence. Conflicts arising from wildlife damage in intensively managed landscapes often undermine conservation efforts, making damage mitigation and compensation of special concern for wildlife conservation. However, the mechanisms underlying the occurrence of damage and claims at large scales are still poorly understood. Here, we investigated the patterns of damage caused by brown bears Ursus arctos and its ecological and socio-economic correlates at a continental scale. We compiled information about compensation schemes across 26 countries in Europe in 2005-2012 and analysed the variation in the number of compensated claims in relation to (i) bear abundance, (ii) forest availability, (iii) human land use, (iv) management practices and (v) indicators of economic wealth. Most European countries have a posteriori compensation schemes based on damage verification, which, in many cases, have operated for more than 30 years. On average, over 3200 claims of bear damage were compensated annually in Europe. The majority of claims were for damage to livestock (59%), distributed throughout the bear range, followed by damage to apiaries (21%) and agriculture (17%), mainly in Mediterranean and eastern European countries. The mean number of compensated claims per bear and year ranged from 0·1 in Estonia to 8·5 in Norway. This variation was not only due to the differences in compensation schemes; damage claims were less numerous in areas with supplementary feeding and with a high proportion of agricultural land. However, observed variation in compensated damage was not related to bear abundance. Synthesis and applications. Compensation schemes, management practices and human land use influence the number of claims for brown bear damage, while bear abundance does not. Policies that ignore this complexity and focus on a single factor, such as bear population size, may not be effective in reducing claims. To be effective, policies should be based on integrative schemes that prioritize damage prevention and make it a condition of payment of compensation that preventive measures are applied. Such integrative schemes should focus mitigation efforts in areas or populations where damage claims are more likely to occur. Similar studies using different species and continents might further improve our understanding of conflicts arising from wildlife damage

Human disturbance is the most limiting factor driving habitat selection of a large carnivore throughout Continental Europe

Habitat selection is a multi-scale process driven by trade-offs between benefits, such as resource abundance, and disadvantages, such as the avoidance of risk. The latter includes human disturbances, to which large carnivores, with their large spatial requirements, are especially sensitive. We investigated the ecological processes underlying multi-scale habitat selection of a large carnivore, namely Eurasian lynx, across European landscapes characterized by different levels of human modification. Using a unique dataset of 125 lynx from 9 study sites across Europe, we compared used and available locations within landscape and home-range scales using a novel Mixed Effect randomForest approach, while considering environmental predictors as proxies for human disturbances and environmental resources. At the landscape scale, lynx avoided roads and human settlements, while at the home-range scale natural landscape features associated with shelter and prey abundance were more important. The results showed sex was of relatively low variable importance for lynx's general habitat selection behaviour. We found increasingly homogeneous responses across study sites with finer selection scales, suggesting that study site differences determined coarse selection, while utilization of resources at the finer selection scale was broadly universal. Thereby describing lynx's requirement, if not preference, for heterogeneous forests and shelter from human disturbances and implying that regional differences in coarse-scale selection are driven by availability rather than preference. These results provide crucial information for conserving this species in human-dominated landscapes, as well as for the first time, to our knowledge, generalising habitat selection behaviour of a large carnivore species at a continental scale.acceptedVersio

Prerequisites for coexistence: human pressure and refuge habitat availability shape continental‑scale habitat use patterns of a large carnivore

Context Adjustments in habitat use by large carnivores can be a key factor facilitating their coexistence with people in shared landscapes. Landscape composition might be a key factor determining how large carnivores can adapt to occurring alongside humans, yet broad-scale analyses investigating adjustments of habitat use across large gradients of human pressure and landscape composition are lacking. Objectives Here, we investigate adjustments in habitat use by Eurasian lynx (Lynx lynx) in response to varying availability of refuge habitats (i.e., forests and rugged terrain) and human landscape modifcation. Methods Using a large tracking dataset including 434 individuals from seven populations, we assess functional responses in lynx habitat use across two spatial scales, testing for variation by sex, daytime, and season. Results We found that lynx use refuge habitats more intensively with increasing landscape modifcation across spatial scales, selecting forests most strongly in otherwise open landscapes and rugged terrain in mountainous regions. Moreover, higher forest availability enabled lynx to place their home ranges in more human-modifed landscapes. Human pressure and refuge habitat availability also shaped temporal patterns of lynx habitat use, with lynx increasing refuge habitat use and reducing their use of human-modifed areas during periods of high exposure (daytime) or high vulnerability (postnatal period) to human pressure. Conclusions Our fndings suggest a remarkable adaptive capacity of lynx towards human pressure and underline the importance of refuge habitats across scales for enabling coexistence between large carnivores and people. More broadly, we highlight that the composition of landscapes determines how large carnivores can adapt to human pressure and thus play an important role shaping large carnivore habitat use and distributions.publishedVersio

Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

Aim: The increasing availability of animal tracking datasets collected across many sites provides new opportunities to move beyond local assessments to enable de-tailed and consistent habitat mapping at biogeographical scales. However, integrating wildlife datasets across large areas and study sites is challenging, as species' varying responses to different environmental contexts must be reconciled. Here, we compare approaches for large-area habitat mapping and assess available habitat for a recolo-nizing large carnivore, the Eurasian lynx (Lynx lynx).Location: Europe.Methods: We use a continental-scale animal tracking database (450 individuals from 14 study sites) to systematically assess modelling approaches, comparing (1) global strategies that pool all data for training versus building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habi-tat selection and (3) different modelling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms.Results: Testing models on training sites and simulating model transfers, global and local modelling strategies achieved overall similar predictive performance. Model performance was the highest using flexible machine-learning algorithms and when incorporating variation in habitat selection as a function of environmental variation. Our best-performing model used a weighted combination of local, site-specific habi-tat models. Our habitat maps identified large areas of suitable, but currently unoccu-pied lynx habitat, with many of the most suitable unoccupied areas located in regions that could foster connectivity between currently isolated populations.Main Conclusions: We demonstrate that global and local modelling strategies can achieve robust habitat models at the continental scale and that considering regional variation in habitat selection improves broad-scale habitat mapping. More generally, we highlight the promise of large wildlife tracking databases for large-area habitat mapping. Our maps provide the first high-resolution, yet continental assessment of lynx habitat across Europe, providing a consistent basis for conservation planning for restoring the species within its former range.publishedVersio

The simulation scenario comparison at a glance.

For political and administrative governance of land-use decisions, high-resolution and reliable spatial models are required over large areas and for various time horizons. We present a process-centered simulation model ‘NextStand’ (a forest landscape model, FLM) and its R-script, which predicts regional forest characteristics at a forest stand resolution. The model uses whole area stand data and is optimized for realistic iterative timber harvesting decisions, based on stand compositions (developing over time) and locations. We used the model for simulating spatial predictions of the Estonian forests in North Europe (2.3 Mha, about 2 M stands); the decisions were parameterized by land ownership, protection regimes, and rules of clear-cut harvesting. We illustrate the model application as a potential broad-scale Decision Support Tool by predicting how the forest age composition, placement of clear-cut areas, and connectivity of old stands will develop until the year 2050 under future scenarios. The country-scale outputs had a generally low within-scenario variance, which enabled to estimate some main land-use effects and uncertainties at small computing efforts. In forestry terms, we show that a continuation of recent intensive forest management trends will produce a decline of the national timber supplies in Estonia, which greatly varies among ownership types. In a conservation perspective, the current level of 13% forest area strictly protected can maintain an overall area of old forests by 2050, but their isolation is a problem for biodiversity conservation. The behavior of low-intensity forest management units (owners) and strict governance of clear-cut harvesting rules emerged as key questions for regional forest sustainability. Our study confirms that high-resolution modeling of future spatial composition of forest land is feasible when one can (i) delineate predictable spatial units of transformation (including management) and (ii) capture their variability of temporal change with simple ecological and socioeconomic (including human decision-making) variables.</div

Fig 3 -

Forest age composition (10-year classes) by management regime at the start (2022) and end of simulation (2050) for the DEFb scenario: (a) intensively managed stands, (b) non-intensively managed stands, (c) state-owned stands, (d) strictly protected stands.</p

Fig 4 -

Predicted median size distributions of early-successional and old-forest patches based on the REAL scenario: (a) stands younger than 20 years, (b) stands aged 80 years or older, (c) stands aged 100 years or older. Dashed lines denote edge proportions of the old-forest patches from the total area. Note the different scales on the panels.</p

Program code of the simulation model in R.

For political and administrative governance of land-use decisions, high-resolution and reliable spatial models are required over large areas and for various time horizons. We present a process-centered simulation model ‘NextStand’ (a forest landscape model, FLM) and its R-script, which predicts regional forest characteristics at a forest stand resolution. The model uses whole area stand data and is optimized for realistic iterative timber harvesting decisions, based on stand compositions (developing over time) and locations. We used the model for simulating spatial predictions of the Estonian forests in North Europe (2.3 Mha, about 2 M stands); the decisions were parameterized by land ownership, protection regimes, and rules of clear-cut harvesting. We illustrate the model application as a potential broad-scale Decision Support Tool by predicting how the forest age composition, placement of clear-cut areas, and connectivity of old stands will develop until the year 2050 under future scenarios. The country-scale outputs had a generally low within-scenario variance, which enabled to estimate some main land-use effects and uncertainties at small computing efforts. In forestry terms, we show that a continuation of recent intensive forest management trends will produce a decline of the national timber supplies in Estonia, which greatly varies among ownership types. In a conservation perspective, the current level of 13% forest area strictly protected can maintain an overall area of old forests by 2050, but their isolation is a problem for biodiversity conservation. The behavior of low-intensity forest management units (owners) and strict governance of clear-cut harvesting rules emerged as key questions for regional forest sustainability. Our study confirms that high-resolution modeling of future spatial composition of forest land is feasible when one can (i) delineate predictable spatial units of transformation (including management) and (ii) capture their variability of temporal change with simple ecological and socioeconomic (including human decision-making) variables.</div

Fig 2 -

Broad forest age-class composition at the start of the simulation (1.1.2022) and at the end (31.12.2050) for all simulation scenarios in Estonia: (a) all forests, (b) production forests, (c) restricted-management forests, and (d) strictly protected forests. The scenarios followed either of two starting points (a, b); see Table 1 for details. For the scenarios with several simulation runs (DEFa, MODa, REAL), the values depicted are medians.</p