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Volume 53, Issue 2 p. 317-322
Practitioner's Perspective
Free Access

Optimizing regulatory requirements to aid in the implementation of compensatory mitigation

Kei Sochi

Corresponding Author

Kei Sochi

The Nature Conservancy, 2424 Spruce Street, Boulder, CO, 80302 USA

Correspondence author. E-mail: [email protected]Search for more papers by this author
Joseph Kiesecker

Joseph Kiesecker

The Nature Conservancy, 117 East Mountain Avenue Suite 201, Fort Collins, CO, 80524 USA

Search for more papers by this author
First published: 24 November 2015
Citations: 10


Governments, companies and conservation organizations seek to minimize the impacts of development through application of the mitigation hierarchy: avoid, minimize, restore and offset (McKenney & Kiesecker 2010). Around the world, policies and performance standards for compensatory mitigation are being strengthened not just to reduce impacts to biodiversity, but to achieve goals for biodiversity that range from “no net loss” to “net gains” (IFC 2012). Although use of offsets is still in its infancy, offsets are gaining traction globally as a goal of public policy (Madsen et al. 2011; Villarroya, Barros & Kiesecker 2014), corporate practices (Rainey et al. 2014), and lending standards (IFC 2012; Equator Principles 2013). As such, these new policies and standards will be important drivers for companies to improve mitigation practices.

Despite growing support for the use of biodiversity offsets, there are many challenges to establishing offsets as an effective conservation tool, among them uncertainties over achieving restoration success, measuring biodiversity values and understanding time-lags in replacing values impacted (Maron et al. 2012; Regnery et al. 2013). Furthermore, most regulatory frameworks focus on a narrow subset of biodiversity features (McKenney & Kiesecker 2010), and mitigation requirements are not set in motion unless there are impacts to one or more of these focal species or systems, limiting their value towards meeting broader conservation goals.

In 2007, Colorado overhauled its oil and gas regulatory framework to address the rapid rise in applications for drilling permits and the geographic expansion of drilling activity in the state. Among the new rules added to existing Colorado statutes governing development within the state was House Bill 07-1298 (hereafter, the 1298 Rules, see charging the Colorado Oil and Gas Conservation Commission (COGCC) to, among other things, establish a process between oil and gas operators and the state's wildlife agency, Colorado Parks and Wildlife (CPW), to minimize adverse impacts to wildlife resources ( The 1298 Rules laid out a roadmap to avoid and minimize impacts from oil and gas operations and to offset unavoidable impacts. Additionally, the COGCC encouraged a landscape-scale approach for reviewing cumulative impacts from anticipated field development, which offered the potential for expedited regulatory approval for an aggregated set of wells in place of the more time-intensive well-by-well permitting approach.

CPW created maps of important wildlife sites or use areas which operators reviewed as part of developing a Wildlife Mitigation Plan outlining steps to address anticipated wildlife impacts. Species included were limited to those most likely to fall under the purview of a statewide wildlife agency, e.g. bald eagle Haliaeetus leucocephalus nest sites and pronghorn Antilocapra americana, elk Cervus canadensis and mule deer Odocoileus hemionus winter range areas (for a full list of all species regulated under the 1298 Rule, see Species not covered by the 1298 Rule, but likely to be impacted by the proposed development, include many listed as being of greatest conservation concern under the Colorado State Wildlife Action plan, as sensitive species by federal agencies and as being of conservation priority for non-governmental organizations such as The Nature Conservancy (TNC). Here, we offer a case study to illustrate how adherence to one regulatory framework, the 1298 Rules, established in Colorado can be adapted to extend mitigation benefits, specifically compensatory mitigation, to a wider suite of species and ecosystems of conservation interest.

