Native American (American Indian)/Alaska Natives (AI/AN) are significantly underrepresented in the U.S. federal science and engineering (S&E) labor force. This underrepresentation extends into the leadership ranks of federal agencies responsible for designing, implementing, and maintaining resource monitoring and enforcement programs on tribal lands. Datasets documenting demographics and salaries of the federal S&E workforce show AI/AN are the smallest S&E workforce segment among minorities and receive the lowest average salaries for engineers and physical scientists. Academic statistics show AI/AN students earn significantly fewer engineering and Earth, atmospheric, and ocean science (EA&OS) bachelor's degrees than other ethnic groups and rarely earn advanced degrees in these disciplines. Additional aspects in federal and academic datasets offer clues on a spectrum of causative factors affecting the AI/AN recruitment pool for federal S&E jobs and the rarity of AI/AN ascending to leadership positions with federal scientific organizations.
The absence of indigenous leaders in federal science and engineering organizations inherently limits effective infusion of traditional ecological knowledge into our nation's sustainability programs
The long-term knowledge of weather extremes and successful adaptation strategies of indigenous people have largely been ignored in ecosystem sustainability planning. Their oral history and artwork provide a vast archive of Earth's climate cycle and extreme events prior to colonization. Traditional knowledge passed from generation to generation of indigenous people of the United States and its territories contains invaluable lessons to adapt to social and environmental changes. Today, our nation's indigenous people continue to persevere in some of our world's harshest climates.
Ignoring the rights of indigenous communities and tribal governments is prevalent in the decision-making process for the development and regulation of natural resources (Robyn 2011; Krakoff 2008; Deloria 1985). Native Americans have ownership rights to natural resources in their homelands and areas to which they were relocated. These rights, established when Native Americans ceded millions of acres of land in the forcible removal from their homelands by the U.S. government, are codified by treaties and a federal trust relationship between the U.S. and tribal governments (Anderson 2010; Echo-Hawk 2010; Zellmer 1998). Creating a sustainable cadre of indigenous environmental leaders provides a pathway for an essential group of disenfranchised resource owners to provide their unique perspective to complex resource management issues. Their absence in discussions that ultimately determine the sustainable management of natural resources including water, oil, natural gas, forests, minerals, and fisheries continues to cost the U.S. billions of dollars in litigation costs and settlements (Indian Trust Settlement et al. 2013; Bark and Jacobs 2009; Nordhaus et al. 2003).
In 2002, President of the National Academy of Engineering William Wulf wrote, “As a consequence of a lack of diversity, we pay an opportunity cost, a cost in designs not thought of, in solutions not produced” (NRC 2002, 8–14). Across our nation's resource management programs we are failing to incorporate the diversity of Native Americans/American Indians (AI) and Alaska Natives (AN).1 Their experiences encompass traditional knowledge of ecosystems, cultural connectivity to weather and water systems, and ownership of natural resources (Semken et al. 2007; Hammer 2002). Other disciplines, especially medicine, have realized the value of ethnic perspectives and the peril of excluding indigenous viewpoints (Smith et al. 2009; Acosta and Olsen 2006; Weiss et al. 1980). Ignoring the AI/AN worldview of a synergistic relationship of humans and nature allows only “traditional western” science solutions to be brought to bear on complex environmental problems. The absence of AI/AN scientists and engineers prevents inclusion of unique perspectives to examine critical ecosystem issues (i.e., water resources) threatening national security, human health, and economic development. As Wulf stated, “Without diversity, the life experiences we bring to an engineering problem are limited. As a consequence, we may not find the best engineering solution. We may not find the elegant engineering solution.” (National Research Council 2002, 8–9).
Documenting the participation of AI/AN in academic programs and the federal workforce is complicated (Faircloth and Tippeconnic 2010). The frequency of data collection efforts and the methodologies used to derive statistics on AI participation are not uniform. Some data collection sets combine AI with AN while others keep the groups separate. The lack of a cohesive dataset to track each group's participation in academic programs, degrees awarded, and federal workforce demographics over multiple decades presents challenges in looking at trends that affect each group exclusively.
