Data For Conservation Planning Research Articles

Seasonally flooded landscape connectivity and implications for fish in the Napo Moist Forest: A high-resolution mapping approach

The Amazon River Basin's fish diversity is shaped by its dynamic flood-pulse system, critical for hydrological and ecological connectivity. This study examines the Napo Moist Forest (NMF) ecoregion, mapping permanent and seasonally flooded areas from 2018 to 2021 using remote sensing and deep learning models. We aimed to map these areas at a high spatial resolution (10-m pixels) and analyse their role in maintaining lateral connectivity essential for fish diversity. Using synthetic aperture radar data from Sentinel-1 combined with deep learning algorithms, we produced high-accuracy flood maps to assess landscape connectivity between rivers and floodplains. Our methodology included creating a ground truth dataset with the Normalized Difference Water Index and integrating high-resolution optical data for model training, overcoming challenges of cloud coverage and dense vegetation. Our predictive model achieved high accuracy (mean pixel accuracy = 97 %) and consistently predicted 4801 km² of surface water, with only 3 % (130 km²) being seasonally flooded areas over four years. The Caquetá, Bajo Marañón, Napo, and Pastaza watersheds accounted for nearly 60 % of the flooded areas, highlighting their ecological importance. Connectivity analysis in three areas of interest within the NMF ecoregion revealed important seasonal and interannual fluctuations in hydrological connectivity due to changes in flooded patch characteristics. Reductions in the number and flooded patch area during low water seasons increased the distance between patches, leading to a disconnection between flooded areas, channels and rivers. Despite hydrological fluctuations, certain patches maintained consistent flooding, critical for lateral connectivity and sustaining aquatic biodiversity. These seasonally flooded areas act as connectors, influencing patch dynamics and connectivity with tributaries. Seasonal and interannual variations in hydrological connectivity are crucial for sustaining fish diversity. Conserving dynamic floodplains supports migratory fish life cycles and biodiversity. This study underscores the importance of high-resolution temporal and spatial data in conservation planning for aquatic ecosystems.

The value of time‐series data for conservation planning

Abstract Protected areas (PAs) are increasingly being used world‐wide for the conservation and management of wildlife. Systematic conservation planning (SCP) aims at ensuring biodiversity persistence while minimizing the threats faced by the species and/or the economic costs related to protection. To account for spatio‐temporal interactions between species and human threats, conservation planning for mobile wildlife requires time‐series data derived from monitoring of species and human threats, a process that is costly and technically challenging. Therefore, assessments of the monitoring period needed to ensure sufficient data input in the design of efficient, adequate and representative networks of PAs are crucial. We demonstrated the value of time‐series data in conservation planning by implementing SCP and data from different monitoring periods to identify priority conservation areas for highly mobile marine megafauna accounting for their main threat: commercial fishing. Two analyses of 10 reserve‐design scenarios each, replicated as many times as the data composing each scenario permitted were run in Marxan. The best solutions of the planning scenarios were statistically compared using the Cohen's Kappa test. We also assessed differences in spatial similarity among and within scenarios using the Wilcoxon nonparametric test and a non‐metric multidimensional scaling analysis. Finally, we compared the necessary cost and the area selected for each scenario. Our study highlights the importance of time‐series ecological and socioeconomic data for the robust selection of priority conservation areas. The results revealed different thresholds of the minimum temporal data required to design efficient networks of PAs for highly mobile species, demonstrating that the incorporation of data covering longer periods to the scenarios produce a more robust selection of priority conservation areas. Conservation plans using data covering <3 years were missing important priority areas. Synthesis and applications. We provide a method for estimating the minimum number of years of monitoring required to design efficient networks of protected areas that ensure the persistence of highly mobile species such as cetaceans and seabirds. This method can be used within an adaptive management framework to evaluate whether a network of protected areas performs as planned, and to test whether management strategies should be altered or adjusted in response to local and global changes.

Low blue carbon storage in eelgrass (Zostera marina) meadows on the Pacific Coast of Canada.

