Tree-Related Microhabitats and Multi-Taxon Biodiversity Quantification Exploiting ALS Data

In addition to studies of beetle and bird communities and TreMs distributions, ALS data hold great potential for analyzing the relationship between forest structure and animal diversity [31]. ALS-based approaches are, therefore, increasingly being used to explore, explain, and predict biodiversity given the promising results and replicability of procedures [5,35,59]. However, the relationship between ALS data and multi-taxon biodiversity has seldom been explored. In this context, we explored the relationship between taxa and ALS in the Nature Reserve of Vallombrosa, a Mediterranean mountainous forested area dominated by silver fir and beech.

In this study, several ALS predictors were calculated [47] which are crucial elements for explaining the occurrence and distribution of the examined taxa. However, the variable selection procedure for each prediction model shows that a limited number of metrics were the best predictors, highlighting that single metrics can explain most of the information provided by the ALS point cloud. Moreover, selecting RF model variables ensures that information retrieved from calculated ALS metrics contributes to uncorrelated information. The most frequent ALS predictor for multi-taxon biodiversity represented the vegetation (e.g., the point cloud-based factors: intensity skewness; 10th z percentiles of fourth intensity quartiles, and differences between 50 z percentiles of second intensity quartiles and 25 z percentiles of second intensity quartiles) and the terrain structure (slope) (Supplementary Material Table S5).

The most important ALS metrics are point-cloud-based (18 of the 26 selected metrics). Among the ALS metrics developed from the intensity pulse and the z values, the most promising are the different z percentiles of each intensity quartile (e.g., for the Shannon index of saproxylic beetles, birds, TreMs, and epixylic TreMs together with the richness of saproxylic and epixylic TreMs) and the structural indices derived from the differences in these (e.g., richness of beetles, birds, epixylic TreMs, and Shannon index of birds and saproxylic and epixylic TreMs). Particularly, the intensity distribution in quartiles (e.g., the richness of the birds), the 20th percentile of z distribution (e.g., for beetle Shannon index), the difference between maximum and minimum intensity values (e.g., for saproxylic TreMs), intensity skewness (e.g., saproxylic beetle richness) were selected (Table 1 and Supplementary Material Table S5). Considering the vertical structure of the forest and ALS metrics, the canopy layer is mainly represented by the proportion of pulse in the upper point cloud vertical layer (10th percentile of z distribution). Additionally, multiple ALS metrics referring to the lower forest layers were selected, such as the high intensity values (4th quartile) of ALS pulses in the lower layers of the point cloud (10th and 20th percentiles of z distribution) (Table S5). Although outside the study scope, these metrics appear to benefit studies investigating forest species diversity, on the one hand, and to detect the occurrence of shrubs or dead wood in the forest, on the other.

In addition to the point cloud predictors, four predictors derived from the CHM were selected: the variance of GLCM (TreMs Shannon index), the standard deviation of GLCM homogeneity (richness of saproxylic beetles), the standard deviation of GLCM mean (richness of saproxylic TreMs), and the standard deviation of GLCM variance (Shannon index of beetles) (Table 1 and Supplementary Material Table S5). In addition to a predictor from spectral values (maximum value of blue for the Shannon index of saproxylic beetles), the last three were derived from auxiliary variables. In particular, the growing stock volume (for TreMs and saproxylic TreMs richness) and the two topographic predictors, slope (Shannon index of TreMs and epixylic TreMs and TreMs richness) and aspect (saproxylic TreMs Shannon index), were selected (Supplementary Material Table S5).

The selected predictors were found in similar studies in which the most frequent explanatory ALS variables selected for the forest structural diversity identification were the coefficient of variation, standard deviation and skewness of ALS heights, canopy cover metrics, and percentiles of vegetation heights [60]. Similar to Herniman et al. [61], our results show that ALS metrics describing topography (slope and aspect) can contribute to the modeling of the biodiversity of TreMs. Therefore, when modeling biodiversity, it is advisable to integrate structural predictors of vegetation with terrain predictors available from ALS data.

While promising, the results show a relatively low coefficient of determination (R²) (maximum 0.30) across all taxa and diversity indices. This can be caused by several factors, such as the complex non-linear relationship between predictors and diversity indices, the presence of outliers both in dependent and independent variables, the complexity of the model after the variable selection, and the low variability of diversity indices within the study area. However, our results in modeling species diversity indices are consistent with other, similar studies showing moderate results with values between 0 and 0.34 [31]. These values can be explained considering that the study area, while sufficiently large, has low environmental variability [62], and vegetation variations resulting in stronger correlations between ALS predictors and taxa [33]. In this sense, increasing the number of sampling areas could improve the results. Despite the low variance explained by the models, random forests obtained a high prediction accuracy, resulting in low RMSE values. Moreover, the aggregation of pixel predictions within homogeneous areas, such as the management units identified by the Reserve management plan, resulted in a close estimate of the true value as the aggregation area increased.

In the context of a forest management approach aimed at sustaining or increasing forest complexity and diversity, ALS, capable of investigating forest structure, emerges as a promising technique for extensively detecting and monitoring potential biodiversity hotspots over large areas [31].

Our results show that specific habitat features (e.g., the occurrence of TreMs) are necessary in making forests suitable for beetle and bird communities. Furthermore, specific characteristics of forest areas may represent the entire species community. For example, saproxylic beetles are highly dependent on the microclimatic conditions of particular TreMs [14,63]. A knowledge of biodiversity indicators allows us to design and optimize management strategies that consider the particular ecological needs of some species of birds and beetles [64,65]. Specifically, our analysis could be used to identify some management options to preserve beetle and bird communities, promote tree habitats, increase total tree volume, and reduce overall forest density [66]. These characteristics are typical of mature forests, the achievement of which should be one of the objectives of sustainable forest management. However, the community structure of beetles and birds was not determined by habitat type, as the forest sectors considered were quite similar in fauna and vegetation [45,65].