With these caveats stated, there are several key factors limiting opportunities for federal science and engineering (S&E) organizations to recruit and retain talented indigenous people as leaders of their organizations. Demographics of the leaders and leadership teams of governmental task forces, strategic initiatives, and regulatory committees show very few AI/AN scientists are actively involved. Data outlining AI/AN academic participation and degree attainment show AI/AN have the lowest levels of participation in Earth, atmospheric, and ocean science (EA&OS) and engineering disciplines at 4-yr academic institutions, seldom earn their bachelor's degrees, and rarely earn advanced degrees (National Science Foundation 2009, 2011, 2012,National Science Foundation 2013). Other minority groups have targeted this issue and are overcoming this obstacle (National Research Council 2013,National Research Council 2011,National Research Council 2002). AI/AN under-representation in terms of workforce numbers and level of authority within a federal agency or on interagency regulatory teams directly affects our nation's ability to leverage traditional ecological knowledge and engage tribal resources to cope with weather and water extremes (Semken et al. 2007; Hammer 2002).
S&E FEDERAL WORKFORCE AND SENIOR EXECUTIVE SERVICE AI/AN PARTICIPATION AND SALARY FACTORS.
From 2000 to 2009 the percentage of AI/AN in the U.S. federal S&E workforce remained flat at 0.9% (U.S. Office of Personnel Management 2010; Burelli and Falkenheim 2011; NSF 2009)—well below their representation in the U.S. population of 1.7% (U.S. Census 2013). This trend was not true for other minority groups as their representation in the federal S&E workforce steadily increased (Table 1). In 2009, only 2,080 employees in the total federal S&E workforce self-identified as AI/AN (1,422 male and 658 female) representing only 0.1% of the total female and only 0.8% of the male federal S&E workforce (Burrelli and Falkenheim 2011). Compared to other underrepresented groups, there were over 10 times more Asian/Pacific Islanders (21,509), 9 times more blacks (18,229), and 5 times more Hispanics (10,258) than AI/AN S&E professionals in the 2009 federal S&E workforce.
Focusing on weather and water forecasting, management, and research, the federal government employs 27% of hydrologists and 34% of atmospheric scientists in the United States (U.S. Department of Labor, Bureau of Labor Statistics 2013). Eight agencies employ the majority of federal hydrologists, atmospheric scientists, and related technical staffs. Across these agencies there is significant S&E hiring disparity between AI/AN and other ethnic groups (Table 2). The Department of Defense (DoD) reported the largest S&E workforce (102,170). The Department of Homeland Security had the smallest S&E workforce (4,764) (Burrelli and Falkenheim 2011). A large S&E workforce does not directly correlate to high numbers of AI/AN. In 2009, the Department of Commerce (DoC) reported the fourth largest workforce (11,748) yet ranked last in AI/AN workforce representation. Only 0.3% of the DoC S&E workforce (40 out of 11,478) self-identified as AI/AN. The largest S&E employer, DoD, reported just 0.5% of its S&E workforce as AI/AN. The Department of Interior (DoI), the third largest S&E employer, reported the highest percentage of AI/AN—3% of its 14,672 S&E workforce—primarily owing to the Bureau of Indian Affairs' large AI/AN workforce.
The level of AI/AN underrepresentation in the federal S&E workforce severely limits the recruitment pool for federal S&E leadership positions particularly those in the senior executive service (SES). Nonpolitical appointees leading federal departments and agencies are most likely SES. The U.S. Office of Personnel Management (OPM) defines an SES position as “A position in the executive branch that is qualified above GS-15 or is in level IV or V of the Executive Schedule.” (U.S. Office of Personnel Management 2013; www.opm.gov/policy-data-oversight/senior-executive-service/reference-materials/) To qualify for SES positions, candidates must have one year of satisfactory performance at one grade (i.e., GS-14) or pay band (i.e., level III or IV) lower than the SES position for which they are appying. Tables 3 and 4 present data from OPM's 2006 Demographic Profile of the Federal Workforce to examine race and gender characteristics of those employed at a GS-14 or pay band III or IV level.