Seagrass habitats provide important ecosystem services, including their ability to take up and store substantial amounts of organic carbon, known as ‘blue carbon.’ However, the paucity of geospatial and carbon storage information along the Pacific Coast of Canada hinders the inclusion of blue carbon storage data in conservation planning and policy development in coastal habitats. We assessed the carbon storage and accumulation rates in three eelgrass (Zostera marina) meadows in southern Clayoquot Sound on the Pacific Coast of British Columbia. The intertidal and subtidal portions of each meadow were mapped and sampled to estimate eelgrass density, biomass, and carbon, and sediment cores were analyzed to estimate sediment carbon storage and accumulation rates. Aboveground biomass measurements were consistent with estimates for Z. marina in other regions, with average aboveground carbon biomass estimates of 16.78 g C m-2 and 16.25 g C m-2 in the intertidal and subtidal areas, respectively. However, the estimated aboveground to belowground biomass ratio was an order of magnitude higher than for seagrass species in temperate/tropical areas, largely because belowground biomass was up to 10 times lower than for other Z. marina meadows, averaging 6.17 g C m-2 and 5.03 g C m-2 in the intertidal and subtidal zones, respectively. Sediment carbon concentrations did not exceed 1.30%Corg, and carbon accumulation rates ranged from 2.90–39.61 g Corg m-2 yr-1, decreasing with depth and averaging 10.8 ± 5.2 g Corg m-2 yr-1. While sediment carbon stocks were generally higher in the eelgrass meadows relative to non-vegetated reference sites, carbons stocks averaged 1343 ± 482 g Corg m-2, substantially less than global averages. These carbon results confirm that eelgrass does contribute to carbon storage in Clayoquot Sound but at lower rates than identified for more tropical seagrasses. By improving the quantification of site-specific carbon dynamics, eelgrass’ role in climate change mitigation and conservation planning can be assessed.

Understanding the effects of different social data on selecting priority conservation areas.

Conservation success is contingent on assessing social and environmental factors so that cost-effective implementation of strategies and actions can be placed in a broad social-ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land-use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial-prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land-use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2-51% different from those based on biological data alone. The inclusion of conservation-compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.

A reliance on agricultural land values in conservation planning alters the spatial distribution of priorities and overestimates the acquisition costs of protected areas

A common focus for conservation planning is to identify locations for siting potential protected areas, something that requires estimates for the costs of setting up these areas and benefits for biodiversity of doing so. When cost data are not available over relevant scales, conservation planners commonly rely on proxy data that they hope will estimate conservation costs. Here, we assessed how accurately agricultural land values, a commonly used proxy for cost data in conservation planning, estimate the actual acquisition costs of protected areas, focusing on a case study from the central and southern Appalachians. We compared plans based on cost estimates derived from different sources and that involved different levels of spatial aggregation to understand how a reliance on these estimates would impact conservation planning. We found that the average agricultural land value in a county did not accurately predict the acquisition costs of protected areas in that county. This lack of accuracy was a result of choosing agricultural land values as a proxy for acquisition costs, and not spatial averaging. A reliance on agricultural land values risks diverting limited funds for establishing protected areas away from parcels that offer the greatest return-on-investment. It would also lead a conservation organization to overestimate the budget needed to protect a given number of species. Our findings highlight the importance of incorporating data on how much protected areas actually cost in future conservation planning studies.

