*2.1. Theoretical Background*

Simply put, the practical question of environmental management is where to do what to sustain environmental quality. For the SFM principle of maintaining and restoring biodiversity, it translates to locating and managing threats and opportunities for biodiversity on dynamic landscapes [57–59]. There are four classes of spatial management decisions involved: stand-scale management for single or multiple goals; landscape design by combining stand-scale goals for landscape functions (including setting aside protected areas); regulating forest benefits and values in time; and managing for uncertainty at multiple scales.

In our view, the strength of Lambeck's [54] concept of focal species is that it integrates these strategic aspects in a way that is understandable to the wider public, thus serving the stakeholder participation principle of SFM [60]. Specifically:


The opportunity for spatial models in this framework is to predict focal species' distribution or performance in real landscapes subject to expected or designed change. Such spatial predictions are derived from species–habitat relationships, where specific 'threats' refer to limiting factors or population processes of the focal species. Note that, for modeling these links, ecological niches of the species must be understood beyond correlative patterns in current distributions [70]; thus, so-called black-box modeling approaches that fit environmental parameters without understanding their ecological meaning [71] are discouraged. Due to the underlying logic that 'sensitive species illustrate a general threat', the maps derived from properly parameterized spatial models might then help to depict, analyze, and communicate broader 'where' and 'how' of sustaining forest biodiversity in the spatial and temporal scales for which the parameter values are available (Figure 1). Depending on how closely the species' distributions follow environmental threats in time, such maps may also reveal past or present spatial extent, severity, and reversibility of the threats (e.g., [72–74]).

The methods for predictive mapping of species distributions can be divided into correlative (inductive) and mechanistic (deductive) models. The inductive methods, where predictions are derived from statistically linking empirical observations with habitat characteristics, include many algorithms and programming tools available [71]. The algorithms basically differ depending on the species data (presence-only, presence–absence, or finer scales) and shapes of its habitat function. The deductive methods are based on prior insight into the species' requirements, with a wide range of more and less formal approaches, including procedures for systematizing expert knowledge (e.g., [75,76]). For either class of models to enable spatially explicit management guidance, they should be able to depict landscape change (including alternative management scenarios) in terms of the factors that indicate threats.

**Figure 1.** A framework of targets and activities that link basic biodiversity knowledge (left triangle) and sustainable forest management (SFM) (right triangle) through the nexus of focal species habitat modeling. The activities indicated by numbers: **1**, Red-listing of threatened species; **2**, distinguishing focal species by listing major threats; **3**, focal species habitat modeling; **4**, habitat conservation; **5**, landscape design.

There are several basic caveats in interpreting predicted distributions of focal species directly for broader biodiversity management. First, each species can be limited by multiple factors, and its distribution is affected by stochastic events and population processes [55,77,78]. This implies that both realized and potential (habitat) distributions of species affected by the same threat only overlap partly and to an extent that varies in time. Hence, increasing model prediction accuracy for a specific species—a major technical aim of distribution modeling [71]—can paradoxically reduce the insight obtained from the model about wider biodiversity. Second, uncertainty of most environmental parameters increases when predicting the future, and data quality is usually reduced toward the past as well. This, too, means general problems with predictions, specifically for complex models. The issue is to find simple and robust habitat characteristics that change predictably in time. Third, most sensitive species may be very rare or even extirpated in the degraded landscapes where habitat improvement is most needed; thus, their habitat prediction may not be practical or reliable for the remaining biodiversity [77]. Instead of completely ignoring such species, a possibility is to add species less vulnerable to the same threat to be able to cover a broad range of environmental change.

An alternative to habitat modeling of threatened species is to map threats (hazards) directly. Such 'exposure maps' have been created by remote sensing of whole landscapes (e.g., for fire frequency, deforestation, night-time lights [79,80]) or modeling point observations based on landscape characteristics (for example, poaching threat maps from camera trapping of poached animals [81]). Threat maps, though, are not explicit about likely biodiversity responses, which in turn limits objective-setting and cost-effective spatial analysis of conservation actions [82]. In a structured decision-making process, focal species thus serve as a multi-purpose tool to set the objectives, choose among actions, and to measure the success.

### *2.2. Published Spatial Models of Focal Species Performance*

Modern techniques of creating species habitat maps for forest management prescriptions originate from the rapid development of spatial analysis in environmental protection, wildlife ecology, and threatened species research in the 1980s. Notably, the United States (US) Forest Service developed Habitat Evaluation Procedures in the 1970s, which became increasingly formalized, supported by guidelines of use and computer programs [83]. Along with the appearance and acceptance of GIS-techniques and data, such procedures transformed from individual assessment to automated landscape analysis (e.g., [84]). In the high-profile conservation case of the spotted owl (*Strix occidentalis*), GIS models were linked with population models in real landscapes first in 1992 [85]. In parallel, there was a development from prescribing forestry activities from the perspective of a single subjectively selected species toward comprehensive sets of species to represent different niche dimensions of habitat specialists ([86,87] or species requiring large areas [88]. In retrospect, these US approaches appear closer to SFM than Lambeck's [54] Australian view that emphasized protection and restoration. The new aspect brought up by the latter was, however, that species should be used to analyze habitat futures, not just to maintain the present values.