For the “1340 meteorology series,” there were 2,975 federal meteorologists in 2006 with only 6 being AI/AN. The 6 AI/AN meteorologists earned $14,221 below the $85,780 average salary paid to other series 1340 employees. Their average salary of $71,559 equated to a GS12 step 5 level in 2006 two grades below the GS14 level requirement for SES eligibility. In contrast, the average salary of $91,260 for the 61 meteorologists self-identifying as black equated to a GS14 step 2 salary level indicating a high likelihood that at least half have positions meeting the SES time-in-grade requirement. For other minority groups, the median salaries for Asians and Hispanics equated to GS14 step 3 and GS14 14 step 1, respectively.
With the exception of “geography series 0150,” the OPM study showed AI/AN workers made less than the average salary for all the occupations listed in Table 4. Environmental engineering, series 0819, showed the most significant disparity between AI/AN wages and the average national federal salary. In series 0819, the average salary for the 48 AI/AN employees was $15,981 less than the average salary paid to the other 4,636 federal environmental engineers. There is not a classification listed in Table 4 where the average salary for AI/AN professionals was above the 2006 GS14 step 1 salary of $87,533.
These datasets show the limited number of AI/AN in the federal government physical science and engineering workforce are likely working in positions that do not qualify them for senior management or SES. This severely constrains the AI/AN recruitment pool for SES as the demographics show. In the SES, AI/AN are relatively rare—especially females (Table 5). In 2009, there were 1,483 S&E professionals in SES positions (Burelli and Falkenheim 2011) with only 12% from ethnic classifications other than whites. Only 9 AI/AN held SES positions in 2009—6 male and 3 female—just 0.6% of the total S&E leadership positions in the federal government. Of the 1,191 SES S&E positions held by males, 89% of the 1,191 positions were filled by whites. Of the 292 SES S&E positions held by females, 83% were filled by whites.
AI/AN UNDERGRADUATE ENROLLMENT AND S&E DEGREES CONFERRED (2001 TO 2009).
Federal workforce numbers show few AI/AN in the federal S&E ranks. Acknowledging issues surrounding National Science Foundation (NSF) and U.S. census data on student enrollment and degrees awarded (Faircloth and Tippeconnic 2010), the following datasets document AI/AN degree attainment and illustrate how dire the situation is compared to other minority groups. As the following sections will show, AI/AN enrollment in university engineering and physical science curriculums is very small compared to other ethnic groups. Nationwide, the number of bachelor's degrees awarded to AI/AN in engineering and physical science is negligible and very few persist to earn advanced academic degrees. These factors indicate most AI/AN fail to meet the minimum educational requirements for federal S&E positions. As workforce statistics illustrate, those who do qualify and enter the federal S&E workforce are not successfully competing for GS14 and higher management positions. Academic data show this is most likely due to the lack of AI/AN earning advanced degrees in EA&OS and engineering.
AI/AN university enrollment compared to U.S. population trends.
AI/AN alone or in combination with another race consitute 1.7% of the total U.S. population in 2010 or 5.2 million people just above Native Hawaiian/Pacific Islander—the smallest population group (Norris et al. 2012; U.S. Census Bureau 2013). Population growth rates for AI/AN outpace the overall U.S. population growth rate of 9.7% (Table 6). The multiple-race AI/AN population grew by 39% compared to 18% for those reporting as AI/AN only. The fastest-growing population segments from 2000 to 2010 were blacks and Asians in combination with other races (U.S. Census Bureau 2001U.S. Census Bureau 2013). Despite above normal growth rates, the total population for most minority groups in the United States remained small in comparison to white alone with 223.5 million people (72.4% of total U.S. population) and Hispanic/Latino at 50.4 million people (12.5% of total U.S. population). In the 2000 and 2010 U.S. population, there were approximately 78 times more whites, 17 times more Hispanics/Latinos, 13 times more black only, and 5 times more Asians only than AI/AN.