Targeting climate diversity in conservation planning to build resilience to climate change

Climate change is raising challenging concerns for systematic conservation planning. Are methods based on the current spatial patterns of biodiversity effective given long‐term climate change? Some conservation scientists argue that planning should focus on protecting the abiotic diversity in the landscape, which drives patterns of biological diversity, rather than focusing on the distribution of focal species, which shift in response to climate change. Climate is one important abiotic driver of biodiversity patterns, as different climates host different biological communities and genetic pools. We propose conservation networks that capture the full range of climatic diversity in a region will improve the resilience of biotic communities to climate change compared to networks that do not. In this study we used historical and future hydro‐climate projections from the high resolution Basin Characterization Model to explore the utility of directly targeting climatic diversity in planning. Using the spatial planning tool, Marxan, we designed conservation networks to capture the diversity of climate types, at the regional and sub‐regional scale, and compared them to networks we designed to capture the diversity of vegetation types. By focusing on the Conservation Lands Network (CLN) of the San Francisco Bay Area as a real‐world case study, we compared the potential resilience of networks by examining two factors: the range of climate space captured, and climatic stability to 18 future climates, reflecting different emission scenarios and global climate models. We found that the climate‐based network planned at the sub‐regional scale captured a greater range of climate space and showed higher climatic stability than the vegetation and regional based‐networks. At the same time, differences among network scenarios are small relative to the variance in climate stability across global climate models. Across different projected futures, topographically heterogeneous areas consistently show greater climate stability than homogenous areas. The analysis suggests that utilizing high‐resolution climate and hydrological data in conservation planning improves the likely resilience of biodiversity to climate change. We used these analyses to suggest new conservation priorities for the San Francisco Bay Area.

Phylogeny, extinction and conservation: embracing uncertainties in a time of urgency.

Evolutionary studies have played a fundamental role in our understanding of life, but until recently, they had only a relatively modest involvement in addressing conservation issues. The main goal of the present discussion meeting issue is to offer a platform to present the available methods allowing the integration of phylogenetic and extinction risk data in conservation planning. Here, we identify the main knowledge gaps in biodiversity science, which include incomplete sampling, reconstruction biases in phylogenetic analyses, partly known species distribution ranges, and the difficulty in producing conservation assessments for all known species, not to mention that much of the effective biological diversity remains to be discovered. Given the impact that human activities have on biodiversity and the urgency with which we need to address these issues, imperfect assumptions need to be sanctioned and surrogates used in the race to salvage as much as possible of our natural and evolutionary heritage. We discuss some aspects of the uncertainties found in biodiversity science, such as the ideal surrogates for biodiversity, the gaps in our knowledge and the numerous available phylogenetic diversity-based methods. We also introduce a series of cases studies that demonstrate how evolutionary biology can effectively contribute to biodiversity conservation science.

Is PPGIS good enough? An empirical evaluation of the quality of PPGIS crowd-sourced spatial data for conservation planning

A significant barrier to the use of public participation GIS (PPGIS) and crowd-sourcing for conservation planning is uncertainty about the quality of the spatial data generated. This study examines the quality of PPGIS data for use in conservation planning. We evaluate two dimensions of spatial data quality, positional accuracy and data completeness using empirical PPGIS data from a statewide study of public lands in Victoria, Australia. Using an expert-derived spatial conservation model for Victoria as a benchmark, we quantify the performance of a crowd-sourced public in their capacity to accurately and comprehensively identify areas of high conservation importance in the PPGIS process. About 70% of PPGIS points that identified biological/conservation values were spatially coincident (position accurate) with modeled areas of high conservation importance, with greater accuracy associated with locations in existing protected areas. PPGIS data had less positional accuracy when participants identified biological values in urban areas and on non-public lands in general. The PPGIS process did not comprehensively identify all the largest, contiguous areas of high conservation importance in the state, missing about 20% of areas, primarily on small public land units in less densely populated regions of the state. Preferences for increased conservation/protection were over-represented in areas proximate to the Melbourne urban area and under-represented in more remote statewide locations. Our results indicate that if PPGIS data is to be integrated into spatial models for conservation planning, it is important to account for the spatial accuracy and completeness limitations identified in this study (i.e., urban areas, non-public lands, and smaller remote locations). The spatial accuracy and completeness of PPGIS data in this study suggests spatial data quality may be “good enough” to complement biological data in conservation planning but perhaps not good enough to overcome the mistrust associated with crowd-sourced knowledge. Recommendations to improve PPGIS data quality for prospective conservation planning applications are discussed.

