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Aim of study: We aim at (i) developing a reference definition of mixed forests in order to harmonize comparative research in mixed forests and (ii) review the research perspectives in mixed forests.Area of study: The definition is developed in Europe but can be tested worldwide.Material and Methods: Review of existent definitions of mixed forests based and literature review encompassing dynamics, management and economic valuation of mixed forests.Main results: A mixed forest is defined as a forest unit, excluding linear formations, where at least two tree species coexist at any developmental stage, sharing common resources (light, water, and/or soil nutrients). The presence of each of the component species is normally quantified as a proportion of the number of stems or of basal area, although volume, biomass or canopy cover as well as proportions by occupied stand area may be used for specific objectives. A variety of structures and patterns of mixtures can occur, and the interactions between the component species and their relative proportions may change over time.The research perspectives identified are (i) species interactions and responses to hazards, (ii) the concept of maximum density in mixed forests, (iii) conversion of monocultures to mixed-species forest and (iv) economic valuation of ecosystem services provided by mixed forests.Research highlights: The definition is considered a high-level one which encompasses previous attempts to define mixed forests. Current fields of research indicate that gradient studies, experimental design approaches, and model simulations are key topics providing new research opportunities.Keywords: COST Action; EuMIXFOR; mixed-species forests; admixtures of species.

Tropical forests are huge reservoirs of terrestrial carbon and are experiencing rapid degradation and deforestation. Understanding forest structure proves vital in accurately estimating both forest biomass and also the natural disturbances and remote sensing is an essential method for quantification of forest properties and structure in the tropics. Our objective is to examine canopy vegetation profiles formulated from discrete return LIght Detection And Ranging (lidar) data and examine their usefulness in estimating forest structural parameters measured during a field campaign. We developed a modeling procedure that utilized hypothetical stand characteristics to examine lidar profiles. In essence, this is a simple method to further enhance shape characteristics from the lidar profile. In this paper we report the results comparing field data collected at La Selva, Costa Rica (10° 26′ N, 83° 59′ W) and forest structure and parameters calculated from vegetation height profiles and forest structural modeling. We developed multiple regression models for each measured forest biometric property using forward stepwise variable selection that used Bayesian information criteria (BIC) as selection criteria. Among measures of forest structure, ranging from tree lateral density, diameter at breast height, and crown geometry, we found strong relationships with lidar canopy vegetation profile parameters. Metrics developed from lidar that were indicators of height of canopy were not significant in estimating plot biomass (p-value=0.31, r2=0.17), but parameters from our synthetic forest model were found to be significant for estimating many of the forest structural properties, such as mean trunk diameter (p-value=0.004, r2=0.51) and tree density (p-value=0.002, r2=0.43). We were also able to develop a significant model relating lidar profiles to basal area (p-value=0.003, r2=0.43). Use of the full lidar profile provided additional avenues for the prediction of field based forest measure parameters. Our synthetic canopy model provides a novel method for examining lidar metrics by developing a look-up table of profiles that determine profile shape, depth, and height. We suggest that the use of metrics indicating canopy height derived from lidar are limited in understanding biomass in a forest with little variation across the landscape and that there are many parameters that may be gleaned by lidar data that inform on forest biometric properties