AI/AN enrollment in academic institutions continued to lag behind AI/AN population growth rates from 2000 to 2010 unlike most other ethnic groups. Among minorities, only AI/AN and Asian enrollment lagged well behind their 2000–10 population growth rates. AI/AN enrollment grew at the same pace, 19%, as the overall U.S. undergraduate enrollment but well behind the 39% population growth rate from 2000 to 2010 of AI/AN in combination with another race. In 2008, AI/AN undergraduate enrollment was just 0.1% (163,816) of the total enrollment (16,570,857) in the United States (NSF 2013). In contrast, Hispanic undergraduate enrollment from 2000 to 2008 increased by 40% (1,522,220 vs 2,129,684) tracking closely with their population growth rate followed by blacks with 32%. Similar to trends in other ethnicities, there were more female AI/AN students (98,372) in 2008 enrolled at academic institutions than males (65,444).
Academic intent of first-year AI university students and gender disparity.
The majors AI/AN students initially choose can ultimately affect their ability to qualify for federal S&E positions as particular curriculums do not include the advanced math and science courses specified in federal job classifications for hydrologists, meteorologists, and engineers. In 2008, 34.7% of all freshmen entering academic institutions indicated an intention to major in S&E fields as classified by the NSF Scientists and Engineers Statistical (SESTAT) data system (NSF 2003). The dataset for freshman intent (NSF 2011) does not group AN with AI. Table 7 shows that fewer AI students, 27.6%, indicated intent to major in an S&E field than any other ethnic group (NSF 2011). Looking at engineering exclusively, fulltime AI freshman enrollment in 2008 was almost 100 times lower than the enrollment for whites and 10 times lower than blacks—the second smallest segment in full-time freshman engineering students (Table 8). For the 10-yr period ending in 2008, the enrollment trend for AI in engineering did not grow as fast as those seen in other groups. Trends from 1998 to 2008 show enrollment numbers for AI in engineering programs are essentially flat with AI comprising the smallest ethnic population enrolled in engineering (NSF 2011). The highest number of AI enrolled in engineering occurred in 2008 with 765 full-time students at all U.S. academic institutions, representing only 0.69% of the total full-time student enrollment in engineering nationwide.
There were significant gender disparities among AI freshmen in 2008 for particular S&E disciplines similar to those seen in other ethnic categories. One-third (33.1%) of male AI freshmen indicated an intention to major in an S&E field, while less than one-quarter (22.7%) of AI females intended to major in an S&E field (NSF 2011). Engineering showed the largest disparity in intended majors for female and male AI freshmen. Only 2.6% of freshmen females elected to major in an engineering field compared to 15.2% of AI freshmen males. For freshmen AI females, biological and agricultural science (8.6%) and social and behavioral science (6.8%) fields were the two most popular S&E fields, with other ethnicities showing the same trend.
Examination of AI/AN degree attainment at 2-yr and 4-yr colleges.
Other datasets point to the type of academic institution and level of degree conferred as factors in AI/AN engineering (hydrology) and physical science (meteorology) degree attainment. AI/AN are enrolling in academic institutions, which is commendable. However, the numbers of AI/AN graduating from 2-yr colleges with qualifications for federal engineering, hydrology, or meteorology positions are dismal. Almost half of AI/AN undergraduates were enrolled in 2-yr colleges (Knapp et al. 2012). However, these 2-yr institutions rarely award associate's degrees in EA&OS. Table 9 shows the number of associate's degrees awarded in EA&OS peaked at 82 in 2000, bottomed out in 2004 with 41 awarded, and climbed slightly to 67 associate degrees awarded in 2008 (NSF 2011). For AI/AN, only three associate's degrees in EA&OS were awarded from 2000 to 2008.
The success of degree attainment at 4-yr institutions is also troubling. Nationally from 2000 to 2008, an average of 4,000 EA&OS bachelor's degrees were awarded annually with that number peaking at 4,313 in 2008 (Table 10). Earth science degrees were overwhelmingly the most popular, representing over 80% of EA&OS degrees conferred nationwide followed by atmospheric science (17%) and ocean science (3%). Table 10 shows that AI/AN are practically absent from EA&OS statistics at 4-yr colleges. For the period from 2000 to 2008, AI/AN earned the fewest number of EA&OS bachelor's degrees: 23 in atmospheric science and 11 in ocean science (NSF 2013). In atmospheric science, the highest number of bachelor's degrees occurred in 2001 when 5 out of the 456 bachelor's degrees conferred nationwide were earned by AI/AN. The largest disparity occurred in 2008 when the largest number of bachelor's degrees, 736, was awarded nationally yet only two of these degrees were earned by AI/AN. In ocean science, there were no bachelor's degrees earned by AI/AN in 2004, 2006, or 2008.