San Juan Basin: a case study in optimizing compensatory mitigation

The project area was defined as part of a Wildlife Mitigation Plan collaboratively developed by BP America Production Company, CPW and TNC to mitigate impacts to wildlife from proposed oil and gas activities in an approximately 55 000-ha development field in the San Juan Basin of south-west Colorado. The boundary of the approximately 561 000-ha project area defined by portions of two elk and mule deer units identified by CPW, is located in a high semi-desert scrub–pinyon–juniper ecosystem that provides critical habitat for migratory big game, songbirds, and raptors within the San Juan Basin Ecosystem (see elk units E30/E31 and deer units D30/D52 – The area is characterized by semi-desert scrub that gives way gradually to pinyon–juniper cover in the foothills. The majority of current and likely future oil and gas development impacts is clustered within the lower elevation areas in south-east La Plata County within the Animas, Florida, and Los Pinos River watersheds. Some of the area's largest herds of large game species (elk and mule deer) winter here and rely on the valley's forage to get them through harsh winter weather. Migratory pathways lace the area, connecting the winter range with alpine terrain in nearby mountain ranges. This area has also been home to active oil and gas development for over 50 years. As wildlife in the field has already incurred significant impacts from previous development, off-site mitigation was considered an appropriate tool for the proposed development.

Selecting conservation targets & setting offset goals

In this case study, we propose an approach to meeting regulatory requirements for offsetting impacts while providing co-benefits to species and ecosystems of conservation interest that are not explicitly covered by the 1298 Rules. We used spatial data designated by CPW to identify important habitat areas for regulated species. We solicited input on additional species and habitats of conservation interest from stakeholders from local, state and federal public agencies. We reviewed the Colorado Natural Heritage Program species occurrence data base, ecoregional planning assessments conducted by The Nature Conservancy, and the Colorado State Wildlife Action Plan. A final review of the draft target list and distribution maps by a team of CPW wildlife biologists, BP representatives, and TNC ecologists narrowed the focus to 19 targets overall (Table 1 and S1, Supporting Information).

Table 1. Conservation goals and results of regulatory run and extended conservation run solutions (in hectares or metres)
Amount impacted Amount held in regulatory run Goals met in regulatory run? Amount held in extended conservation run Goals met in extended conservation run?
1298 Species
1a. Bald Eagle nest (RSO)a 19 ha 50 Yes 39 Yes
1b. Bald Eagle nest (SWH)b 68 ha 100 Yes 123 Yes
2. Bald Eagle Winter roost (SWH)b 129 ha 128 Yes 140 Yes
3a. Golden Eagle nest (RSO)a 36 ha 37 Yes 47 Yes
3b. Golden Eagle nest (SWH)b 111 ha 159 Yes 160 Yes
4. Osprey nest (RSO)a 29 ha 38 Yes 50 Yes
5. Elk winter concentration area 18 829 ha 36 756 Yes 28 820 Yes
6. Mule Deer critical winter range 42 169 ha 42 178 Yes 42 172 Yes
Additional conservation species & systems
7. Lazuli bunting 12 370 ha 2548 No 12 236 Yes
8. Bluehead Sucker 90 510 m 774 055 Yes 3 911 846 Yes
9. Flannelmouth Sucker 61 316 m 637 302 Yes 2 908 202 Yes
10. Roundtail Chub 39 955 m 619 086 Yes 2 549 684 Yes
11. Gunnison Prairie Dog (colonies) 856 ha 138 No 979 Yes
12. Riparian & wetlands 2040 ha 1520 No 3780 Yes
13. Rocky Mountain Ponderosa Pine Woodland 4184 ha 16 767 Yes 5727 Yes
14. Colorado Plateau Pinyon Juniper Woodland 16 403 ha 9732 No 16 426 Yes
15. Rocky Mountain Gambel Oak Mixed Montane Shrubland 1585 ha 6598 Yes 3212 Yes
16. Intermountain Basins Sagebrush Shrubland 4021 ha 5229 Yes 7371 Yes
17. Intermountain Basins Semi-desert Shrub Steppe 743 ha 1216 Yes 1740 Yes
18. Intermountain Basins Semi-desert Grassland 119 ha 209 Yes 350 Yes
19. Rocky Mountain Lower Montane Riparian Woodland and Shrubland 543 ha 702 Yes 2545 Yes
  • a RSO (Restricted Surface Occupancy) area designations are places which warrant avoidance to the maximum extent feasible. RSO nest sites represent a 0.25 mile (402 m) buffer zone around active nests. See for definition of what a RSO area is.
  • b SWH (Sensitive Wildlife Habitat) designations are places where timing limitation or other actions in minimizing impacts are warranted. Here, SWH nest sites represent a 0.5 mile (805 m) buffer zone around active nests.