Characterizing spatial uncertainty when integrating social data in conservation planning.

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches.

Assessing the risks and opportunities of presence‐only data for conservation planning

AbstractAimPresence‐only data represent a significant source of information for quantifying biodiversity distributions and provide opportunities for use in conservation planning. The large databases of presence‐only records that are available and the lower cost of acquisition could help overcome the traditional problem of lack of data for conservation. However, there are risks associated with the use of presence‐only data inherent with the lack of true absences that might cause omission errors (species are erroneously thought to be absent) and loss of efficiency (more areas are thought to be necessary than needed). These errors could constrain the economic viability of conservation plans and thus the success of conservation practice. We therefore evaluated the opportunities and risks of using presence‐only data for conservation planning.LocationNorthern Australia.MethodsThe effects of using two different types (presence‐only and presence–absence) and different quantities of data were simulated by building predictive models on different subsets of data with increasing numbers of presence–absence or presence‐only records or a combination of both, for 80 freshwater fish species. We then compared the performance of conservation planning outcomes with the best information attainable (a true model built on the complete set of presence–absence data). We measured omission and commission errors in conservation planning outcomes, and the efficiency of and return on the investment in data acquisition.ResultsIncluding presence‐only data helped reduce commission and omission errors in conservation planning outcomes, but only when used in combination with at least some presence–absence data. The use of just a large quantity of presence‐only data resulted in significant reductions in the efficiency of conservation planning outcomes, as more areas than actually needed were required to achieve conservation targets. This reduction in efficiency was mainly related to inflated omission errors.Main conclusionsWe recommend using presence‐only data cautiously if this is the only source of data available; whenever possible, presence‐only data should be complemented with presence–absence data.

Putting people on the map through an approach that integrates social data in conservation planning.

Conservation planning is integral to strategic and effective operations of conservation organizations. Drawing upon biological sciences, conservation planning has historically made limited use of social data. We offer an approach for integrating data on social well-being into conservation planning that captures and places into context the spatial patterns and trends in human needs and capacities. This hierarchical approach provides a nested framework for characterizing and mapping data on social well-being in 5 domains: economic well-being, health, political empowerment, education, and culture. These 5 domains each have multiple attributes; each attribute may be characterized by one or more indicators. Through existing or novel data that display spatial and temporal heterogeneity in social well-being, conservation scientists, planners, and decision makers may measure, benchmark, map, and integrate these data within conservation planning processes. Selecting indicators and integrating these data into conservation planning is an iterative, participatory process tailored to the local context and planning goals. Social well-being data complement biophysical and threat-oriented social data within conservation planning processes to inform decisions regarding where and how to conserve biodiversity, provide a structure for exploring socioecological relationships, and to foster adaptive management. Building upon existing conservation planning methods and insights from multiple disciplines, this approach to putting people on the map can readily merge with current planning practices to facilitate more rigorous decision making.

Global agricultural expansion and carnivore conservation biogeography

Global conservation prioritization must address conflicting land uses. We tested for spatial congruence between agricultural expansion in the 21st century and priority areas for carnivore conservation worldwide. We evaluated how including agricultural expansion data in conservation planning reduces such congruence and estimated the consequences of such an approach for the performance of resulting priority area networks. We investigated the correlation between projections of agricultural expansion and the solutions of global spatial prioritizations for carnivore conservation through the implementation of different goals: (1) purely maximizing species representation and (2) representing species while avoiding sites under high pressure for agriculture expansion. We also evaluated the performance of conservation solutions based on species’ representation and their spatial congruence with established global prioritization schemes. Priority areas for carnivore conservation were spatially correlated with future agricultural distribution and were more similar to global conservation schemes with high vulnerability. Incorporating future agricultural expansion in the site selection process substantially reduced spatial correlation with agriculture, resulting in a spatial solution more similar to global conservation schemes with low vulnerability. Accounting for agricultural expansion resulted in a lower representation of species, as the average proportion of the range represented reduced from 58% to 32%. We propose that priorities for carnivore conservation could be integrated into a strategy that concentrates different conservation actions towards areas where they are likely to be more effective regarding agricultural expansion.