Stepping away from EA&OS, engineering degree attainment statistics from 2000 to 2008 showed an increasing number of AI/AN students earned degrees in engineering mirroring national trends (Table 11; NSF 2012). However, AI/AN engineering degree attainment lagged woefully behind those of other minority groups. For most minority groups, the total number of degrees conferred tracked closely with their representation in the overall U.S. population. This is not true for AI/AN. Nationally, the number of bachelor's degrees awarded in engineering rose steadily from 59,487 in 2000 to 68,895 in 2008 (Table 10) with mechanical engineering ranking the highest with 17,683 degrees conferred in 2008. For AI/AN the number of engineering bachelor's degrees dropped significantly from 326 in 2000 to 256 in 2001. Since 2001, the number of engineering bachelor's degrees earned by AI/AN has slowly increased to 344 in 2008—just slightly higher than the 326 total of 2000. In 2008, AI/AN earned only 0.4% of all engineering B.S. degrees awarded in the United States. Looking at other ethnicities in 2008, blacks had the second lowest number of engineering bachelor's degrees, earning only 4% (3,101) of the bachelor's degrees earned nationwide. However, this number is nearly 10 times higher than AI/AN attainment. Among engineering disciplines in 2008, the top three fields for AI/AN bachelor degrees were mechanical (81), electrical (81), and civil (63) where hydrology and hydraulic design are taught.
TRENDS IN GRADUATE DEGREES.
Master's degrees awarded in EA&OS categorized by ethnicity.
The lack of AI/AN earning advanced academic degrees in EA&OS and engineering is a significant obstacle preventing promotion into federal S&E leadership positions. Data contained in NSF reports on women and minorities in S&E show whites earned the majority of master's degrees in atmospheric science from 2000 to 2008 with minorities earning less than 10% of master's degrees in atmospheric science (NSF 2012). Seventy-one percent of EA&OS master's degrees were earned by whites in 2000, increasing to 74% in 2008. Temporary residents represented the second largest master's degree recipients in atmospheric science. In 2000, 24% of master's degrees were earned by temporary residents declining to 13% in 2008.
For AI/AN, from 2000 to 2008 only two master's degrees in atmospheric science were conferred: one in 2005 and one in 2007 (Table 10). In ocean science, three master's degrees were awarded for the 9-yr period with all three conferred in 2001. The percentage of master's degrees awarded to AI/AN in any engineering discipline never rose above 1% for this period. Focusing on water resources engineering, the percentage of master's degrees awarded to AI/AN students in civil engineering with respect to the total number conferred nationwide never exceeded 0.5%. In 2001, only 10 civil engineering master's degrees, or 0.3% of all U.S. degrees conferred, were earned by AI/AN. Recent data (NSF 2013) continues to show it is very rare that AI/AN successfully complete the advanced coursework to compete as applicants for federal leadership positions in water resource engineering, planning, and policy.
Doctoral degrees in EA&OS and engineering.
Considering the numbers of AI/AN earning master's degrees, NSF data for AI/AN doctoral recipients should not be surprising. The statistics show very few AI/AN earn doctorates in EA&OS and engineering further limiting the recruitment pool for AI/AN federal weather and water leaders. More important to the dialogue of how to remedy this disparity, failure to earn doctorate degrees prevents AI/AN from being considered for university EA&OS and engineering faculty positions (DeVoe et al. 2008). NSF statistics tracking the number of EA&OS doctoral degrees awarded nationwide do not separate the individual disciplines. In 2001, 2002, and 2006 there were no EA&OS doctoral degrees conferred to AI/AN (Table 10). Of the 3,357 EA&OS doctoral degrees awarded from 2000 to 2008, only 17 were awarded to AI/AN with 6 awarded to females and 11 to males (NSF 2011).