We used a simplified approach to calculate the extent to which species (delineated by CPW as buffered nest sites, important concentration or wintering areas) and other terrestrial ecosystems might be impacted by future development in the field using parameters stipulated by CPW (for further thinking on the challenges of how to measure development impacts, see Jones et al. 2014)). We considered 723 existing wells where infill development could occur, applied a 0.81 ha (2 acres) footprint for well sites and a 500-m buffer for indirect impacts for a total development footprint of 48 849 ha (Hebblewhite 2008). Following Kiesecker et al. (2009), we overlaid the spatial data for the regulatory species and the additional conservation targets over this footprint and used the size of the resulting overlap as our offset goals (see Table 1 for amount impacted by individual conservation target and data format used). In setting the amount of offsets needed, practitioners may want to additionally consider the use of multipliers to take into account uncertainty in measuring development impacts or in representations of species targets.

Conservation planning principles guide compensatory mitigation optimization

We examined two offset site selection runs – a set of offset sites focused on the regulated species alone (“regulatory run”) and a set of offset sites focused on regulated species with additional conservation targets (“extended conservation run”). We used a decision-support software called Marxan with Zones (Watts et al. 2009) to identify potential offset sites for each scenario. Marxan with Zones is more generally used to identify solutions for meeting multiple management objectives while optimizing trade-offs between mitigation goals and “costs” (e.g. land acquisition, landscape condition). Although we were dealing with only one zone (private land protection as an offset strategy), we chose Marxan with Zones in order to utilize the functionality within the program to constrain the areal solution of the extended conservation run to that of the regulatory run. That is, we forced the offset solution for the extended conservation run to meet its conservation goals within the same area as the regulatory run. This was done on the assumption that extending benefits that accrue to areas of importance for regulatory species to additional conservation targets not explicitly covered by existing legal frameworks by merely increasing the total area of offset mitigation required would be unpalatable to any operator bearing the costs of implementing offset actions. It must, minimally, be accomplished with no additional cost (here, represented simply as area) compared to fulfilling offset obligations to regulated species alone.

We set additional rules within Marxan to help guide the offset site selection process. Chief among them was the inclusion of a disturbance index summing weighted raster layers of impacts (roads, mines, oil and gas wells, pipelines, transmission lines, developed lands) to the landscape as an indirect measure of relative ecological integrity. The disturbance index is incorporated in Marxan to optimize site selection for occurrences in good condition by attempting to minimize the overall disturbance index score for different solutions. We adjusted additional Marxan parameters including the boundary length modifier (0.015 and 0.075 for regulatory and extended runs respectively) to promote solutions which clustered the selected sites to avoid an overly splintered network of potential sites that resulting in a less desirable fragmentation of important wildlife habitat. Finally, we introduced a species penalty factor (set to 2 for regulatory species, 1 for all others) in the extended conservation run to ensure that offset solutions did not prioritize meeting conservation goals for non-regulatory species at the expense of required offset goals for regulatory species.

Optimized offset outcomes

When we focused on meeting goals only for regulatory species, we selected 428 000 ha that were consistent with our offset goals. However, in this solution set we were unable to meet offset goals for several non-regulatory targets (Table 1). There were some notable misses – in particular, the Gunnison's prairie dog Cynomys gunnisoni, which was designated a species of greatest conservation concern in Colorado's State Wildlife Action Plan. In addition, riparian wetlands and Colorado Plateau pinyon–juniper woodland habitats did not meet impact goals in the regulatory analysis. For the Gunnison's prairie dog, the regulatory run would only offset approximately 16% of the potential conflicts with the proposed development footprint.