Usefulness of species range polygons for predicting local primate occurrences in southeastern Peru

AbstractSpecies distribution maps are widely used in predicting areas of conservation concern, particularly where species distributions are poorly known. However, the accuracy of range maps for regional/local planning is questionable. We compared published putative geographic range polygons of ten primate species to their actual occupancy at 23 survey sites in southeastern Peru to assess the fine‐scale accuracy of these polygons for regional conservation planning. We analyzed the proportion of sites at which each species was detected, both inside and outside of its published NatureServe [Patterson et al., Digital distribution maps of the mammals of the western hemisphere. Version 1.0. Arlington, VA, 2003] and IUCN [2008; Red List, 2008] range polygons. There were mismatches between our line‐transect survey data and range polygon boundaries for nine of the ten species (from 15 to 80% cases), including both false presences and false absences. Each published dataset overestimated the presence of seven primate species and the absence of four species, though errors varied among species. Occupancy patterns of species with larger geographic ranges were no more accurately predicted than those of more narrow‐range species. Regional barriers to dispersal, such as rivers, and finer‐scale ecological specialization may limit the applicability of range map polygons to regional‐scale conservation priority setting, even for relatively well‐studied taxa. Despite the risk of errors, range polygons are still used as baseline data in conservation planning. We suggest some measures that could reduce the error risk. Am. J. Primatol. 73:53–61, 2011. © 2009 Wiley‐Liss, Inc.

Conservation Planning when Costs Are Uncertain

Spatially explicit information on the financial costs of conservation actions can improve the ability of conservation planning to achieve ecological and economic objectives, but the magnitude of this improvement may depend on the accuracy of the cost estimates. Data on costs of conservation actions are inherently uncertain. For example, the cost of purchasing a property for addition to a protected-area network depends on the individual landholder's preferences, values, and aspirations, all of which vary in space and time, and the effect of this uncertainty on the conservation priority of a site is relatively untested. We investigated the sensitivity of the conservation priority of sites to uncertainty in cost estimates. We explored scenarios for expanding (four-fold) the protected-area network in Queensland, Australia to represent a range of vegetation types, species, and abiotic environments, while minimizing the cost of purchasing new properties. We estimated property costs for 17, 790 10 × 10 km sites with data on unimproved land values. We systematically changed property costs and noted how these changes affected conservation priority of a site. The sensitivity of the priority of a site to changes in cost data was largely dependent on a site's importance for meeting conservation targets. Sites that were essential or unimportant for meeting targets maintained high or low priorities, respectively, regardless of cost estimates. Sites of intermediate conservation priority were sensitive to property costs and represented the best option for efficiency gains, especially if they could be purchased at a lower price than anticipated. Thus, uncertainty in cost estimates did not impede the use of cost data in conservation planning, and information on the sensitivity of the conservation priority of a site to estimates of the price of land can be used to inform strategic conservation planning before the actual price of the land is known.

Diminishing return on investment for biodiversity data in conservation planning

AbstractIt is generally assumed that gathering more data is a good investment for conservation planning. However, the benefits of additional data have seldom been evaluated by analyzing the return on investment. If there are diminishing returns in terms of improved planning, then resources might be better directed toward other actions, depending on their relative costs and benefits. Our aim was to determine the return on investment from spending different amounts on survey data before undertaking a program of implementing new protected areas. We estimated how much protea data is obtained as a function of dollars invested in surveying. We then simulated incremental protection and loss of habitat to determine the benefit of investment in that data on the protection of proteas. We found that, after an investment of only US$100,000 (∼780,000 South Africa Rand [ZAR]), there was little increase in the effectiveness of conservation prioritizations, despite the full data set costing at least 25 times that amount.