From 2000 to 2008 in engineering, 23,403 doctoral degrees were awarded nationwide; however, only 77 were earned by AI/AN. With respect to gender, females were awarded 4,951 doctoral degrees in engineering with only 18 of those degrees earned by AI/AN females. For engineering, no doctoral degrees were earned by AI/AN females in 2002 or 2005. In 2004, there was one doctorate earned by an AI/AN nationwide in civil engineering and I believe that was my doctorate conferred by the University of New Orleans. For males, 18,452 doctoral degrees in engineering were awarded nationwide with only 59 of those degrees awarded to AI/AN males for the 9-yr period (NSF 2011).
CRITICAL INTERVENTION POINTS FOR AI /AN STUDENTS.
Academic data show the current educational framework fails to produce sustainable AI/AN representation in the federal S&E workforce and more importantly a critical mass of highly qualified AI/AN leaders of federal S&E programs and agencies. Data outlining household income, education levels, and school resources drive home the fact that a majority of AI/AN students are at risk. Among minorities, AI/AN had the highest national poverty rates from 2007 to 2011 with 27% of the population living in poverty (Macartney et al. 2013). Over half (53%) of AI children in female single-parent households live below the poverty level (Ross et al. 2012).
It is imperative to develop counseling and educational resources for fourth- through sixth-grade AI/AN students when they are deciding whether to enroll in advanced math and science classes. Ross et al. (2012) present data that show the bleak reality of the AI/AN school environment and access to academic mentors and role models. In the United States, almost onethird (31%) of AI/AN students attend a high-poverty school compared to 6% of whites. Only 18% of AI and 16% of AN children between the ages of 6 and 18 lived in households where parents hold a bachelor's or advanced degree. With few college graduate role models and limited household income, aspiring AI/AN students must depend on school resources and counseling services—items that are not equally distributed. In 2009, a survey of school counselors indicated only 29% of counselors serving AI/AN ninth graders reported their primary counseling goal was planning and preparing for postsecondary education in contrast to 60% of Asian, 44% of black, and 41% of Hispanic ninth-grade counselors.
There must be direct interaction with AI/AN students and their families no later than sixth grade. Students arriving at college without “gateway courses” such as physics and calculus are already behind (Akweks et al. 2009). There must be access to tutors, computer laboratories, and summer math and science camps (Riggs et al. 2007; Riggs 2005; Dubiel et al. 1997) for sixth grade AI/AN students choosing to enroll in advanced science and math coursework as statistics show these students are less likely to have these resources at school or home. Without intervention, studies show a majority of AI/AN students will enroll in lower academic tracks where classes are less challenging and teachers are teaching “out of field,” limiting the quality of scientific inquiry and mentoring needed to succeed in advanced high school science and math courses (Oakes 1990; Ingersoll 2002).
In 2010, an assessment of AI/AN education at Washington State colleges and universities was completed (Akweks et al. 2009). As a tribal citizen, student mentor, research advisor, and graduate committee member for AI/AN meteorology and engineering students primarily in Oklahoma, four points hit home. The first is that there is not a “typical” AI/AN university student. Successful AI/AN programs are multifaceted with curriculums designed for incoming freshmen, returning students, and adults seeking professional credentials. Curriculums at successful institutions have multiple entry points and instructors understand and connect with their clientele. The second point is the systemic change Washington made in the curriculum for freshman to enable high levels of interaction between students and instructors to create a sense of community. Freshman classes are integrated and coherent across disciplines with an emphasis on active learning. “Gatekeeper classes” (i.e., calculus and physics) have also been restructured to intervene before freshman make the choice to dropout. The third point is clear institutional leadership with long-term vision. This approach addresses a common pitfall of AI/AN academic outreach activities that end when a short-term grant runs out or a faculty member leaves. The fourth point is the importance AI/AN place on giving back to their respective communities and serving as role models for their families. The study cites many authors who have documented this sentiment as a critical element in AI/AN persistence and ultimately success at college.
POTENTIAL FRAMEWORK FOR AI/AN S&E ENGAGEMENT.