When we explicitly included offset goals for our additional conservation species and systems, we were able to find an alternative offset solution set that met goals for all species. 40% of the extent of this set of offset sites overlapped with the regulatory run solution (Fig. 1). Every planning unit included in this offset solution held a regulated species, ensuring that conservation actions will always be directed at 1298 species. Importantly, this regulatory run and extended conservation run solution did so without increasing the total area of the offset sites.

Details are in the caption following the image
Offset solutions for regulatory and extended conservation runs.

Whilst taking this approach can steer choices to places that encompass a more inclusive suite of biological targets, it can also inadvertently lead to places of lower quality. Practitioners need to have metrics to compare the relative exchangeability of the species and habitats impacted and proposed offset areas as well as trade-offs for offset selections for the regulatory species. In our case, we compared the disturbance index across both solutions, and we found a minimal increase in the overall index score. While the index does not in and of itself tell us whether a planning unit has exceeded a threshold of ecological integrity, it can be viewed as a coarse proxy for relative landscape condition and thus, the likelihood of successful conservation action. Higher disturbance scores can also mean higher conservation costs (e.g. additional management costs). Practitioners would have to be cautious about the economic trade-offs that may be associated with alternative offset selections and may wish to more explicitly integrate additional considerations into offset selection (Naidoo et al. 2006). Restricting area and minimizing total disturbance values is but a first step in comparing alternative solution sets. For example, incorporating land acquisition costs could result in larger offset solution sets without a concomitant increase in economic costs borne.

Land ownership patterns between the two solutions also differed but the implication of the trade-offs are more mixed. In the regulatory run solution, approximately 14% of the offsets were on publicly managed lands, whereas 3% of the area was in the extended conservation run solution. There was a concurrent increase in the amount of privately owned and managed lands in the extended conservation run solution. The particular mix of land ownership patterns and its implications for where biodiversity values are situated needs to be carefully considered. In this case, given CPW's focus on land protection as the primary offset strategy and the current management focus of the public lands in the project for wilderness and wildlife values, increasing the amount of private land in the solution may provide better opportunities to facilitate implementation.

Making the case for the next generation of mitigation

Mitigation addresses the impacts of development through the application of the mitigation hierarchy: avoid, minimize, restore, and offset. However, there are many problems with how mitigation is currently applied (Kiesecker et al. 2010). Governments typically grant exploration leases across large regions without funding the necessary mitigation planning (Evans & Kiesecker 2014). In light of the shortcomings of past mitigation frameworks, offset programs are beginning to recognize the need to move away from such piecemeal mitigation, which often results in a patchwork of isolated, degraded and difficult-to-manage habitats, to an approach that is more ecologically relevant in scale and more comprehensive in accounting for cumulative impacts affecting an entire region. For example, compensatory mitigation in the USA is now required to use a landscape-scale approach to identify and facilitate investment in key regional conservation priorities and to ensure early integration of mitigation considerations in project planning and design (Hayes 2014). Other countries are following this trend. In Colombia, new mitigation regulations (Resolution 1517 of 2012) require that both the amount and location of compensation are based on a series of landscape features, e.g. size, condition and landscape context (Saenz et al. 2013). This scaling up of mitigation is expected to provide more effective conservation outcomes, reduce regulatory hurdles and potentially offer cost savings to private developers (Kiesecker et al. 2010).

This shift provides a unique opportunity to move mitigation from a reactive tool focused primarily on threatened and endangered systems and species (Pilgrim et al. 2013) to one that supports the full suite of conservation priorities in a region (Kiesecker et al. 2010, 2011; Regnery et al. 2013). At present, conservation laws in general and mitigation frameworks in particular are too narrowly focused on threatened and endangered species and habitats, and miss opportunities to provide more general value. In the absence of regulatory requirements that encompass a wider spectrum of biodiversity interests, optimization approaches applied at a landscape scale like we have presented here can be harnessed to extend benefits.