Change vector analysis to categorise land cover change processes using the tasselled cap as biophysical indicator

The continuous extraction of wood and the conversion of forest to small- and large-scale agricultural parcels is rapidly changing the land cover of the mount Cameroon region. The changes occur at varying spatial scales most often not more than 2ha for the small-scale subsistence farms and above 10ha for the extensive agricultural plantations of cocoa and palm. Given the importance of land use and land cover data in conservation planning, accurate and efficient techniques to provide up-to-date change information are required. A number of techniques for realising the detection of land cover dynamics using remotely sensed imagery have been formulated, tested and assessed with the results varying with respect to the change scenario under investigation, the information required and the imagery applied. In this study the Change Vector Analysis (CVA) technique was implemented on multitemporal multispectral Landsat data from the Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) sensors to monitor the dynamics of forest change in the mount Cameroon region. CVA was applied to multi-temporal data to compare the differences in the time-trajectory of the tasseled cap greenness and brightness for two successive time periods - 1987 and 2002. The tasseled cap was selected as biophysical indicator because it optimises the data viewing capabilities of vegetation, representing the basic types of land cover - vegetation, soil and water. Classes were created arbitrarily to predict the technique's potential in monitoring forest cover changes in the mount Cameroon region. The efficiency of the technique could not be fully assessed due to the inavailability of sufficient ground truth data. Assessment was based on the establishment of an error matrix of change versus no-change. The overall accuracy was 70%. The technique nevertheless demonstrated immense potentials in monitoring forest cover change dynamics especially when complemented with field studies.

How can you conserve species that haven't been found?

How can you conserve species that haven't been found?

Evaluating habitat as a surrogate for population viability using a spatially explicit population model.

Because data for conservation planning are always limited, surrogates are often substituted for intractable measurements such as species richness or population viability. We examined the ability of habitat quality to act as a surrogate for population performance for both Red-shouldered Hawks (Buteo lineatus) and Northern Goshawks (Accipiter gentilis). We compared simple measures of habitat quality to estimates of population growth rates obtained from a spatially explicit model of population dynamics. We found that habitat quality was a relatively poor predictor of simulated population growth rates for several reasons. First, a relatively small proportion of the potential habitat for each species served as population sources in our simulations--15% for Red-shouldered Hawks and 2% for Goshawks. Second, when habitat quality correctly predicted demographic sources on the landscape, it consistently underestimated the contribution of these areas to the population. In areas where habitat quality correctly anticipated the presence of demographic sinks, we found no useful quantitative relationship between the two measures. Our simulation model captured the influence of habitat quality on the hawk populations, but it also incorporated interactions between dispersing individuals and landscape patterns. Thus, the discrepancies we observed likely reflected the influence of forest fragmentation and the spatial arrangement of forest patches on the populations. We conclude that simple measures of habitat quality will often be poor surrogates for population persistence, but that spatially explicit population models can help inform the development of better indices.

Effectiveness of land classes as surrogates for species in conservation planning for the Cape Floristic Region

Land classes are often used in conservation planning as surrogates for species. The relationship between these surrogates and the distribution of species is usually assumed but rarely tested. Using broad habitat units (BHUs) to represent biodiversity pattern in the Cape Floristic Region, together with point locality data for species (proteas and selected vertebrates), we calculated the effectiveness of BHUs as surrogates for species. Our planning units were grid cells of about 40 km 2, together with boundaries of existing reserves. After assigning conservation targets to BHUs, we derived minimum sets of planning units to meet all targets and calculated irreplaceability values for all units (irreplaceability measures the likelihood of a unit being required to achieve targets). Results showed that BHUs were good surrogates for the majority of protea species, but were not good surrogates for vertebrate species or for a small subset of protea species. These species shared the following characteristics: rarity, limited ranges, Red Data Book status, specialised habitats not defined by BHUs, and distributions driven by historical rather than contemporary ecological factors. We show that targeting land classes and species simultaneously is a viable option and requires only 0.1–0.8% more land (depending on species targets) than targeting land classes alone. We conclude by recommending two different strategies for combining land class and species data in conservation planning, depending on data availability.