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Fire regime shifts are driven by climate and natural vegetation changes, but can be strongly affected by human land management. Yet, it is poorly known how humans have influenced fire regimes prior to active wildfire suppression. Among the last 250 years, the human contribution to the global increase in fire occurrence during the mid-19th century is especially unclear, as data sources are limited. Here, we test the extent to which forest management has driven fire regime shifts in a temperate forest landscape. We combine multiple fire proxies (macroscopic charcoal and fire-related biomarkers) derived from highly resolved lake sediments (i.e., 3–5 years per sample), and apply a new statistical approach to classify source area- and temperature-specific fire regimes (biomass burnt, fire episodes). We compare these records with independent climate and vegetation reconstructions. We find two prominent fire regime shifts during the 19th and 20th centuries, driven by an adaptive socio-ecological cycle in human forest management. Although individual fire episodes were triggered mainly by arson (as described in historical documents) during dry summers, the biomass burnt increased unintentionally during the mid-19th century due to the plantation of flammable, fast-growing pine tree monocultures needed for industrialization. State forest management reacted with active fire management and suppression during the 20th century. However, pine cover has been increasing since the 1990s and climate projections predict increasingly dry conditions, suggesting a renewed need for adaptations to reduce the increasing fire risk. [ABSTRACT FROM AUTHOR]

Airborne Laser Scanning (ALS) is a technology often used to study forest areas. The main area of application of ALS in forests is collecting data to determine the height of individual trees and entire stands, tree density and stand biomass. The content of the ALS data is also classified, i.e., registered objects are identified, including the species affiliation of individual trees. Important information for forest districts includes other parameters related to the structure and share of stands and the number of trees in the forest district. The main goal of this study was to propose the new ALS data processing methodology for detecting single trees in the Samławki Forest District. The idea of the proposed methodology is to indicate a free and accessible solution for any user (at least in Poland). This new ALS data processing methodology contributes to research on the use of ALS data in forest districts to maintain up-to-date and accurate stand statistics. This methodology was based on free data from the geoportal.gov.pl portal and free software, which allowed to minimize the costs of preparing data for the needs of forestry activities. In cooperation with the Samławki Forest District, the proposed methodology was used to detect the number and heights of trees for two forest addresses 13b and 30a, and then to calculate the volume of stands. As a result, the volume of the analyzed stands was calculated, obtaining values differing from the nominal ones included in the FMP (Forest Management Plan) by about 25% and 5%, respectively, for larch and oak. [ABSTRACT FROM AUTHOR]

(1) Red deer (Cervus elaphus L.) population estimates in Poland have increased dramatically in recent years, averaging 15.2% increase per year between 1975 and 1979. We attribute these recent population increases to (1) low culling quotas because of underestimated population size, (b) culls averaging 2/3 of quotas, and (c) recent changes in forest management practices. (2) Timber harvests have increased with concomitant decreases in new clear-felled areas (clearcuts). This occurs because of increased incidence of selective cutting of mature stands. Red deer density is positively correlated with area in selectively cut forest stands in the Carpathian Mountains because these stands provide both food and cover, and thus receive more thorough utilization than do clear-felled areas. (3) Red deer density is highest in mountain deciduous forest types is so strong that there is an inverse correlation between red deer population density and the diversity of forest types. (4) We develop a predictive model of forest habitat influence on red deer populations: y = 0·15 + 1·4x1 - 2·22x2 where y is red deer density per 100 ha. x1 is the proportion of the area in mountain deciduous forest type, and x2 is the proportion of stand area selectively cut. [ABSTRACT FROM AUTHOR]