To characterize the field's development since then, we performed a search of modeling studies that spatially predicted the performance (incidence, trends, or demography) of representative species in real forest landscapes and through time, and in response to threats that could be mitigated by forest management or conservation. We performed initial searches on 8 April 2020, using the Scopus database and two alternative search strings: (i) TITLE-ABS-KEY ("focal species" AND forest) AND TITLE-ABS-KEY (model\* OR predict\* OR simulat\*); (ii) REF ("Lambeck") AND TITLE-ABS-KEY (forest) AND TITLE-ABS-KEY (model\* OR predict\* OR simulat\*). We excluded irrelevant studies by considering the title and, if unclear, by the abstract. Full texts of the remaining 38 studies were then assessed for whether they included a forestry perspective *s. lat.* (i.e., including also policies, planning of landscapes and set-asides) and met at least three of the following four criteria: A, addressed environmental threats that also affect wider, at least partly known range of forest taxa (note that, for practical purposes, we here restricted the focal species surrogacy assessment to species diversity only); B, included management options or approaches that also affect wider, known species diversity; C, were a part of a legitimate planning process; D, described how the focal taxa were selected based on specific threats and their surrogate value to represent wider species' diversity. If these criteria were supported by references only, we also checked the original publications. Finally, we integrated a series of papers by the same research group in the same study system, and assessed potential gaps in the search string for additional searches on specific topics (metapopulation models; forest water bodies).

Thirteen of the 19 focal-species' modeling studies detected address North American forests (Table 1). Another pattern is that such modeling has remained [89], at least in forestry, largely based on vertebrates. This is despite the problems with cross-taxon congruence being well acknowledged [56]. In fact, many field surveys have addressed potential non-vertebrate surrogate taxa. For example, forest fungal surrogates have been explored in many studies [90], including the matching of selected wood-inhabiting species with threats [91,92]. Specialized lichens appear suitable for guiding multiple management dimensions [93], but spatial models for that remain scarce (Table 1). Modeling for decision-making may have thus contributed to the taxonomic bias in SFM, which is usually attributed to insufficient stakeholder knowledge [94]. A similar gap appears in ecosystem coverage regarding the management of small freshwater bodies, notably headwater streams in forests. Again, there is well-established literature on the indicator value of many aquatic or semi-aquatic taxa, including suggestions to use some invertebrates, fish, amphibians, or birds as broader management targets (e.g., [95–97]). Relevant spatial models are, however, rare and tend to focus solely on the species' indicator value (e.g., [98]) or its conservation perspectives (e.g., [99]).

**Table 1.** Published habitat modeling studies of focal forest species that link the distribution and dynamics of biodiversity threats with implications to management planning and forest policy.



#### **Table 1.** *Cont*.

<sup>1</sup> Main focus of the study: I, mapping forest biodiversity dimensions of landscape change; II, stand-scale effects of intensive timber harvesting in forests; III, metapopulation viability in dynamic woodlands; IV, forest set-asides to protect flagship species; V, biodiversity assessment of forest futures in biodiversity hotspots. <sup>2</sup> \*Studies for official programs for biodiversity conservation or mitigating the environmental impact; #studies not captured with the formal search string

In our view, most studies listed in Table 1 appear relevant to support decision-making at different levels. This contrasts with an overall scarcity of such studies, of which (judging from the statements in original studies), even fewer were parts of actual decision-making processes. There may be two reasons for such neglect. First, modeling of futures relies on diverse assumptions on the system's behavior, so that the best focal-species 'models' are actually sets of several linked models depicting social, climatic, ecosystem, and population changes. To develop such sets may require expensive study programs, such as the Northwest Forest Plan, Coastal Landscape Analysis and Modeling Study (CLAMS) [121] or the Forest Landscape Disturbance and Succession (LANDIS) model programs in the US [122,123]. Second, there may be broader political inertia in the SFM and forest conservation, which inhibits the practical adoption of new analytical tools for designing futures [124–126]. Such inertia is particularly harmful to biodiversity when it suppresses spatial planning under the conditions of increasing timber harvest since spatial solutions are among those few that could mitigate such pressure [127]. Institutional collaboration for mutual understanding of research development might help in both cases (e.g., [125,128]).