As an AI/AN engineering and meteorology student, I have personally seen the critical role that mentoring, internships, and scholarships play in successfully completing S&E degree programs and competing for federal S&E positions. In talking with other AI/AN scientists and non-AI/AN scientific leaders, there are many opportunities to forge collaborations that expose AI/AN students to weather and water science positions and to embody the four elements noted in the Washington State study. However, as the federal S&E workforce numbers show, there are very few federal AI/AN scientists to champion the cause and serve as mentors. There is also the issue of tribal sovereignty with each tribe an independent self-governing body (Echo-Hawk 2010) that operates differently (Hammer 2002). This fact is very important. A successful engagement strategy for one tribe may not work with another tribe. Despite these two factors, the environment for collaborative research driven by tribal needs continues to expand. As tribes reach out to work collaboratively with scientific consortiums, the opportunities to engage AI/AN students of all ages emerge.
One path forward is to look at weather and water resource research specifically observing networks. There are large gaps of high-quality in situ data at critical locations for assimilation into numerical weather and hydrologic models (Knopfmeier and Stensrud 2013; Tyndall and Horel 2013; National Research Council 2009). Where observations are collected, the length of the observational records are relatively short and do not capture conditions prior to colonization. Partnerships between tribes, federal agencies charged with weather and water monitoring and prediction, and academic institutions hosting state climate offices and federal climate centers offer opportunities to address shortcomings in our nation's observing capabilities and increase AI/AN S&E participation (Apple et al. 2011; Riggs et al. 2007; Riggs 2005; Riggs and Semken 2001). Working with AI/AN and non-AI/AN scientists operating within existing federal and academic observing networks, high-quality rural mesonets can be created and sustained using tribal citizens as project scientists, quality assurance professionals, and network technicians. These networks provide a means to gather high-value observations for numerical weather prediction data assimilation. With active AI/AN involvement, the length of weather and water datasets can likely be extended using the cultural resources of tribes similar to the hurricane reanalysis project (Landsea et al. 2004). Most importantly, these collaborations build scientific capacity in areas and populations underserved by our nation's scientific enterprise.
For a majority of AI/AN, the opportunities to actively participate in community-based science programs with access to academic mentors who are physical science or engineering graduates are few. Observing networks are tremendous teaching tools to engage students and their communities (Horel et al. 2013; White et al. 2013; Smith et al. 2000; McPherson and Crawford 1996). Active engagement of AI/AN, a majority of whom live near and in remote communities, through weather and water monitoring networks addresses three critical needs: 1) increased awareness of EA&OS and engineering disciplines by AI/AN fourth- through sixth-grade middle school students, 2) access to a cadre of community and academic S&E mentors and counselors, and 3) development of culturally sensitive science and engineering curriculum materials to increase AI/AN enrollment in college preparatory courses.
Two targets of opportunity appear. The first is looking at states with the highest population of AI/AN, significant tribal resources, and academic institutions hosting climate centers operating observing networks locally, regionally, and globally. Census statistics for 2007–11 show California, Arizona, Oklahoma, and New Mexico have the largest AI/AN populations (Table 12; Macartney et al. 2013). Their public academic institutions host climate centers; support accredited undergraduate and advanced degree programs in engineering, hydrology, and meteorology; and have active student chapters of the American Indian Science and Engineering Society (AISES) and Society for the Advancement of Hispanics/Chicanos and Native Americans in Science (SACNAS). Their students actively participate in workforce experiences funded by their tribes or federally supported internships serving under-represented minorities (Pandya et al. 2007; Pyrtle and Williamson Whitney 2008; Windham et al. 2004). Tribal governments in these states are also reaching out to federal, state, and academic research consortiums to fund weather and water monitoring programs, research activities, and student scholarships. With active mesonets, these four states, among others, offer very attractive environments to expand on existing AI/AN partnerships to engage students and provide the support services to help them succeed in college and S&E careers.