Companies, wildlife resource managers, and conservation organizations stand to benefit from such an approach. Companies that have made commitments to no net loss of biodiversity can use this type of approach to adhere to required regulations while meeting voluntary commitments. In doing so, extending dollars invested in mandated mitigation action to include non-regulatory species and habitats. Similarly, resource managers and conservation organizations working within the limits of mitigation rules that narrowly focus on rare and imperilled species can optimize the implementation of required mitigation action to places that more favourably benefit a broader suite of conservation targets without expanding existing regulatory scopes. The benefits and trade-offs of alternative offset solutions can become a starting point for discussions among interested stakeholders about how to manage and prioritize conservation actions and investments in a particular landscape.

Offsets are intended to provide an additional tool to mitigate impacts after efforts have been undertaken to avoid and minimize impacts. For offsets to be effective, they should be new and additional contributions to conservation and should move beyond simple acreage comparisons to include balancing ecological quality as well (Quétier & Lavorel 2011; Overton, Stephens & Ferrier 2013). To ensure that trade-offs of project impacts for offset benefits are balanced and sufficient, we will need to develop an appropriate currency (i.e. area, habitat quality) to more effectively and transparently quantify these exchanges (Gardner et al. 2013; Maron, Rhodes & Gibbons 2013; Bull et al. 2014). The framework we develop here is the first step in an offset process wherein we select a set of sites that have been valued for their ability to meet the biologically based offset goals within a broader landscape context.

In the San Juan Basin project, we made several simplifying assumptions due primarily to the stipulations set by the regulating agency, the CPW. These included the calculation of the extent of potential development impacts, the spatial representation of regulated species, the focus of offset actions on land protection, and the a priori determination that offsets themselves were possible and appropriate. As on-the-ground projects are considered, practitioners will want to establish, among other things, a finer scale currency that incorporates values associated with losses and gains in ecological functions (e.g. connectivity and minimum size requirements), quality, integrity and uncertainty in the effectiveness of subsequent offset actions (Moilanen et al. 2009; Bull et al. 2014).


Balancing growing development demands with biodiversity conservation necessitates a shift from business as usual. Offsets represent an opportunity for mobilizing billions of dollars for conservation (McKenney & Kiesecker 2010). If we ensure that development is consistent with broader conservation goals, avoiding impacts where they are not, then development can fuel significant conservation outcomes. The results of our analysis illustrate how offset benefits can be extended to components of biodiversity not explicitly covered by current regulations. This will be important as the USA moves to exploit more of its domestic energy resources (McDonald et al. 2009; Kiesecker et al. 2011)and attempts to ensure early integration of mitigation considerations in project planning and design (Hayes 2014; Presidential Executive Order 13604 2012). Lessons learned, such as the benefits of taking a landscape-level approach and understanding the offset options and trade-offs within a landscape (e.g. optimizing offset selections within the confines of a regulatory framework), could have implications for designing development that's compatible with nature and human well-being and that proactively reduces conflicts for both biodiversity conservation and business.


We thank our colleagues and partners who participated in the San Juan basin wildlife mitigation planning process. We are grateful for the contributions of Megan Kram (The Nature Conservancy), Jon Holst, Tony Gurzick, Pat Dorsey, Gary Skiba, Brian Magee, and Chris Woodward (Colorado Parks and Wildlife), and Dave Brown, Andy Hawk, and Lindy Hanson (BP America Production). We also thank Mike Gordon for helpful discussions. Funding for this project was provided by BP Production America, The Nature Conservancy and the Anne Ray Charitable Trust.

    Data accessibility

    Spatial data (species, planning units, project area): Dryad Digital Repository doi:10.5061/dryad.4n86v (Sochi & Kiesecker 2015).

    Contact information for access to restricted species location data, and riparian and wetlands habitat information is given in Table S1 in Supporting Information.


      Kei Sochi is a spatial ecologist for the Development by Design team at The Nature Conservancy and her work focuses on the impacts of development on biodiversity in Indonesia, Brazil and the western USA. Joseph Kiesecker, Ph.D., is lead scientist for The Nature Conservancy's Global Conservation Lands Program. In his work, he seeks to improve the siting and mitigation of energy, mining, and infrastructure development through the use of predictive modelling and landscape planning.