Mixed forests with heterogeneous stands provide a greater ecological niche [62]. Forest management can have implications on the proportion of tree species, like, for example, the abandonment of silvicultural activities in mixed stands, which has led to an increase in beech and a decrease in silver fir [67]. On the other hand, in the Vallombrosa pure even-aged fir forests, where silvicultural practices have long been absent, gaps are spontaneously opening up with a gradual transformation into mixed, naturally regenerating forests. The management plan in place simulates these natural events to increase the specific diversity and structural and compositional complexity of the fir stands [38].

The diversity of species and taxa sampled in this work is reflected in their different ecological roles and characteristics within the community, as well as in forest management. Notably, the relationship between the different analyzed taxa and the tree component depends on the trees’ physical structure, which can be derived from ALS point clouds through 3D structural metrics. Large trees with complex canopy structures are often cavitated and rich in TreMs, hosting populations of saproxylic beetle communities and birds. Moreover, the high number of different TreMs typically found in long-unmanaged stands results in differential levels of specialization of the ornithic and saproxylic components, often at risk of extinction [8,14]. As for the Picidae, Bütler et al. [68] have suggested conserving at least 5% of standing dead trees in forest areas larger than 100 ha. These thresholds correspond to the amount of habitat below which fragmentation may affect population persistence [69].

About half of the sampled beetles in the forest of Vallombrosa are saproxylic species included in the Italian Red List. Saproxylic beetles include highly specialized species. Consequently, they are considered valid indicators for assessing the naturalness of forest ecosystems [21]. In our case, we found 10 endangered species, including Megathous nigerrimus (Elateridae) and Anaspis ruficollis (Scraptiidae) for the Endangered (EN) category and Mordellochroa milleri (Mordellidae) included in the Critically Endangered (CR) category. Furthermore, two species were included in the DD (Data Deficient) category (i.e., Rhizophagus cribratus and R. perforatus (Monotomidae)). Saproxylic beetles play an essential role in the food chain of the forest ecosystem, particularly in the recycling of nutrients, as they depend on—or are involved in—deadwood decay processes. However, information on the status and distribution of the population of these species is particularly scarce in the Mediterranean area [70]. Our results indicate that vertical forest heterogeneity is an important variable for saproxylic assemblages in these Mediterranean montane forests [14].

In the Northern Apennines (including the study area), the presence of a significant proportion of conifers is a decisive factor for the occurrence of numerous bird species [65], including, among the target species, Lophophanes cristatus and the Dryocopus martius [71]. Mixed forests generally have heterogeneous stands, providing a greater range of nesting and foraging sites [72,73]. Indeed, cavity-nesting species, being more vulnerable to mammalian predation, could therefore suffer from the indirect effect of an increase in tree species [74]. Species that have specific habitat requirements (e.g., the presence of cavities, the structure of canopy layers) usually have strict preferences in terms of tree species, and therefore an increase in tree species in general could correspond to a decrease in the availability of niches [72].

ALS data represent an effective technique for detecting multi-taxon biodiversity patterns. They can be used at different spatial scales to capture highly detailed data about forest structure and terrain characteristics, also facilitating monitoring purposes thanks to easy comparability when a multitemporal survey is available. Despite the excellent opportunities remote sensing offers, some limitations must also be considered. Although ALS data provide information at the level of the canopy and its structure [75], in dense and continuously closed canopies, ALS metrics cannot fully describe the ecosystem biodiversity by capturing microspatial variations [62], for which accurate field campaigns remain necessary. Accordingly, one potential issue of ALS is related to the point density. Specifically, as the density of points decreases, the forest structure ALS variables become less accurate. Particularly promising are high-point-density ALS data that allow detailed descriptions of lower canopy structures and enable new voxel-based approaches to retrieve information from ALS data [76]. On the other hand, our ALS metrics are derived from a survey at 10 pulses m⁻², which is higher than other studies that still showed reasonable results (⁻²) [31].

Furthermore, ALS data can provide a snapshot of the landscape at a specific time, while biodiversity is dynamic. Due to the acquisition cost, ALS data are often not open access and cannot provide repeated and temporally dense observations, not capturing seasonal variations, migration patterns, or long-term trends. Moreover, and most critical, is the lack of national wall-to-wall ALS data in many countries, as in Italy, which limits large-scale implementation [49,52].

On the other hand, RS data are repeatable and increasingly freely accessible and can be used at different spatial and temporal scales to support fieldwork limitations [77]. Accordingly, combining field activities and remote sensing approaches (also considering integration with other RS spectral data) for forest monitoring can overcome these shortcomings, helping to design and optimize regular and cost-effective monitoring strategies to address biodiversity loss in forest ecosystems. Therefore, further biodiversity studies should be conducted to evaluate the effectiveness of satellite LiDAR data such as GEDI [78], or the integration with terrestrial laser scanning data, to overcome the major limitations of ALS in penetrating the canopy in dense forests. On the other hand, RS data do not replace field surveys, which are essential for assessing forest biodiversity and identifying individual species, along with their rarity and composition.

Furthermore, TreMs are not the only drivers of ecological indicators of habitats for animal species, which are also determined by multiple interspecific and intraspecific biotic interactions with forest variables [14]. Therefore, our integrated approach could help in identifying hotspots in the forest with the greatest need for management interventions to improve the conservation of red-listed saproxylic species and birds.