The second target of opportunity is the Bureau of Indian Education (BIE) which funds 184 elementary and secondary schools on 64 reservations in 23 states (BIE 2014). The organization also funds 25 tribal colleges and operates Haskell Indian Nations University and Southwestern Indian Polytechnic Institute. From their national directory, in 2009 BIE had approximately 4,200 teachers, administrators, and support staff in BIE schools and another 6,000 in the tribally operated schools serving approximately 48,700 students in kindergarten through grade 12. and another 400,000 through other Native American educational programs (i.e., Johnson O'Malley). It is difficult to obtain data from these tribally based, but federally funded, educational institutions (Faircloth and Tippeconnic 2010) yet as part of the national collective, degree attainment statistics show the current educational strategy is not producing AI/AN EA&OS and engineering graduates. Federally funded, tribally focused schools can and should be leaders in developing successful college preparatory AI/AN S&E educational programs. They have a unique environment to create S&E curriculums that respect AI/AN traditional knowledge (Pierotti and Wildcat 2000; Cajete 2000) with the sensitivity that AI/AN students must be able to adapt and learn in academic environments currently dominated by western science pedagogy and value systems.
Many of the BIE schools are located in states with active weather and water observing programs that could benefit from installation of observing platforms at school campuses located in remote areas and on reservations. These mutually beneficial collaborations need to be initiated to provide active learning environments for AI/AN students while strengthening relationships with academic institutions. Connecting AI/AN university students, especially those at BIE universities, with BIE elementary and secondary schools can facilitate a cohort relationship among AI/AN students. Supplement this program with visiting teacher and faculty programs and you have the foundation for a sustainable cadre of mentors to help students as they make decisions on college and then continue to support them once they arrive on campus and embark on their EA&OS and engineering degree programs.
Collaborative partnerships related to the nation's weather and water research enterprise offer a conduit for AI/AN perspectives to be heard and respected (Galloway McLean 2010). They offer opportunities for tribal citizens to be fairly represented within the federal EA&OS and engineering workforce in terms of numbers and salary levels. For AI/AN, education deficits and poverty have existed too long. Actively engaging AI/AN students of all ages in weather and water research programs can be one element in a comprehensive national portfolio of science and workforce initiatives that address long-term S&E workforce disparities for AI/AN and create economic stability for AI/AN citizens and communities.
There are a number of tribes that have broken the cycle of poverty by building successful economies to support their citizens. These “successful” tribes offer valuable lessons that we as a nation must look to in the context of the trust relationships between individual tribes and the United States government (Cornell and Kalt 1998). Former Senator Byron Dorgan, chair of the Senate Indian Affairs committee from 2007 to 2011, wrote recently about traveling to South Dakota's Pine Ridge Indian Reservation—the second largest Indian reservation in the U.S. (Dorgan 2013). Over 48% of AI/AN in South Dakota live in poverty (Table 13). Pine Ridge fully encompasses Shannon County—the second-poorest county in the United States. In his New York Times op-ed, the former Indian Affairs chairman urged Congress to “hold a series of investigative hearings on our unfulfilled treaties with American Indians. Add up the broken promises, make an accounting of the underfunding of all of it, and then work with tribes to develop a plan to make it right” (Dorgan 2013).
It is to this call that I hope the data presented and the ideas put forth will serve as a catalyst for the hydrometeorological community, tribal governments and councils, federal and state leaders, professional organizations, and education experts to come together to address AI/AN educational and S&E workforce disparity. We must develop and commit to a strategic long-term plan to create pathways for AI/AN students and citizens to become active stakeholders in weather and water programs within their communities. It is time to create the capacity to enable, empower, and energize indigenous people to be leaders in our nation's S&E enterprise. Our nation's environmental and resource management issues are too great to continue to ignore this segment of resource owners and their unique and valuable perspectives on sustainable resource management.
Thank you to Bernard Meisner of NWS Southern Region Headquarters and the three anonymous reviewers who provided valuable comments and suggestions to greatly improve this manuscript. I especially appreciate the encouragement from the anonymous reviewers to include personal anecdotes. Amy Wampler of the Chickasaw Nation Division of Health provided valuable information related to AI/AN health recruitment initiatives and Native American student activities of the Chickasaw Nation's Chokka' Kilimpi' Family Resource Center. Final acknowledgement goes to the University of Oklahoma faculty affiliated with the Department of Interior's South Central Climate Center and the NOAA partners of the National Weather Center for their support and commitment to Native American inclusion in weather and water education and research programs.
1Native American and American Indian, used interchangeably, and Alaska Natives refer to members or citizens of federally recognized and unrecognized tribes and communities comprising descendants of indigenous inhabitants of the lower 48 states and Alaska.